{"question_id": 1, "text": "1. We first import the necessary Python packages, including the SentenceTransformer from sentence_transformers and the numpy and scipy packages for feature extraction.\\n2. Then, we create a list of article IDs that the user has already liked, ready to be compared to the new article IDs.\\n3. We load the pre-trained SentenceTransformer model 'nikcheerla/nooks-amd-detection-v2-full' for feature extraction.\\n4. We use the model to encode the user's previously liked articles and the new article being considered for recommendation.\\n5. We compute the cosine similarity between the user's previously liked articles and the new article, then select the top-k most similar articles as recommended articles.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom scipy.spatial.distance import cosine\\nimport numpy as np\\nimport pandas as pd\\nmodel = SentenceTransformer('nikcheerla/nooks-amd-detection-v2-full')\\nold_article_ids = [1, 2, 3, 4, 5]\\nnew_article_ids = [6, 7, 8, 9, 10]\\n# Load the user's previously liked articles\\nuser_preferences = pd.DataFrame({'article_id': old_article_ids})\\n# Load the new article for recommendation\\nnew_article = {'article_text': 'Sample article text'}\\n# Compute the similarity between the user's previously liked articles and the new article\\nsimilarity_matrix = cosine(user_preferences.T @ new_article, user_preferences.T @ model.encode(new_article_ids))\\n# Find the top-k most similar articles to the new article\\ntop_k_similar_article_ids = np.argsort(similarity_matrix, axis=1)[:, -k:\\nrecommended_article_ids = [new_article_ids[i] for i in top_k_similar_article_ids[0]]\\nprint(recommended_article_ids)\"", "answer_id": "LKFhLozixyBhScJxk2qwQK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 2, "text": "<<>>: Natural Language Processing Feature Extraction\\n<<>>: AutoModel.from_pretrained('dmis-lab/biobert-v1.1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, which are 'AutoTokenizer' and 'AutoModel' from the transformers library by Hugging Face.\\n2. Load the pre-trained 'dmis-lab/biobert-v1.1' model for feature extraction. BioBERT is a powerful language representation model specifically designed and pre-trained for the biomedical domain.\\n3. Tokenize the input text using the BioBERT tokenizer.\\n4. Pass the tokenized input through the BioBERT model to extract features.\\n5. The extracted features can be used to find relationships between medical terms, such as synonymy, antonymy, or related terms.\\n### Example code:\\n```>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/hubert-base-superb-sat')\\n<<>>: PyTorch Transformers\\n<<>>: 1. Import the necessary libraries, such as 'pipeline' from transformers and 'librosa' for audio processing.\\n2. Load the pre-trained Hubert-Base model ('superb/hubert-base-superb-sat') designed for audio classification tasks with sentiment analysis.\\n3. Use the pipeline function to create an audio classifier that uses the pre-trained model.\\n4. Load the audio file and pass it through the classifier to obtain sentiment analysis results.\\n<<>>: from transformers import pipeline\\nimport librosa\\nclassifier = pipeline('audio-classification', model='superb/hubert-base-superb-sat')\\naudio_file = 'path/to/your/audio_file.wav' # Replace with the path to your audio file\\nresult = classifier(audio_file, top_k=5)\\n\"", "answer_id": "6pSbwn3QMpk7R5uckQCHmQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 4, "text": "1. Import the necessary libraries, which are 'AutoModel', 'AutoTokenizer', and 'FeatureExtractionPipeline' from transformers.\\n2. Load the model 'MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary' using the 'AutoModel' class.\\n3. Load the tokenizer using 'AutoTokenizer' and the same model name.\\n4. Create the feature extraction pipeline using 'FeatureExtractionPipeline' and the previously loaded model and tokenizer.\\n5. Extract sentence embeddings from user inputs using the created feature extraction pipeline.\\n6. Compare and cluster these embeddings to determine sentence similarity across different languages.\\n<<>>: from transformers import AutoModel, AutoTokenizer, FeatureExtractionPipeline\\nmodel = AutoModel.from_pretrained('MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary')\\ntokenizer = AutoTokenizer.from_pretrained('MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary')\\nfeature_extractor = FeatureExtractionPipeline(model=model, tokenizer=tokenizer)\\nsentences = ['This is an example sentence', 'Ceci est une sentence example']\\nembeddings = feature_extractor(sentences, return_tensors='pt')\\n\"", "answer_id": "BhP8PYAzUUUjK2LwaTXoV2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 5, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', vae=AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-ema'))\\n<<>>: Hugging Face\\n<<>>: 1. We first import necessary libraries, including AutoencoderKL from the diffusers.models module, StableDiffusionPipeline from the diffusers module, and torch.\\n2. We then load the pre-trained model 'CompVis/stable-diffusion-v1-4', which is a text-to-image generation model, and initialize it with a fine-tuned VAE decoder (\\\"stabilityai/sd-vae-ft-ema\\\").\\n3. We provide a text prompt that describes the desired image, which is: \\\"modern living room with a fireplace and a large window overlooking a forest\\\".\\n4. The model generates an image based on the given prompt, which is then saved to a file called 'living_room.png'.\\n<<>>: from diffusers.models import AutoencoderKL\\nfrom diffusers import StableDiffusionPipeline\\nimport torch\\nmodel = 'CompVis/stable-diffusion-v1-4'\\nvae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-ema')\\npipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)\\nprompt = \\\"modern living room with a fireplace and a large window overlooking a forest\\\"\\nimage = pipe(prompt).images[0]\\nimage.save('living_room.png')\\n\"", "answer_id": "jFeABMVbTN8hDHeppJ3ku3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 6, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text2text-generation model.\\n3. Specify the model 'salesforce/blip2-opt-6.7b' to be loaded. This model is trained on a large dataset of images and text and can generate relevant product descriptions given an image as input.\\n4. The created model can be used to generate a description for the image-based online store platform's products by providing the image as input.\\n5. The generated text will describe the specifics of the product and help customers understand it better.\\n<<>>: from transformers import pipeline\\nproduct_description_generator = pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\nimage_path = 'path/to/product_image.jpg'\\n# replace 'path/to/product_image.jpg' with the path to your image file\\ndescription = product_description_generator(image_path)\\n\"", "answer_id": "dDQFvRcsoH5WbpY4ueCow8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 7, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the necessary Pix2StructForConditionalGeneration class from the transformers library.\\n2. Instantiate the model by loading the pretrained 'google/pix2struct-base' model.\\n3. The loaded model is designed for image-to-text tasks, such as image captioning, which allows it to generate a textual description of an input image.\\n4. Provide the image as input to the model and let it generate a textual description of the image.\\n<<>>: from transformers import Pix2StructForConditionalGeneration\\nmodel = Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-base')\\n# Load your image and preprocess it as required by the model\\nimage_description = model.generate_image_description(image)\\n\"", "answer_id": "nfwZYREyWmN26RZNsBA3mn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 8, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-linete')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and classes, including the ControlNetModel, StableDiffusionControlNetPipeline, and UniPCMultistepScheduler, from the diffusers package.\\n2. Load the pre-trained ControlNet model 'lllyasviel/sd-controlnet-linete' using the from_pretrained method.\\n3. Create a pipeline using the StableDiffusionControlNetPipeline and configure it with the pre-trained ControlNet model.\\n4. Set the scheduler for the pipeline using the UniPCMultistepScheduler.\\n5. With these in place, you can now input any image, and the pipeline will generate a linearized table as the output.\\n6. Save the generated table as an image file.\\n<<>>: from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nfrom diffusers.utils import load_image\\nfrom PIL import Image\\nimage = load_image('your_image_url_here') # replace with the URL of your chart\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-linete')\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\nimage = pipe('table title', image, num_rows=5).images[0]\\nimage.save('output_table.png')\\n\"", "answer_id": "XBVvhhKB92Xe8R6tTU2RkZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 9, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: BaseModel.from_pretrained('duncan93/video')\\n<<>>: Hugging Face\\n<<>>:1. We first import the necessary class from the asteroid package. This includes BaseModel for the text-to-video generation model.\\n2. We then use the from_pretrained method of the BaseModel class to load the pre-trained model 'duncan93/video'. This model has been trained for text-to-video generation tasks, which is exactly what we need for generating videos based on user-provided text instructions.\\n3. We can then use this model to generate videos based on input text provided by the users.\\n<<>>: from asteroid import BaseModel\\nmodel = BaseModel.from_pretrained('duncan93/video')\\n# Generate video from text instructions\\nvideo = model.generate_video(input_text)\"", "answer_id": "6qccsKKZGALbj9ZeVWUJ7P", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 10, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: BaseModel.from_pretrained('duncan93/video')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries, which include BaseModel from the huggingface_hub package.\\n2. Load the 'duncan93/video' model using the BaseModel.from_pretrained() function. This model is trained to generate videos based on text prompts.\\n3. Pass the text input to the model, which will generate a video based on the input content.\\n4. Note that the output video will be a synthesized and not a real video.\\n5. Video generation code can be added based on the provided API call.\\n<<>>: from huggingface_hub import BaseModel\\nmodel = BaseModel.from_pretrained('duncan93/video')\\n# Code to generate video from text, using the loaded model, should be added here.\\n\"", "answer_id": "mXdVZdgBksF9hvA2ntPt9g", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 11, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: pipeline('text-to-video-synthesis', model_dir.as_posix())\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and functions, including pipeline from the transformers package and the make_video function.\\n2. Load the pre-trained model 'damo-vilab/modelscope-damo-text-to-video-synthesis' using the pipeline function. This model has been trained for text-to-video synthesis tasks.\\n3. Pass a text description to the model using the make_video function. The description can be about any scenario or story.\\n4. The model will generate a video file based on the given text description, which can be used as a demonstration of the AI's capabilities.\\n<<>>: from transformers import pipeline\\nfrom modelscope_damo import make_video\\ntext_description = \\\"A robot is working in a factory.\\\"\\nmodel = pipeline('text-to-video-synthesis', model_dir.as_posix())\\ngenerated_video = make_video(text_description, model)\\ngenerated_video.save('generated_video.mp4')\"", "answer_id": "bjpeKmwhYxSPYtPNukrpTz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 12, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then use the pipeline function to create a visual question answering model with the 'microsoft/git-base-vqav2' model. This model is designed to analyze images and answer questions about their contents.\\n3. To analyze an image, we need to provide the image file path and the question we want to ask the model about the image content.\\n4. The created model can then be used to answer questions about images in your phone gallery.\\n<<>>: from transformers import pipeline\\nvqa_model = pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\nimage_path = 'path/to/image.jpg'\\nquestion = 'What is in the image?'\\nanswer = vqa_model(image=image_path, question=question)\\n\"", "answer_id": "GRVrRsnmFCnAZtRXqNhHhs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 13, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='JosephusCheung/GuanacoVQA')\\n<<>>: Hugging Face\\n<<>>: 1. We first import the necessary library, which is the 'pipeline' function from the transformers package.\\n2. We then use the 'pipeline' function to load the 'JosephusCheung/GuanacoVQA' model, which was trained on the GuanacoVQADataset and is designed for visual question answering tasks.\\n3. This model can analyze an image and answer questions based on its content.\\n4. To use the model, provide the image file and the question as input to the model.\\n5. The model will then generate an appropriate answer based on the image and the question provided.\\n<<>>: from transformers import pipeline\\nimport urllib.request\\nfrom PIL import Image\\n# Load the model\\nvqa_model = pipeline('visual-question-answering', model='JosephusCheung/GuanacoVQA')\\n# Load the image file\\nimage_url = \\\"path/to/image.jpg\\\"\\nimage = Image.open(urlopen(image_url))\\n# Provide the question\\nquestion = \\\"What is in the dish?\\\"\\n# Get the answer\\nanswer = vqa_model(image, question)\\n\"", "answer_id": "4RV9ZNBxbHbP8WdfNLFZS8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 14, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='impira/layoutlm-invoices')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'impira/layoutlm-invoices'.\\n3. The loaded model specializes in extracting information from invoices and other documents.\\n4. Pass the question and context to the model to get the answer.\\n5. The model will parse the invoice document and return the total amount.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='impira/layoutlm-invoices')\\nquestion = 'What is the total amount?'\\ncontext = 'Invoice information for order ABC_123\\\\nProduct: Widget A, Quantity: 10, Price: $5 each\\\\nProduct: Widget B, Quantity: 5, Price: $3 each\\\\nProduct: Widget C, Quantity: 15, Price: $2 each\\\\nSubtotal: $75, Tax: $6.38, Total Amount Due: $81.38'\\nanswer = qa_pipeline({\\\"question\\\": question, \\\"context\\\": context})\\n\"", "answer_id": "BueHgAQdCGuNyVRDcgdjFM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 15, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the 'pipeline' function from the transformers library.\\n2. Initialize the question-answering pipeline using the 'tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa' model, which is specifically designed for extracting information from documents.\\n3. Use the pipeline to extract information from the student enrollment forms by providing the question and the form data as input. The model will return the extracted details such as name, age, and address.\\n4. The pipeline can handle both image-based and text-based documents, making it suitable for various use cases.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa')\\nquestion = 'What is the student's name?'\\nform_data = {'student enrollment form content here'}\\nresult = qa_pipeline(question=question, context=form_data)\\n\"", "answer_id": "jRqWXBamF4EC25mBdgZZty", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 16, "text": "<<>>: Multimodal Graph Machine Learning\\n<<>>: AutoModel.from_pretrained('clefourrier/graphormer-base-pcqm4mv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the AutoModel class from the transformers library.\\n2. Use the from_pretrained method of the AutoModel class to load the pre-trained Graphormer model, 'clefourrier/graphormer-base-pcqm4mv2'.\\n3. This model has been pre-trained on the PCQM4M-LSCv2 dataset and is designed for graph classification tasks and graph representation tasks.\\n4. You can use this model to predict the properties of molecules based on their graph representations by providing the graph data as input.\\n5. Make sure the input graph data is preprocessed as required by the Graphormer model.\\n<<>>: from transformers import AutoModel\\ngraphormer = AutoModel.from_pretrained('clefourrier/graphormer-base-pcqm4mv2')\\n# Preprocess the input graph data and use the graphormer model for prediction.\"", "answer_id": "WvXpVxTyrgie59UPQwSXsc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 17, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: pipeline('depth-estimation', model='sayakpaul/glpn-nyu-finetuned-diode-221221-102136')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model, which in this case is 'sayakpaul/glpn-nyu-finetuned-diode-221221-102136'.\\n3. The loaded model will be used for depth estimation, which is the task of estimating the depth of an image.\\n4. Provide the underwater photo as input to the model and obtain a depth estimation.\\n<<>>: from transformers import pipeline\\nimport cv2\\nfrom PIL import Image\\ndepth_estimator = pipeline('depth-estimation', model='sayakpaul/glpn-nyu-finetuned-diode-221221-102136')\\n# Load the image\\nimg = Image.open('underwater_photo.jpg')\\n# Replace 'underwater_photo.jpg' with the path to your image\\n# Get the depth estimation\\nresult = depth_estimator(img)\\n# The output is the depth estimation in meters\"", "answer_id": "3izGyjFziYpgHnE9pKpHi5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 18, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-230103-091356')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We need to import the necessary libraries for this task, including 'AutoModel' from transformers and 'PyTorch'.\\n2. We then use the 'from_pretrained' function of the 'AutoModel' class to load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-230103-091356'. This model is specifically designed for depth estimation tasks using a single camera.\\n3. We load the image data from a file or acquire it in real-time from the camera.\\n4. This model can then be used to analyze an image and estimate the depth information in a single camera.\\n<<>>: import torch\\nfrom transformers import AutoModel\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-230103-091356')\\nimage = load_image('path/to/image')\\n# replace 'path/to/image' with the path to your image\\nwith torch.no_grad():\\n depth_map = model(image)\\n\"", "answer_id": "Bt2jRcqqydD6TaRuhxbLLb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 19, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, including DPTForDepthEstimation from the transformers package, Image from the PIL package, and torch for tensor operations.\\n2. Load the pre-trained depth estimation model 'Intel/dpt-hybrid-midas' using the from_pretrained method of the DPTForDepthEstimation class with the 'low_cpu_mem_usage' argument set to True.\\n3. Load the image of the house to be processed for depth estimation.\\n4. Create a tensor from the image and pass it through the model to get the depth estimation.\\n5. The depth estimation can be used for creating a virtual tour of the house and providing a better understanding of the property to potential buyers.\\n<<>>: from PIL import Image\\nimport torch\\nfrom transformers import DPTForDepthEstimation\\nmodel = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\nimage = Image.open('house_image.jpg') # Replace 'house_image.jpg' with the path to the house image\\ninput_tensor = torch.tensor(image).unsqueeze(0)\\ndepth_estimation = model(input_tensor)\\n\"", "answer_id": "mV96vhHc7NEv5XwiEL7WXa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 20, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vits8')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries: ViTFeatureExtractor and ViTModel from transformers, Image from PIL, and requests.\\n2. Load the image from a URL using the requests library and open it using the Image class from PIL.\\n3. Create an instance of the feature extractor using the ViTFeatureExtractor.from_pretrained() method with the 'facebook/dino-vits8' model.\\n4. Load the pre-trained ViT model using the ViTModel.from_pretrained() method with the 'facebook/dino-vits8' model.\\n5. Utilize the feature extractor to process the image and prepare the inputs for the model.\\n6. Pass the processed inputs to the model to obtain the hidden states, which can be used to perform image classification.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimport requests\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits8')\\nmodel = ViTModel.from_pretrained('facebook/dino-vits8')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\\n\"", "answer_id": "LbHjwVBVgVghMmvpQMJHqQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 21, "text": "<<>>: Computer Vision Object Detection\\n<<>>: OwlViTForObjectDetection.from_pretrained('google/owlvit-base-patch32')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries: Image from PIL, requests, and OwlViTProcessor and OwlViTForObjectDetection from transformers.\\n2. Load the image using the requests library and PIL's Image.open.\\n3. Create a processor using OwlViTProcessor.from_pretrained().\\n4. Load the pre-trained OwlViT model using OwlViTForObjectDetection.from_pretrained().\\n5. Define the text description of the object you want to detect (e.g., \\\"A photo of a dog\\\").\\n6. Preprocess the text and image using the processor, and pass the outputs to the model.\\n7. Apply the model's outputs to an object detection framework (e.g., DetrForObjectDetection.from_pretrained()) to get the final results.\\n8. Post-process the results to obtain the bounding box coordinates for the detected object.\\n<<>>: import requests\\nfrom PIL import Image\\nimport torch\\nfrom transformers import OwlViTProcessor, OwlViTForObjectDetection\\nprocessor = OwlViTProcessor.from_pretrained('google/owlvit-base-patch32')\\nmodel = OwlViTForObjectDetection.from_pretrained('google/owlvit-base-patch32')\\ntext = \\\"a photo of a dog\\\"\\nimage = Image.open(requests.get(IMAGE_URL, stream=True).raw)\\ninputs = processor(text=text, images=image, return_tensors='pt')\\noutputs = model(**inputs)\\ntarget_sizes = torch.Tensor([image.size[::-1]])\\nresults = processor.post_process(outputs=outputs, target_sizes=target_sizes)\\n\"", "answer_id": "PD3gkmeTNM865fAgZ6JfEu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 22, "text": "\\n<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-valorant-detection')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the required libraries ultralyticsplus and ultralytics.\\n2. Import YOLO and ultralyticsplus packages.\\n3. Instantiate the YOLO model with the provided model name 'keremberke/yolov8m-valorant-detection'.\\n4. Configure the model parameters like confidence threshold, IOU threshold, and maximum detections.\\n5. Use the model to predict objects in the game screenshot provided as an input.\\n6. Process the model's outputs to retrieve the detected objects like dropped spike, enemy, planted spike, and teammate.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-valorant-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path_to_game_screenshot.jpg'\\n# Replace 'path_to_game_screenshot.jpg' with the actual path to your game screenshot\\nresults = model.predict(image)\\nprint(results[0].boxes)\\n\"", "answer_id": "Er3HAVY9A6SqcWV2BvYVyb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 23, "text": "<<>>: Computer Vision Object Detection\\n<<>>: DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101-dc5')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import necessary libraries, which are 'DetrFeatureExtractor', 'DetrForObjectDetection', 'torch', 'Image' from PIL, and 'requests'.\\n2. Load the 'facebook/detr-resnet-101-dc5' model using the DetrForObjectDetection.from_pretrained() function.\\n3. Create an instance of the 'DetrFeatureExtractor' to process the input images.\\n4. Use the feature extractor to convert the input image into a format suitable for the model.\\n5. Pass the processed image to the model, which will return the detected objects and their bounding boxes.\\n6. Use the bounding boxes and objects' labels to generate a list of objects present in the image.\\n7. Repeat the process for all images to create a list of objects present in each property listing picture.\\n<<>>: from transformers import DetrFeatureExtractor, DetrForObjectDetection\\nimport torch\\nfrom PIL import Image\\nimport requests\\nurl = 'http://example.com/property_image_url.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-dc5')\\nmodel = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101-dc5')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\nbboxes = torch.argmax(logits, dim=1)\\n\"", "answer_id": "YFPp3ZTMdACypxBZRSBm6x", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 24, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes, including Mask2FormerForUniversalSegmentation and AutoImageProcessor from the transformers package, and Image from the PIL package.\\n2. Load the pre-trained model 'facebook/mask2former-swin-base-coco-panoptic' using the from_pretrained method of the Mask2FormerForUniversalSegmentation class. This model has been trained for panoptic segmentation tasks, which divide an image into visually distinct regions.\\n3. Read the input image using the Image class from the PIL package and preprocess it using the AutoImageProcessor class.\\n4. Pass the preprocessed image through the model to obtain the panoptic segmentation results.\\n5. Post-process the results to obtain the final separated elements in the image.\\n<<>>: from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation\\nfrom PIL import Image\\nimport requests\\nprocessor = AutoImageProcessor.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\nurl = 'https://path_to_your_image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = processor(images=image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\nresult = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_panoptic_map = result['segmentation']\"", "answer_id": "W4XLC6tgT4m6ZYdMihj8cQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 25, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: OneFormerForUniversalSegmentation.from_pretrained('shi-labs/oneformer_ade20k_swin_large')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'OneFormerProcessor' and 'OneFormerForUniversalSegmentation' from transformers, and 'Image' from PIL.\\n2. Load the pre-trained OneFormer model using the from_pretrained method.\\n3. Process the input image using the OneFormerProcessor.\\n4. The input image is then processed through the OneFormer model, which performs semantic segmentation and instance segmentation tasks.\\n5. The model returns class and mask logits that can be used to further analyze the image and separate objects and their masks.\\n<<>>: from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation\\nfrom PIL import Image\\nimage = Image.open('room_mixture.jpg')\\nprocessor = OneFormerProcessor.from_pretrained('shi-labs/oneformer_ade20k_swin_large')\\nmodel = OneFormerForUniversalSegmentation.from_pretrained('shi-labs/oneformer_ade20k_swin_large')\\nsemantic_inputs = processor(images=image, task_inputs=['semantic'], return_tensors='pt')\\nsemantic_outputs = model(**semantic_inputs)\\npredicted_semantic_map = processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]\"", "answer_id": "K78Z2e8xUWFDAGWjyzGMSv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 26, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DiffusionPipeline.from_pretrained('CompVis/ldm-celebahq-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary class DiffusionPipeline from the diffusers package.\\n2. Load the pre-trained model 'CompVis/ldm-celebahq-256' using the from_pretrained method of the DiffusionPipeline class. This model is trained for high-quality image synthesis of celebrity faces using the Latent Diffusion Models (LDM) technique.\\n3. Generate a new, random image of a celebrity face by calling the model. The generated image will be a realistic representation of a celebrity's face.\\n4. Save the generated image to a file, such as 'ldm_generated_image.png'.\\n<<>>: from diffusers import DiffusionPipeline\\npipeline = DiffusionPipeline.from_pretrained('CompVis/ldm-celebahq-256')\\nimage = pipeline().images[0]\\nimage.save('ldm_generated_image.png')\"", "answer_id": "ZRfEkab6nLEjKY7fkTEbLz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 27, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_bedroom')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary classes and functions from the diffusers, transformers, and controlnet_aux libraries.\\n2. Load the pre-trained ControlNet model using the from_pretrained method with the model checkpoint 'lllyasviel/control_v11p_sd15_bedroom'.\\n3. Create a StableDiffusionControlNetPipeline using the from_pretrained method, and specify the controlnet model and the scheduler.\\n4. Set the number of inference steps and seed for the random number generator.\\n5. Use the pipeline to generate a new image based on the art from the online database, and save the resulting image to 'image_out.png'.\\n<<>>: from diffusers import ControlNetModel\\nfrom controlnet_aux import StableDiffusionControlNetPipeline\\nfrom torch import controlnet_aux\\nfrom diffusers import UniPCMultistepScheduler\\nimport random\\nimport torch\\nnum_inference_steps = 30\\nseed = 33\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_bedroom', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\ngenerator = torch.manual_seed(seed)\\nimage = pipe(art_image, num_inference_steps=num_inference_steps, generator=generator).images[0]\\nimage.save('image_out.png')\\n\"", "answer_id": "7aaf96kToXdawJp8Bseuoa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 28, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('utyug1/sd-class-butterflies-32')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the DDPMPipeline class from the diffusers library.\\n2. Load the pretrained model 'utyug1/sd-class-butterflies-32' using the from_pretrained method of DDPMPipeline. This model generates images of cute butterflies.\\n3. Generate an image of a cute butterfly using the loaded model. You can access the generated image using the '.images' attribute and display it or use it for your social media needs.\\n<<>>: from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained('utyug1/sd-class-butterflies-32')\\nimage = pipeline().images[0]\\nimage.show()\"", "answer_id": "CCnmTSycqpPvgGudNW89UW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 29, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required VideoMAEImageProcessor and VideoMAEForPreTraining classes from the transformers library.\\n2. Load the pretrained VideoMAE model 'MCG-NJU/videomae-base-short-ssv2', which is designed for video classification tasks.\\n3. Create a VideoMAEImageProcessor instance to preprocess the video frames before inputting them into the model.\\n4. Use the processor to process the video frames and generate the necessary input tensors.\\n5. Feed these tensors into the VideoMAE model, which will then analyze the video and output classification results based on the frames.\\n6. Check the classification results against existing security guidelines to categorize the footage.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining\\nimport numpy as np\\nimport torch\\n# Load the video frames\\nvideo = list(np.random.randn(16, 3, 224, 224)) # Replace with actual video frames\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\nmodel = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\\n\"", "answer_id": "2vDR4Bj2zHrbyHf4xGA64X", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 30, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, including the VideoMAEImageProcessor for pre-processing the video data and VideoMAEForPreTraining for the video classification model.\\n2. Load the pre-trained model 'MCG-NJU/videomae-base-short-ssv2' using the from_pretrained method of the VideoMAEForPreTraining class.\\n3. Create a VideoMAEImageProcessor instance using the from_pretrained method to pre-process the video data.\\n4. Process the video and convert it into a suitable format for input to the model.\\n5. Pass the processed video data to the model and obtain classification results.\\n6. Use the obtained results to classify the videos as needed for the new project.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining\\nimport numpy as np\\nimport torch\\nnum_frames = 16\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\nmodel = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\npixel_values = processor(video, return_tensors='pt').pixel_values\\nnum_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2\\nseq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame\\nbool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()\\noutputs = model(pixel_values, bool_masked_pos=bool_masked_pos)\\nloss = outputs.loss\"", "answer_id": "PCz2UBryeM698gVrzLJoXV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 31, "text": "<<>>: Computer Vision Image Classification\\n<<>>: CLIPModel.from_pretrained('openai/clip-vit-large-patch14')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, such as PIL, requests, and transformers. We will use the CLIPModel and CLIPProcessor classes from the transformers library.\\n2. Load the pre-trained model 'openai/clip-vit-large-patch14' using the CLIPModel.from_pretrained() method. This model has been trained for zero-shot and few-shot image classification tasks.\\n3. Load the processor using the CLIPProcessor.from_pretrained() method.\\n4. Retrieve the image of the car, either from a URL or a local file. The image can be in any format supported by the Image class from the PIL library.\\n5. Define a list of possible car damage labels, such as 'minor damages' and 'major accident'.\\n6. Process the image and text labels using the processor and create the input tensors.\\n7. Pass the input tensors into the model to obtain the logits_per_image tensor.\\n8. Apply softmax on the logits_per_image tensor to get the probabilities for each label.\\n9. The highest probability label will indicate if the car has suffered minor damages or major accidents.\\n<<>>: from PIL import Image\\nimport requests\\nfrom transformers import CLIPProcessor, CLIPModel\\nmodel = CLIPModel.from_pretrained('openai/clip-vit-large-patch14')\\nprocessor = CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14')\\nurl = 'https://example.com/car_image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nlabels = ['minor damages', 'major accident']\\ninputs = processor(text=labels, images=image, return_tensors='pt', padding=True)\\nlogits_per_image = model(**inputs).logits_per_image\\nprobs = logits_per_image.softmax(dim=-1).tolist()[0]\\nresult = dict(zip(labels, probs))\\n\"", "answer_id": "UxXSFxHjcFKaUaBz5X6RUS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 32, "text": "<<>>: Multimodal Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a zero-shot image classification model.\\n3. Specify the model 'microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224' to be loaded. This model is trained on a biomedical dataset and can be used for analyzing medical images.\\n4. Use the created classifier to classify the input medical image into one of the three categories: X-ray, MRI scan, or CT scan.\\n5. Provide the path to the medical image file and a list of possible class names as arguments.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('zero-shot-image-classification', model='microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224')\\nresult = image_classifier('path/to/medical_image.png', ['X-ray', 'MRI scan', 'CT scan'])\\n\"", "answer_id": "4XeCjifWKJm2uXCihESBcs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 33, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-ViT-B-32-laion2B-s34B-b79K')\\n<<>>: Hugging Face\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Instantiate the image classification pipeline using the 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model, which is trained for zero-shot image classification tasks.\\n3. Provide the image file path and a list of possible dressing labels as input to the classification pipeline.\\n4. The model will then predict which dressing is most likely to match the given image.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-ViT-B-32-laion2B-s34B-b79K')\\nimage_path = 'path/to/image.jpg'\\ndressing_labels = ['dress1', 'dress2', 'dress3', 'dress4']\\nresult = image_classifier(image_path, dressing_labels)\\n\"", "answer_id": "Nmj8ptWP35Rdu4TJ5qUA3d", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 34, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('image-to-text', model='facebook/mmt-en-de')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to create an image-to-text model with the pre-trained model 'facebook/mmt-en-de'. This model has been fine-tuned on a large dataset of images and can be used for quickly identifying and describing images in multiple languages, including Chinese.\\n3. Pass the image path to the model, and it will return the image's description in Chinese.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-to-text', model='facebook/mmt-en-de')\\nimage_description = image_classifier(image_path_in_chinese)\\n\"", "answer_id": "jQgN9iLifZZ5dkLdybwCwM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 35, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='siebert/sentiment-roberta-large-english')\\n<<>>: Transformers\\n<<>>:\\n1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a sentiment analysis model by specifying the model 'siebert/sentiment-roberta-large-english'. This model is a fine-tuned version of the standard RoBERTa-large model and has been trained on diverse text sources, making it suitable for analyzing user messages in a customer support setting.\\n3. The created sentiment analysis classifier can then be used to analyze user messages and determine their sentiment, which can be positive, negative, or neutral.\\n4. This information can be helpful for analyzing trends in customer feedback and improving the overall customer support experience.\\n<<>>: from transformers import pipeline\\nsentiment_analysis = pipeline('sentiment-analysis', model='siebert/sentiment-roberta-large-english')\\nuser_message = \\\"I'm very disappointed with the quality of this product. It doesn't live up to the hype.\\\"\\nresult = sentiment_analysis(user_message)\\n\"", "answer_id": "Bo9ULCL8p6aG3r4DsVLGBa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 36, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Use the pipeline function to create a text classification model with the pre-trained model 'bhadresh-savani/distilbert-base-uncased-emotion'. This model is designed for emotion classification and can be used for sentiment analysis.\\n3. Process the customer review text and pass it through the pipeline to obtain the sentiment result (positive or negative).\\n4. Based on the sentiment result, categorize the customer review.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('text-classification', model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)\\nreview_text = \\\"The book was amazing, I loved every moment of it.\\\"\\nsentiment = classifier(review_text)\\nsentiment_label = sentiment[0]['label']\\nsentiment_score = sentiment[0]['score']\\n\"", "answer_id": "EFtkZt9e333oGfGAQWtfrP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 37, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text classification model, specifying the model 'bhadresh-savani/distilbert-base-uncased-emotion'. This model is trained to classify emotions in text, which we will utilize to determine the sentiment of the comments.\\n3. The created classifier can be used to analyze the sentiments of the consumers' comments.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('text-classification', model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)\\ncomment = \\\"I love the quality of your news website. The content is very informative.\\\"\\nresult = classifier(comment)\"", "answer_id": "BEguP7mXmgfJVaDmbPifxE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 38, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='facebook/opt-350m')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a text generation model.\\n3. We specify the model 'facebook/opt-350m' to be loaded. This is a model that is trained on a large corpus of text and can generate text based on the input provided.\\n4. The model is then used to analyze the sentiment of the customer review provided. The model generates a text describing the sentiment of the review.\\n<<>>: from transformers import pipeline\\ntext_gen = pipeline('text-generation', model='facebook/opt-350m')\\nreview_text = \\\"\\u00a1Esto es maravilloso! Me encanta.\\\"\\nsentiment_analysis = text_gen(review_text)[0]['generated_text']\\nsentiment_analysis\"", "answer_id": "jiLfaxVAJTDvheZWneSrWo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 39, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='kredor/albert-base-v2-xnli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary functions like pipeline from the transformers package.\\n2. We then use the pipeline function to create a zero-shot classification model, which can classify text into predefined categories even if it has not been explicitly trained on those categories.\\n3. We specify the model 'kredor/albert-base-v2-xnli' to be loaded. This is a model that has been trained and fine-tuned on various sources of text in multiple languages for different tasks, including classification.\\n4. The created classifier can then be used to classify comments into toxic or non-toxic categories based on the text.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='kredor/albert-base-v2-xnli')\\nresult = classifier(comment_text, candidate_labels=['toxic', 'non-toxic'])\\n\"", "answer_id": "fxJFVEQboBtF2LfdonW8An", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 40, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment-latest', tokenizer='cardiffnlp/twitter-roberta-base-sentiment-latest')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model 'cardiffnlp/twitter-roberta-base-sentiment-latest'. This model is specifically designed for sentiment analysis on Twitter texts.\\n3. The loaded model will be used for classifying the sentiment of any user-generated reviews or tweets concerning your product.\\n4. The model will analyze the text and return a sentiment label (positive, negative, or neutral) for the input data.\\n5. This will help your company to understand the general sentiment of user feedback and make appropriate decisions accordingly.\\n<<>>: from transformers import pipeline\\nsentiment_task = pipeline('sentiment-analysis', model='cardiffnlp/twitter-roberta-base-sentiment-latest', tokenizer='cardiffnlp/twitter-roberta-base-sentiment-latest')\\nsentiment_result = sentiment_task(user_review)\\n\"", "answer_id": "hYKKQd3qv5ZLArKR6bZKR3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 41, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: pipeline('ner', model='dslim/bert-base-NER-uncased')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'dslim/bert-base-NER-uncased'.\\n3. The loaded model will be used for Named Entity Recognition (NER), which is the task of identifying and classifying entities in text.\\n4. The model will detect and highlight the names of organizations or cities within the input French text.\\n<<>>: from transformers import pipeline\\nnlp = pipeline('ner', model='dslim/bert-base-NER-uncased')\\ntext = \\\"La soci\\u00e9t\\u00e9 de Paris est sp\\u00e9cialis\\u00e9e dans la vente de v\\u00e9hicules \\u00e9lectriques. Responsable des ventes, vous travaillerez au sein d'une \\u00e9quipe dynamique dans l'agence de Lyon. Vous \\u00eates charg\\u00e9(e) de d\\u00e9velopper le portefeuille client et d'assurer la satisfaction des clients existants. Dans ce contexte, vous devriez travailler en lien \\u00e9troit avec le directeur commercial et les autres \\u00e9quipes de l'entreprise. Une exp\\u00e9rience pr\\u00e9alable chez Renault est un atout.\\\"\\nentities = nlp(text)\\nprint(entities[0]['entity'])\\n\"", "answer_id": "n4euhoFJ3EKj9wkmtZq4Pb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 42, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: pyannote.audio\\n<<>>: 1. Import the 'Pipeline' class from the pyannote.audio library.\\n2. Load the pre-trained speaker diarization model using the 'from_pretrained' method and the provided model name and API access token.\\n3. Apply the diarization model to the given customer review audio file, which can be obtained from the customer support interaction.\\n4. The resulting speaker diarization object can be saved in the RTTM format to better understand the names and organizations mentioned in the customer review.\\n<<>>: from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\ndiarization = pipeline('customer_review_audio.wav')\\nwith open('customer_review_audio.rttm', 'w') as rttm:\\n diarization.write_rttm(rttm)\"", "answer_id": "a3Am3fkJrppe8EKDezFmCJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 43, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/ner-english-large')\\n<<>>: Flair\\n<<>>: 1. We import the necessary classes from the Flair library, including Sentence and SequenceTagger.\\n2. The SequenceTagger.load('flair/ner-english-large') method is used to load the pre-trained NER model, which can identify named entities in a sentence.\\n3. We create a Sentence object from the input text, and the loaded model is used to predict named entities in the text.\\n4. The tagged entities can be extracted from the sentence object and displayed in the app for users to improve their writing.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/ner-english-large')\\nsentence = Sentence(\\\"I love using Flair.\\\")\\ntagger.predict(sentence)\\nnamed_entities = [entity for entity in sentence.get_spans('ner')]\\nprint(named_entities)\"", "answer_id": "YqAbSKMspt2VP5ASMVhY4h", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 44, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wikisql-supervised')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'TapasTokenizer' and 'TapasForQuestionAnswering' from transformers.\\n2. Load the pre-trained TAPAS model 'google/tapas-base-finetuned-wikisql-supervised', which is specifically designed for table question answering tasks.\\n3. Use the 'TapasTokenizer' to tokenize the questions and table data.\\n4. Pass the tokenized input to the model, which will generate answers for the given questions.\\n5. Retrieve the answers from the model output and process them.\\n<<>>: from transformers import TapasTokenizer, TapasForQuestionAnswering\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wikisql-supervised')\\ntokenizer = TapasTokenizer.from_pretrained('google/tapas-base-finetuned-wikisql-supervised')\\nquestions = [\\\"What is the total order amount?\\\", \\\"Which item has the highest price?\\\"]\\ntable_data = [['Item', 'Price'], ['Item2', 'Price2']]\\ninputs = tokenizer(table=table_data, queries=questions, padding='max_length', return_tensors='pt')\\noutputs = model(**inputs)\\npredicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach(), outputs.logits_aggregation.detach())\\nanswers = tokenizer.table_data_to_dict(table_data)\\nresults = [{answer: round(answer * 100, 1) for answer in answer_coordinates}, answer for answer in answers[predicted_aggregation_indices[0]]}\\n\"", "answer_id": "niGmPQE2yckoyBqnthXzrH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 45, "text": "<<>>: Tabular Tabular Classification\\n<<>>: AutoModel.from_pretrained('abhishek/autotrain-adult-census-xgboost')\\n<<>>: Joblib\\n<<>>:1. We import the necessary libraries - joblib and pandas. Joblib is used to load the pre-trained XGBoost model, and pandas is used to handle the CSV data file containing employee information.\\n2. We load the pre-trained model 'abhishek/autotrain-adult-census-xgboost', which is designed for predicting the income and age demographics of employees.\\n3. We read the employee data file and preprocess it according to the model's requirements (split into features and target variables).\\n4. We use the model to make predictions on the data, which can be used to identify top employees for potential promotions based on their income and age.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\n# Read and preprocess the employee data file\\nemployee_data = pd.read_csv('employee_data.csv')\\nemployee_data = employee_data[['feat1', 'feat2', 'feat3', 'feat4', 'feat5', 'feat6', 'feat7', 'feat8', 'feat9', 'feat10', 'feat11', 'feat12', 'feat13', 'feat14', 'feat15', 'feat16', 'feat17', 'feat18', 'feat19', 'feat20', 'feat21', 'feat22', 'feat23', 'feat24', 'feat25', 'feat26', 'feat27', 'feat28', 'feat29', 'feat30', 'feat31', 'target']]\\n# Make predictions based on the employee data\\npredictions = model.predict(employee_data)\\n# Identify top employees for potential promotions\\ntop_employees = employee_data[predictions.argmax()]\\n\"", "answer_id": "ahKnheRPa3ftWPguEwPQnQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 46, "text": "\\n<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-sqa')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, including TapasForQuestionAnswering and TapasTokenizer.\\n2. Load the pre-trained model 'google/tapas-base-finetuned-sqa' and the corresponding tokenizer.\\n3. Define the table containing the sales data and the question we want to answer.\\n4. Tokenize the question and table using the loaded tokenizer, and provide the input to the TapasForQuestionAnswering model.\\n5. Use the model's output to retrieve the desired information from the table, such as the total sales of the specified product.\\n<<>>: from transformers import TapasForQuestionAnswering, TapasTokenizer\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-sqa')\\ntokenizer = TapasTokenizer.from_pretrained('google/tapas-base-finetuned-sqa')\\ntable = \\\"Product,Week,Sales\\n A, 1, 100\\n A, 2, 200\\n A, 3, 150\\n B, 1, 500\\n B, 2, 400\\n B, 3, 250\\\"\\nquestion = \\\"What is the total sales of Product B?\\\"\\ninputs = tokenizer(table=table, queries=question, padding='max_length', return_tensors='pt')\\noutputs = model(**inputs)\\npredicted_answer = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach())\\n\"", "answer_id": "D7JNgPifaAwXWbLTrQwpqJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 47, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-mini-finetuned-sqa')\\n<<>>: Transformers\\n<<>>:1. Import the TapasForQuestionAnswering class from the transformers library provided by Hugging Face.\\n2. Load the pre-trained model 'google/tapas-mini-finetuned-sqa' using the from_pretrained method. This model is specifically designed for Sequential Question Answering (SQA) tasks, which is what we need for answering queries related to a table.\\n3. Create a SQA instance using the loaded model and the provided table data.\\n4. Pass the query, which will ask for the tallest animal in the table, to the SQA instance.\\n5. The model will then analyze the table data and return the answer accordingly.\\n<<>>: from transformers import TapasForQuestionAnswering\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-mini-finetuned-sqa')\\nmodel.instantiate(table=table_data)\\nquery = \\\"What is the tallest animal in the table?\\\"\\nresult = model(question=query, table=table_data)\\n\"", "answer_id": "AsnH28eJhooFmcJii45VSC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 48, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForDocumentQuestionAnswering.from_pretrained('L-oenai/LayoutLMX_pt_question_answer_ocrazure_correct_V15_30_03_2023')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes AutoModelForDocumentQuestionAnswering for the question-answering model.\\n2. We then use the from_pretrained method of the AutoModelForDocumentQuestionAnswering class to load the pre-trained model 'L-oenai/LayoutLMX_pt_question_answer_ocrazure_correct_V15_30_03_2023'. This model has been trained for document question-answering tasks, which is exactly what we need for finding answers from textbook content.\\n3. To use this model, we need to prepare the input by providing textbook content and a question. We can then use the model to detect the answer within the textbook content.\\n4. The app can then display the detected answer to the user.\\n<<>>: from transformers import AutoModelForDocumentQuestionAnswering\\nmodel = AutoModelForDocumentQuestionAnswering.from_pretrained('L-oenai/LayoutLMX_pt_question_answer_ocrazure_correct_V15_30_03_2023')\\n# Prepare input (textbook content + question)\\ninput_dict = {'context': 'This is the textbook content.', 'question': 'What is the answer?'}\\n# Use the model to detect answer in the textbook content\\noutput = model(input_dict)\"", "answer_id": "3ZsQY4QGwBA7cNxdUQo3hs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 49, "text": "<<>>: Natural Language Processing Document Question Answering\\n<<>>: pipeline('question-answering', model=LayoutLMForQuestionAnswering.from_pretrained('impira/layoutlm-document-qa', return_dict=True))\\n<<>>: Hugging Face Transformers\\n<<>>: 1. To analyze the legal contract, we first import the necessary classes from the transformers library and the pydal package. This includes LayoutLMForQuestionAnswering and pipeline from transformers, and Image and Layout from pydal.\\n2. We then create a question-answering pipeline using the LayoutLMForQuestionAnswering model and the pretrained 'impira/layoutlm-document-qa'.\\n3. Next, we load the image file and the text of the legal contract as a document. The image is converted to RGB format.\\n4. We then use the pipeline to extract information from the contract by providing specific questions and the image.\\n5. The output of the question-answering pipeline will provide the answers to the given questions about the legal contract.\\n<<>>: from transformers import LayoutLMForQuestionAnswering, pipeline\\nfrom PIL import Image\\nimport pydal\\ndocument_image = Image.open('contract_image.jpg').convert('RGB')\\nnlp = pipeline('question-answering', model=LayoutLMForQuestionAnswering.from_pretrained('impira/layoutlm-document-qa', return_dict=True))\\nquestions = [\\n \\\"What is the term of the agreement?\\\",\\n \\\"Who is the licensor?\\\",\\n \\\"What is the license fee?\\\",\\n]\\nanswers = []\\nfor question in questions:\\n inputs = {\\\"image\\\": document_image, \\\"question\\\": question}\\n answer = nlp(inputs)\\n answers.append(answer)\\nprint(answers)\\n\"", "answer_id": "aXzF2XTK5bj6N3Bhcickcw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 50, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='tinyroberta-distilled')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Initialize the Distilled RoBERTa-based question answering model 'tinyroberta-distilled' by creating a question-answering pipeline with the specified model.\\n3. Provide a sample input, which should include a context and question. The context can be any text relevant to the question.\\n4. The created pipeline will process the provided input and return an answer to the question.\\n<<>>: from transformers import pipeline\\nnlp = pipeline('question-answering', model='tinyroberta-distilled')\\ninput_dict = {\\n 'context': 'This is a context example.',\\n 'question': 'What is this example for?'\\n}\\nresult = nlp(input_dict)\\nanswer = result['answer']\"", "answer_id": "RExzMgay5J77serC2eAMxJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 51, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='thatdramebaazguy/roberta-base-squad')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'thatdramebaazguy/roberta-base-squad'. This model is a Roberta-based model trained on the SQuAD dataset for question-answering tasks.\\n3. The loaded model can then be used to answer questions from a given context. To do this, you will provide the context and the question to the model, and it will return the answer.\\n4. This model is a good choice for answering questions automatically from a document.\\n<<>>: from transformers import pipeline\\nquestion_answering_model = pipeline('question-answering', model='thatdramebaazguy/roberta-base-squad')\\ncontext = \\\"The document's text goes here...\\\"\\nquestion = \\\"Your question goes here...\\\"\\nresult = question_answering_model({'context': context, 'question': question})\"", "answer_id": "5aRh2DqQHxnumnkDg8royY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 52, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='BaptisteDoyen/camembert-base-xnli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'BaptisteDoyen/camembert-base-xnli'.\\n3. This model is a French language version of the CamemBERT model, which is capable of zero-shot classification tasks.\\n4. We can provide the news articles' text to the model along with the candidate labels (in French: 'sport', 'politique', 'science'), and the model will classify the articles based on the semantic similarity of their content and the candidate labels.\\n5. The output will be a list of probabilities for each candidate label, and the label with the highest probability can be considered the correct category for the news article.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='BaptisteDoyen/camembert-base-xnli')\\nnews_article_text = \\\"Text du bulletin d'information en fran\\u00e7ais...\\\"\\ncandidate_labels = ['sport', 'politique', 'science']\\nclassification_result = classifier(news_article_text, candidate_labels, hypothesis_template='Ce texte parle de {}.')\"", "answer_id": "Pa3HUeWYiDa3hmeB9BpBWK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 53, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Create a zero-shot classification pipeline using the 'cross-encoder/nli-deberta-v3-xsmall' model.\\n3. Pass the given piece of news to the classifier along with the candidate labels ('technology', 'sports', or 'politics').\\n4. The classifier will return a probability for each label, indicating its confidence that the news topic belongs to that category.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\nnews_topic = 'Apple just announced the newest iPhone X'\\ncandidate_labels = ['technology', 'sports', 'politics']\\nresult = classifier(news_topic, candidate_labels)\\nprint(result)\"", "answer_id": "RTus6gGA8pghyC8Q5vUpMN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 54, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5ForConditionalGeneration.from_pretrained('google/flan-t5-xxl')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library, including T5Tokenizer for tokenizing input text and T5ForConditionalGeneration for the text-to-text generation model.\\n2. Load the pre-trained 'google/flan-t5-xxl' model, which is specifically designed for various NLP tasks, including text-to-text generation.\\n3. Tokenize the input text (in English) using the T5Tokenizer and convert it into input_ids.\\n4. Use the model.generate() method to generate a response in French for the input_ids.\\n5. Decode the generated output using the T5Tokenizer to retrieve the response in French text.\\n<<>>: from transformers import T5Tokenizer, T5ForConditionalGeneration\\ntokenizer = T5Tokenizer.from_pretrained('google/flan-t5-xxl')\\nmodel = T5ForConditionalGeneration.from_pretrained('google/flan-t5-xxl')\\ninput_text = \\\"Bonjour, comment \\u00e7a va?\\\"\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\nresponse = tokenizer.decode(outputs[0])\\n\"", "answer_id": "ZcvsndbLh68Aptar6jw9GL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 55, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_fr_to_en', model='Helsinki-NLP/opus-mt-fr-en')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the import to create a translation pipeline with the model 'Helsinki-NLP/opus-mt-fr-en' that translates from French to English.\\n3. Pass the French text to the translation pipeline to obtain the English translation.\\n<<>>: from transformers import pipeline\\ntranslation = pipeline('translation_fr_to_en', model='Helsinki-NLP/opus-mt-fr-en')\\nfrench_text = \\\"Le syst\\u00e8me \\u00e9ducatif fran\\u00e7ais est compos\\u00e9 d'\\u00e9coles maternelles, d'\\u00e9coles \\u00e9l\\u00e9mentaires, de coll\\u00e8ges et de lyc\\u00e9es.\\\"\\nresult = translation(french_text)\\nenglish_text = result[0]['translation_text']\"", "answer_id": "FuuHBH5Cuo8DBXK7oX9Nh5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 56, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: M2M100ForConditionalGeneration.from_pretrained('facebook/m2m100_7_7')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, including M2M100ForConditionalGeneration for the encoder, and M2M100Tokenizer for tokenizing the input text.\\n2. Load the pre-trained model 'facebook/m2m100_7_7' using the from_pretrained method of the M2M100ForConditionalGeneration class. This model has been trained for many-to-many multilingual translation tasks.\\n3. Prepare the input text for the translation model by tokenizing it using the M2M100Tokenizer.\\n4. Use the model to generate translated text by calling the generate method with the tokenized input and specifying the desired target language code.\\n5. Decode the generated output to obtain the translated text.\\n<<>>: from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer\\nmodel = M2M100ForConditionalGeneration.from_pretrained('facebook/m2m100_7_7')\\ntokenizer = M2M100Tokenizer.from_pretrained('facebook/m2m100_7_7')\\ntext = \\\"Hello, how are you?\\\"\\ninput_tokens = tokenizer(text, return_tensors='pt')\\noutput_tokens = model.generate(**input_tokens, forced_bos_token_id=tokenizer.get_lang_id('en_XX'))\\ntranslated_text = tokenizer.batch_decode(output_tokens, skip_special_tokens=True)[0]\\n\"", "answer_id": "Jjjn4RckPtKNciJbwZwzQo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 57, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text summarization model.\\n3. Specify the model 'Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2' to be loaded. This is a pre-trained T5 model specifically designed for summarizing text.\\n4. Pass the lengthy article text to the summarizer and obtain a shorter summary.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2')\\narticle = \\\"Apple Inc. reported its quarterly earnings results yesterday. The company posted a record-breaking revenue of $123.9 billion for the first quarter of 2022, up by 11% from the same period last year. The increase was fueled by stronger demand for iPhones, iPads, and Macs, as well as continued growth in its services segment. Apple's operating profit for the quarter came in at $38.3 billion, up 17% from a year earlier. The results surpassed analysts' expectations, who had anticipated revenue of around $118 billion. This strong performance is largely attributed to the successful launch of the iPhone 13, which has enjoyed robust sales since its debut in September. Apple CEO Tim Cook said in a statement, \\\"Our record-breaking quarter reflects the strength of our entire ecosystem, from our innovative products and services to the unmatched dedication of our teams around the world.\\\" Despite the ongoing global supply chain disruptions, Apple has maintained its growth trajectory, thanks in part to its vertically integrated operations and nimble supply chain management. The company is expected to face stiffer competition going forward, particularly in the smartphone market, as rivals introduce new devices and increased pricing pressures.\\\"\\\"\\nsummary = summarizer(article, max_length=100, min_length=25, do_sample=False)[0]['summary_text']\"", "answer_id": "Evmrx6djWSeoK8EbLCXsAf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 58, "text": "The World Health Organization held a conference discussing the impacts of climate change on human health. The conference brought together leading experts from around the world to examine the current problems affecting people's health due to changing environmental conditions. Topics of discussion included increased occurrence of heat-related illnesses, heightened rates of vector-borne diseases, and the growing problem of air pollution. The conference concluded with a call to action for governments and organizations to invest in mitigating and adapting to the negative consequences of climate change for the sake of public health.\"", "answer_id": "UUy5h4PBMR6pL5EMgzFw5k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 59, "text": "A new study found that eating chocolate at least once a week can lead to better cognition, potentially due to its antioxidant-rich flavonoids and improved brain blood flow.\"", "answer_id": "mzeBZatTJjC2pg65aRpboV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 60, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-newsroom')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a summarization model.\\n3. Specify the model 'google/pegasus-newsroom' to be loaded. This model is designed for summarizing text, particularly long articles or news stories.\\n4. The created summarization model can be used to generate a summary of the input article, which can be provided to users before they read the full article.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-newsroom')\\nsummary = summarizer(long_article, min_length=50, max_length=150)[0]['summary_text']\\n\"", "answer_id": "DyDToCVeT3Zj3p8SfPfPvV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 61, "text": "YouTube will remove videos containing false claims about approved vaccines for measles, hepatitis B, and other long-running vaccines, and terminate the accounts of anti-vaccine influencers. In a blog post, the company said it removed 130,000 videos since it implemented a ban on misinformation about Covid-19 vaccines last year. The new policy covers long-approved vaccines, which are considered safe and effective by local health authorities and the World Health Organization (WHO). Tech giants have been criticized for not countering false health information on their sites. US President Joe Biden said social media platforms were largely responsible for people's skepticism in getting vaccinated, and appealed for them to address the issue.\"", "answer_id": "mSneaBqWnq5WQ3frErWM8N", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 62, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-generation', model='PygmalionAI/pygmalion-2.7b')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-generation model, which is capable of generating conversational responses.\\n3. Specify the model 'PygmalionAI/pygmalion-2.7b' to be loaded. This model has been trained on dialogue data and can be used to generate engaging and believable responses as a fictional chatbot.\\n4. The chatbot can be used to engage with users, providing them with a conversational experience based on the character's personality.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('text-generation', model='PygmalionAI/pygmalion-2.7b')\\ncharacter_personality = {'dialogue_history': '', 'character_persona': ''}\\ndialogue_history = \\\"You: \\\"\\ninput_prompt = f\\\"{character_personality['dialogue_history']}{chatbot('Hello, who are you?')['generated_text']}\\\"\\ncharacter_personality['dialogue_history'] = dialogue_history\\ncharacter_personality['character_persona'] = chatbot(input_prompt)\\nuser_response = character_personality['generated_text']\\n\"", "answer_id": "ZvXNSJxTrDF9vLwPMzTwXt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 63, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='facebook/opt-125m')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary functions, which are 'pipeline' and 'set_seed' from transformers.\\n2. Set a random seed using the 'set_seed' function.\\n3. Instantiate a text generation pipeline using the 'facebook/opt-125m' pretrained model, which is designed for generating text, particularly in a conversational context.\\n4. Provide a seed text for the story, such as \\\"A spaceship journey to a distant planet in search of a new home for humanity\\\".\\n5. Set the options 'max_length' and 'num_return_sequences' to control the length of the generated text and the number of alternative storylines.\\n6. Call the 'generate' function to create the story.\\n7. Print the generated storyline.\\n<<>>: from transformers import pipeline, set_seed\\nset_seed(42)\\ngenerator = pipeline('text-generation', model='facebook/opt-125m', do_sample=True, num_return_sequences=5, max_length=200)\\nseed_text = \\\"A spaceship journey to a distant planet in search of a new home for humanity\\\"\\ngenerated_story = generator(seed_text, max_length=200, num_return_sequences=5)\\n\"", "answer_id": "47WQbg8777zv2BEGv6mVRj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 64, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='gpt2-large')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' and 'set_seed' from transformers.\\n2. Use the 'pipeline' function to create a text generation model with the 'gpt2-large' model.\\n3. Set a random seed to ensure reproducibility of the generated text.\\n4. Use the generator function along with the provided prompt, \\\"Once upon a time, there was a brave knight and a dragon\\\", to generate a story start.\\n5. You can adjust 'max_length' if you want the story to end earlier.\\n<<>>: from transformers import pipeline, set_seed\\ngenerator = pipeline('text-generation', model='gpt2-large')\\nset_seed(42)\\nprompt = \\\"Once upon a time, there was a brave knight and a dragon\\\"\\ngenerated_story = generator(prompt, max_length=300)\\nprint(generated_story[0]['generated_text'])\"", "answer_id": "WrFa528tL6TverC3KW88vk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 65, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='bert-base-cased')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a fill-mask model, which is designed to automatically predict the missing word in a sentence where a mask token has been placed.\\n3. Specify the model 'bert-base-cased' to be loaded. This is a pre-trained transformer model that can analyze and predict missing words in a given sentence.\\n4. To use the model, simply provide a sentence with a mask token () where the missing word is expected to be. The model will then return the most plausible word to fill in the masked position.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='bert-base-cased')\\nresult = fill_mask('The cat is sitting on the chair.')\"", "answer_id": "7rrKpPQ7YfdmySsadAWuhh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 66, "text": "\\u5f88\\u9ad8\\u8f83\\u5728\\u5403\\u5927\\u57ce\\u5e02\\u3002.\"", "answer_id": "95PYbSQ9JQZe8SsuGV5CuY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 67, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='huggingface/CodeBERTa-small-v1')\\n<<>>: Transformers\\n<<>>: 1. We first import the necessary pipeline class from the transformers package.\\n2. We then create an instance of the fill-mask pipeline, specifying the model 'huggingface/CodeBERTa-small-v1'. This model has been trained on the CodeSearchNet dataset and is capable of completing masked code snippets.\\n3. We provide the incomplete code snippet with a masked token to the autocompleter, and it will predict the most likely token to fill the gap.\\n<<>>: from transformers import pipeline\\ncode_completer = pipeline('fill-mask', model='huggingface/CodeBERTa-small-v1')\\nincomplete_code = \\\"def hello_world():\\\\n\\\\t('hello, world!');\\\\n\\\\nhello_world()\\\"\\ncompleted_code = code_completer(incomplete_code)\\n\"", "answer_id": "Nj7zWq2gcKLWematuqHsh2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 68, "text": "\\n<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='cl-tohoku/bert-base-japanese-whole-word-masking')\\n<<>>: Transformers\\n<<>>: 1. We first import the necessary pipeline function from the transformers library.\\n2. We then use the pipeline function to load the fill-mask model 'cl-tohoku/bert-base-japanese-whole-word-masking'. This model is designed for filling in missing Japanese words within a given context.\\n3. We provide the text with the missing word(s) indicated by the [MASK] token, and the model will predict the most likely word to fill in the gap.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='cl-tohoku/bert-base-japanese-whole-word-masking')\\ntext = \\\"\\u3059\\u65e5\\u306f\\u540d\\u3046\\u306b\\u306a\\u3088\\u306e[MASK]\\u3067\\u3059\\u3002\\\"\\nresult = fill_mask(text)\"", "answer_id": "YPUJp6ZeerTcBTkbz2nnRs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 69, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Create an instance of the SentenceTransformer class with the model 'sentence-transformers/all-MiniLM-L12-v2'. This model is designed to map sentences to a high-dimensional vector space, which can be used for tasks like clustering or semantic search.\\n3. Encode the user input sentence and the existing sentences in your database using the model's encode method. This will convert each sentence into a 3-dimensional vector.\\n4. Compute the cosine similarity between the user input vector and the vectors of the existing sentences. Then sort the results by their similarity scores in descending order.\\n5. Return a list of sentence pairs with high similarity scores, which can be used for the comparison and contrast function on your platform.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nmodel = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\nuser_input_sentence = \\\"This is an example sentence.\\\"\\nexisting_sentences = [\\\"This is an example sentence.\\\", \\\"Each sentence is converted.\\\"]\\nuser_input_vector = model.encode(user_input_sentence)\\nexisting_vector = model.encode(existing_sentences)\\nsimilarity_scores = cosine_similarity(user_input_vector, existing_vector)\\nsorted_pairs = sorted(zip(existing_sentences, similarity_scores[0]), key=lambda x: x[1], reverse=True)\\n\"", "answer_id": "HJzWXdcViJbLfmwoz7Vumb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 70, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Initialize the model by loading the pre-trained 'sentence-transformers/paraphrase-multilingual-mpnet-base-v2' model.\\n3. Encode the two input sentences using the model's encode() method. This will generate embeddings for the sentences.\\n4. Compute the cosine similarity between the embeddings to find the similarity score between the two sentences. A higher similarity score indicates that the sentences are more similar in meaning.\\n5. Use the similarity score to produce a similar caption for the memes.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nmodel = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2')\\nsentences = ['Sentence 1', 'Sentence 2']\\nembeddings = model.encode(sentences)\\nsimilarity_score = cosine_similarity(embeddings[0].reshape(1, -1), embeddings[1].reshape(1, -1))[0][0]\\n# Use the similarity score to generate a similar caption\\n\"", "answer_id": "Dbg7debcNDCSW7pXCWBaQB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 71, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Create a SentenceTransformer instance using the 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2' model. This model has been trained to generate embeddings for sentences that can be used to compute their semantic similarity.\\n3. Use the encode method of the SentenceTransformer instance to get embeddings for each article title and compare their similarity.\\n4. Identify similar articles based on their embeddings and include them in the literature review section of the student's research paper.\\n<<>>: from sentence_transformers import SentenceTransformer\\narticle_titles = [\\\"Article title 1\\\", \\\"Article title 2\\\", \\\"Article title 3\\\"]\\n# replace with the titles of your articles\\nmodel = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')\\nembeddings = model.encode(article_titles)\\n\"", "answer_id": "R2FJPvBgDZctPrepsyutcw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 72, "text": "<<>>: Audio Text-to-Speech\\n<<>>: AutoModelForCausalLM.from_pretrained('espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804')\\n<<>>: ESPnet\\n<<>>:1. Import the necessary libraries for text-to-speech, such as transformers and IPython.\\n2. Load the Japanese text-to-speech model using the provided model name.\\n3. Convert the input Japanese sentence into a spectrogram (phonic) format using a separate phonetic pronunciation dictionary.\\n4. Use the loaded model to generate the speech audio file based on the input sentence and the previously mentioned conversion process.\\n5. Finally, save the generated speech audio file for later use or display.\\n<<>>: from transformers import AutoTokenizer, AutoModelForCausalLM\\nimport IPython.display as ipd\\ntokenizer = AutoTokenizer.from_pretrained('espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804')\\nmodel = AutoModelForCausalLM.from_pretrained('espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804')\\ninput_text = \\\"\\u65e5\\u672c\\u8a9e\\u306e\\u30c6\\u30ad\\u30b9\\u30c8\\\"\\ninput_ids = tokenizer.encode(input_text, return_tensors='pt')\\noutput = model.generate(input_ids)\\ngenerated_speech = ipd.Audio(output[0], rate=24000)\\ngenerated_speech.save('output_speech.wav')\\n\"", "answer_id": "48RbTb6ovfBPwKQ89xFJwq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 73, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We first import the necessary class from the transformers package. This includes WhisperProcessor and WhisperForConditionalGeneration.\\n2. We then use the from_pretrained method of the WhisperProcessor and WhisperForConditionalGeneration classes to load the pre-trained model 'openai/whisper-base'. This model has been trained for automatic speech recognition tasks, which is exactly what we need for transcribing audio files into text.\\n3. We load the audio file as an array of floating point values and preprocess it using the processor.\\n4. We then pass the preprocessed audio to the model, which generates a sequence of predicted token IDs.\\n5. Finally, we decode the predicted token IDs into a human-readable transcription.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-base')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\naudio_data = load_audio_data('audio_file_path.wav') # replace with the path to your audio file\\ninput_features = processor(audio_data, sampling_rate=16000, return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\\n\"", "answer_id": "nr7y3ujgPGdN5sH8Cf5nJX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 74, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: pipeline.from_pretrained('pyannote/overlapped-speech-detection', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: pyannote.audio\\n<<>>: 1. Import the 'Pipeline' class from the pyannote.audio library.\\n2. Use the 'from_pretrained' method of the Pipeline class to load the pre-trained model 'pyannote/overlapped-speech-detection'.\\n3. This model is designed to detect when two or more speakers are speaking at the same time in an audio stream.\\n4. Process the audio file with the pipeline to detect overlapping speakers.\\n<<>>: from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained('pyannote/overlapped-speech-detection', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\noutput = pipeline('audio.wav')\\n# replace 'audio.wav' with path to your audio file\\nfor speech in output.get_timeline().support():\\n print(f\\\"Overlapping speakers detected between {speech.start} and {speech.end}\\\")\\n\"", "answer_id": "hqLhmrpsyTDRt7b35WTEqP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 75, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Speech2TextForConditionalGeneration.from_pretrained('facebook/s2t-medium-librispeech-asr')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, such as Speech2TextProcessor and Speech2TextForConditionalGeneration, as well as torchaudio to load and process the audio data.\\n2. Load the pre-trained model 'facebook/s2t-medium-librispeech-asr' using the Speech2TextForConditionalGeneration class.\\n3. Load the processor using the Speech2TextProcessor class, which will help with converting audio input into a format the model can understand.\\n4. Create a function to handle user input, which should capture the audio data and then process it using the pre-trained processor.\\n5. Finally, use the model to generate a transcription of the input audio.\\n6. Repeat this process for every user input to transcribe the interaction with the smart speaker device.\\n<<>>: from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration\\nimport torchaudio\\nmodel = Speech2TextForConditionalGeneration.from_pretrained('facebook/s2t-medium-librispeech-asr')\\nprocessor = Speech2TextProcessor.from_pretrained('facebook/s2t-medium-librispeech-asr')\\ndef transcribe_user_input(audio_path):\\n audio, _ = torchaudio.load(audio_path)\\n input_features = processor(audio, return_tensors='pt').input_features\\n generated_ids = model.generate(input_features)\\n transcription = processor.batch_decode(generated_ids)\\n return transcription\"", "answer_id": "ARvptVCtZXN6K58jDpiKxK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 76, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: separator.from_hparams(source='speechbrain/sepformer-whamr-enhancement', savedir='pretrained_models/sepformer-whamr-enhancement')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class (SepformerSeparation) and package (torchaudio) for our task.\\n2. We then use the from_hparams method to load the pre-trained SepFormer model 'speechbrain/sepformer-whamr-enhancement'. This model is specifically designed for speech enhancement.\\n3. We can load the noisy audio file using the model's `model.sep.pathos.torchaudio.load()` method.\\n4. We then use the `model.sep.pathos.enhance_file()` method to generate an enhanced version of the noisy audio file.\\n5. Finally, we save the enhanced audio file using torchaudio's `save()` method.\\n<<>>: from speechbrain.pretrained import SepformerSeparation as separator\\nimport torchaudio\\nmodel = separator.from_hparams(source='speechbrain/sepformer-whamr-enhancement', savedir='pretrained_models/sepformer-whamr-enhancement')\\nnoisy = model.model.pathos.torchaudio.load('path/to/noisy_audio.wav')\\nenhanced = model.sep.pathos.enhance_file(noisy)\\ntorchaudio.save('enhanced.wav', enhanced.unsqueeze(0).cpu(), 16000)\\n\"", "answer_id": "mxKjkhJz5pksRKYDiL5Pgn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 77, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2Model.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the SpeechRecognitionModel from the huggingsound library.\\n2. Create a SpeechRecognitionModel instance by loading the pre-trained model 'jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn'. This model has been fine-tuned for Chinese speech recognition.\\n3. Provide a list of audio file paths to the model's transcribe method. This will transcribe the audio files into Chinese text.\\n4. The transcriptions will be returned as a list of strings, where each element corresponds to a transcription for the specified audio file paths.\\n<<>>: from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel('jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn')\\naudio_paths = ['/path/to/file.mp3', '/path/to/another_file.wav']\\ntranscriptions = model.transcribe(audio_paths)\\n\"", "answer_id": "D4qtrDtf4H3JzDRnSEyWze", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 78, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include transformers and torchaudio.\\n2. Load the pre-trained model 'JorisCos/DCUNet_Libri1Mix_enhsingle_16k'. This model is designed for enhancing audio recordings by reducing background noise.\\n3. Load the recording you want to enhance and preprocess it to match the model's requirements.\\n4. Feed the preprocessed recording to the model and generate the enhanced audio.\\n5. Save the enhanced audio to a file for further use.\\n<<>>: from transformers import BaseModel\\nimport torchaudio\\nmodel = BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\n# Load and preprocess your recording\\nrecording = ...\\n# Enhance the recording\\nenhanced_audio = model(torchaudio.load(recording))\\n# Save the enhanced audio\\nenhanced_audio.save('enhanced_recording.wav')\\n\"", "answer_id": "FAhbT9rYi7mEa338DaVNTF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 79, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_s2ut_en-hk')\\n<<>>: Fairseq\\n<<>>: 1. Import the required libraries, including fairseq, torchaudio, and huggingface_hub.\\n2. Download the speech-to-speech translation model 'facebook/xm_transformer_s2ut_en-hk' using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Use the downloaded model to perform speech-to-speech translation from English to Hokkien by providing an input file containing spoken English audio.\\n4. The output file will be the translated spoken Hokkien audio.\\n<<>>: import torchaudio\\nfrom fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nfrom huggingface_hub import snapshot_download\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_s2ut_en-hk')\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\ngenerator = task.build_generator([model], cfg)\\naudio, _ = torchaudio.load('/path/to/an/audio/file')\\nsample = S2THubInterface.get_model_input(task, audio)\\ntranslated_audio = S2THubInterface.get_prediction(task, model, generator, sample)\\nsnapshot_download('facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS', archive_map=hub_utils.from_pretrained('fairseq/xm_transformer_s2ut_en-hk', 'models'))\\ntranslated_unit = snapshot_download('facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS')\\nwith open(translated_unit, 'rb') as f:\\n translated_audio = f.read()\\n\"", "answer_id": "5YVm9Tf9aR8Xjmg8TmpeP9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 80, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/hubert-base-superb-ks')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'superb/hubert-base-superb-ks'.\\n3. The loaded model will be used for keyword spotting, which is a task where the model identifies a set of predefined keywords within a given audio or text file.\\n4. The created classifier can be used to detect user commands within an audio file or a voice assistant application.\\n<<>>: from transformers import pipeline\\nkeyword_spotting_model = pipeline('audio-classification', model='superb/hubert-base-superb-ks')\\naudio_file_path = 'path_to_audio_file.wav'\\n# Replace 'path_to_audio_file.wav' with the path to your audio file\\nresult = keyword_spotting_model(audio_file_path)\\n\"", "answer_id": "aKtSuQdzDDbY2CMqY3fuPx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 81, "text": "<<>>: Audio Classification\\n<<>>: Wav2Vec2Model.from_pretrained('facebook/wav2vec2-xlsr-53-espeak-cv-ft')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face.\\n2. Use the from_pretrained method to load the pre-trained model 'facebook/wav2vec2-xlsr-53-espeak-cv-ft'. This model has been trained on multilingual Common Voice data and can recognize phonetic labels in a variety of languages.\\n3. Load the audio file that you would like to transcribe into text.\\n4. Pass the loaded audio data through the model, which will output a list of predicted phonetic labels.\\n5. Use a dictionary to map the predicted phonetic labels to their corresponding textual representations.\\n<<>>: from transformers import Wav2Vec2Model\\nfrom datasets import load_dataset\\nmodel = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-xlsr-53-espeak-cv-ft')\\n# Load the audio file\\naudio_data = load_audio_data('')\\n# Replace with the path to your audio file\\nphonetic_labels = model(audio_data)\\ntranscription = map_phonetic_to_text(phonetic_labels)\\n\"", "answer_id": "crN5WEZ6k3HXXnJfHp8eVA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 82, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: Pipeline.from_pretrained('pyannote/voice-activity-detection')\\n<<>>: pyannote.audio\\n<<>>: 1. Import the necessary libraries, which are 'Pipeline' from pyannote.audio.\\n2. Use the 'Pipeline.from_pretrained' function to load the 'pyannote/voice-activity-detection' model.\\n3. Process your audio file with the voice activity detection (VAD) model to detect segments with voice activity.\\n4. Iterate over the detected segments and identify the spoken regions.\\n5. Summarize the spoken regions based on your preferences or use an existing summarization technique.\\n<<>>: from pyannote.audio import Pipeline\\nvad_pipeline = Pipeline.from_pretrained('pyannote/voice-activity-detection')\\noutput = vad_pipeline('audio.wav')\\n# replace 'audio.wav' with the path to your audio file\\nspoken_regions = []\\nfor segment in output.get_timeline().support():\\n # do the summary between segment.start and segment.end\\n # add the summarized text to spoken_regions list\\n\"", "answer_id": "UFXrirtJqxn5VJPqSGwekK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 83, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load(cached_download(hf_hub_url('julien-c/wine-quality', 'winequality-red.csv')))\\n<<>>: Scikit-learn\\n<<>>: 1. We import the necessary libraries, including huggingface_hub, joblib, and pandas.\\n2. We use the Hugging Face Hub to download the pre-trained model 'julien-c/wine-quality' and the associated wine data file 'winequality-red.csv'.\\n3. We load the model using joblib and the cached_download function to avoid any download delays in the future.\\n4. We read the wine data file into a pandas DataFrame and select the relevant features specified in the model configuration.\\n5. We preprocess the DataFrame by renaming the columns to maintain consistency with the model input requirements.\\n6. We use the loaded model to classify the quality of wine samples based on the input feature data.\\n<<>>: from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nREPO_ID = 'julien-c/wine-quality'\\nFILENAME = 'sklearn_model.joblib'\\nmodel = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))\\ndata_file = cached_download(hf_hub_url(REPO_ID, 'winequality-red.csv'))\\nwine_df = pd.read_csv(data_file, sep=';')\\nX = wine_df.drop(['quality'], axis=1)\\nY = wine_df['quality']\\nlabels = model.predict(X[:3])\"", "answer_id": "GyoQUQVoMNB8nwzAMGShKS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 84, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import the necessary libraries: joblib, pandas, and json.\\n2. Load the trained model using joblib.load() and the provided model file.\\n3. Load the configuration file to get the required features for the model (age, gender, and passenger class).\\n4. Read the passenger data file (data.csv) into a pandas DataFrame using pandas.read_csv().\\n5. Filter the DataFrame to include only the necessary features (age, gender, and passenger class) and rename the columns.\\n6. Use the model.predict() function to make predictions on the filtered DataFrame for survival status (survived or deceased).\\n<<>>: import joblib\\nimport pandas as pd\\nimport json\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "59et2gHSGoTzYEfaY2WTfF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 85, "text": "<<>>: Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries, including joblib and pandas.\\n2. Load the pre-trained model using joblib.load('model.joblib').\\n3. Read the input data, which contains the vehicle characteristics and miles traveled, in a pandas DataFrame.\\n4. Create a function that takes the input data and applies the necessary transformations, such as standardization and scaling.\\n5. Use the trained model to predict CO2 emissions based on the input data.\\n6. The output will provide the predicted CO2 emissions for each vehicle based on its characteristics.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\ndata = data[['feat_1', 'feat_2', 'feat_3', 'feat_4', 'miles']]\\ndef predict(data):\\n transformed_data = (data - pd.to_numeric(data['feat_1'], data['feat_2'], data['feat_3'], data['feat_4'])) / data['miles']\\n return model.predict(transformed_data)\\nco2_emissions = predict(data)\"", "answer_id": "Ydixg8GCFpVDvFufQsdzms", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 86, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load(cached_download(hf_hub_url('imodels/figs-compas-recidivism', 'sklearn_model.joblib')))\\n<<>>: Scikit-learn\\n<<>>:1. Import the required libraries, including joblib, pandas, numpy, and Hugging Face Hub utilities like 'hf_hub_url' and 'cached_download'.\\n2. Load the pre-trained Scikit-learn model from the Hugging Face Hub using the 'imodels/figs-compas-recidivism' identifier. The model is trained for predicting future criminal re-offense from the COMPAS dataset.\\n3. Load the dataset of interest, preprocess it if required, and split it into training and testing sets.\\n4. Pass the test set data through the trained model to evaluate its performance.\\n5. The model will produce a set of scores corresponding to the likelihood of future criminal re-offense. The highest-scoring individuals will be more likely to re-offend, while those with lower scores are less likely to re-offend.\\n<<>>: from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nimport numpy as np\\nREPO_ID = 'imodels/figs-compas-recidivism'\\nFILENAME = 'sklearn_model.joblib'\\nmodel = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))\\n# Load your dataset here and preprocess as required\\nX_test = pd.DataFrame(test_data)\\nY_test = model.predict(X_test)\\n\"", "answer_id": "J27dYsCaKveZnwxUfB9Jj5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 87, "text": "<<>>: Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We first import the necessary libraries, including joblib for loading the pre-trained model and pandas for handling tabular data.\\n2. We then load the pre-trained model 'model.joblib', which has been trained to predict carbon emissions based on input features.\\n3. We load the given compound data from a CSV file into a pandas DataFrame and extract the relevant features for the model.\\n4. We preprocess the DataFrame to match the model's requirements and then use the predict function to obtain the carbon emissions prediction for the given compound.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('compound_data.csv')\\nfeatures = ['feature1', 'feature2', 'feature3']\\ndata = data[features]\\npredictions = model.predict(data)\\n\"", "answer_id": "aZrEMeGKbSkZvMmqiXekZT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 88, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the required libraries, which include joblib, pandas, and json.\\n2. Load the pre-trained model using joblib.load and the provided model path ('model.joblib').\\n3. Load the feature configuration using json.load and the provided config path ('config.json').\\n4. Read the input data from a CSV file using pandas, and select the required features from the dataset.\\n5. Rename the columns of the input data with the prefix 'feat_'.\\n6. Use the loaded model to predict carbon emissions for each row in the input data.\\n7. Return a list of dictionaries containing the input data frames and the predicted carbon emissions.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "auxfCzpQyx4xxUh3X7e7wm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 89, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We import the necessary libraries, including joblib and pandas. Joblib is used for loading the pre-trained regression model, and pandas helps with handling tabular data.\\n2. We then load the pre-trained regression model 'model.joblib' using joblib.load. This model has been trained for predicting carbon emissions of electric vehicles.\\n3. Next, we load the data for this year's emissions predictions. This data should be in a CSV format and should match the features used for training the model.\\n4. We then use the model to make predictions on the input data, which will result in forecasted carbon emissions for this year.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\n# replace 'data.csv' with the path to your data file\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "T2EyEJaZvJTxoi9YELPffF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 90, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Hugging Face\\n<<>>: 1. First, import necessary libraries including joblib and pandas.\\n2. Load the pre-trained model 'model.joblib' using joblib.load. This model has been trained on tips given by waiters, and it can predict the appropriate tip amount based on input features.\\n3. Read the dataset containing the input features of the customers (e.g., total bill, time of day, size of the party, and smoker/non-smoker status). This can be done using pandas.\\n4. Extract the necessary features specified in the 'config.json' file and convert the input data into a dataframe with those features.\\n5. Finally, use the model to predict the appropriate tip amount for each customer by calling the 'predict' method on the loaded model.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('customer_data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "28t6GthxpXAK59MFBkTjay", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 91, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/ppo-CartPole-v1',filename='{MODEL FILENAME}.zip')\\n<<>>: Stable-Baselines3\\n<<>>: 1. Install the necessary packages, including rl_zoo3, stable-baselines3, and stable-baselines3-contrib.\\n2. Train the reinforcement learning model using the provided RL Zoo training tools and the CartPole-v1 environment. The trained model will be optimized for the CartPole-v1 environment.\\n3. Load the trained model from the Hugging Face model hub using the provided repo_id and filename.\\n4. Use the loaded model to perform loading and unloading tasks in the robotic arm application.\\n5. Integrate the trained model into the robotic arm control system to optimize loading and unloading tasks.\\n<<>>: import rl_zoo3\\nfrom stable_baselines3 import PPO\\nfrom stable_baselines3.common.env_util import make_vec_env\\nrepo_id = 'sb3/ppo-CartPole-v1'\\nfilename = '{MODEL FILENAME}.zip'\\nmodel_checkpoint = rl_zoo3.load_from_hub(repo_id, filename=filename)\\nmodel = PPO.load(model_checkpoint)\\nenv = make_vec_env('CartPole-v1', n_envs=1)\\n\"", "answer_id": "UdMNfVoKz8uRWXNDdJsrqo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 92, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionInpaintPipeline.from_pretrained('stabilityai/stable-diffusion-2-inpainting', torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. We first import the necessary class from the diffusers package. This includes StableDiffusionInpaintPipeline for the text-to-image generation model.\\n2. We then use the from_pretrained method of the StableDiffusionInpaintPipeline class to load the pre-trained model 'stabilityai/stable-diffusion-2-inpainting'. This model has been trained for text-to-image tasks, which is exactly what we need for generating an image for the \\\"Space Party\\\" event.\\n3. We provide a prompt describing the desired image: \\\"A party in space with astronauts and aliens having fun together\\\".\\n4. The model generates an image based on the prompt, which can then be used for the event promotion.\\n<<>>: from diffusers import StableDiffusionInpaintPipeline\\nimport torch\\nprompt = 'A party in space with astronauts and aliens having fun together'\\npipe = StableDiffusionInpaintPipeline.from_pretrained('stabilityai/stable-diffusion-2-inpainting', torch_dtype=torch.float16)\\nimage = pipe(prompt=prompt).images[0]\"", "answer_id": "MXJ2nhv6WkSgWicB4ape3C", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 93, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base', scheduler=EulerDiscreteScheduler.from_pretrained(stabilityai/stable-diffusion-2-1-base, subfolder=scheduler), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. We first import the necessary classes from the diffusers package, which includes StableDiffusionPipeline and EulerDiscreteScheduler.\\n2. We then load the pre-trained model 'stabilityai/stable-diffusion-2-1-base' using the from_pretrained method of the StableDiffusionPipeline class, and the corresponding scheduler using the from_pretrained method of the EulerDiscreteScheduler class.\\n3. By specifying the 'cuda' device, we can use the GPU if available.\\n4. We provide the text prompt \\\"two tigers in a natural setting\\\" to generate an image based on that description using the pipeline.\\n5. We save the generated image to a file, which can be used for the promotional material.\\n<<>>: from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\\nmodel_id = 'stabilityai/stable-diffusion-2-1-base'\\nscheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder='scheduler')\\npipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nprompt = 'two tigers in a natural setting'\\nimage = pipe(prompt).images[0]\\nimage.save('tiger_image.png')\\n\"", "answer_id": "dUQym438KrRRVPBZ9jRGMW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 94, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'Swin2SRForConditionalGeneration' from transformers and 'torch' for PyTorch.\\n2. Use the 'from_pretrained' method of the Swin2SRForConditionalGeneration class to load the pre-trained model 'condef/Swin2SR-lightweight-x2-64'. This model is designed for image super-resolution and will upscale the low-resolution movie character images.\\n3. Prepare the input image by converting it to a tensor format and keeping only the required parts.\\n4. Use the model to upscale the low-resolution image and generate a high-resolution version.\\n5. Convert the generated image back to a tensor format and save or display it.\\n<<>>: from transformers import Swin2SRForConditionalGeneration\\nimport torch\\nmodel = Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\ninput_image = torch.tensor(low_res_image).cuda() # Preprocess the input image\\ngenerated_image = model.generate_image(input_image)\\nhigh_res_image = generated_image.squeeze().cpu().numpy()\\n\"", "answer_id": "BmfLTLsCCyLTKKYYT2bLAS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 95, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: AutoModel.from_pretrained('naver-clova-ix/donut-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, which are AutoModel and AutoTokenizer.\\n2. Load the pre-trained 'naver-clova-ix/donut-base' model using the AutoModel.from_pretrained() method. This model is trained to generate text descriptions for images.\\n3. Load the tokenizer for the model using AutoTokenizer.from_pretrained().\\n4. For each image in the list, convert it to an input suitable for the model by using the tokenizer.encode() method.\\n5. Generate a description for the image using the model.generate() method, and decode the output with the tokenizer.decode() method to obtain the caption.\\n<<>>: from transformers import AutoModel, AutoTokenizer\\nmodel = AutoModel.from_pretrained('naver-clova-ix/donut-base')\\ntokenizer = AutoTokenizer.from_pretrained('naver-clova-ix/donut-base')\\nimage_captioning_pipeline = pipeline('image-to-text', model=model, tokenizer=tokenizer)\\ncaptions = [image_captioning_pipeline(image) for image in image_list]\\n\"", "answer_id": "d6eRaYkaF2EXmiC9Zyxovf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 96, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text2text-generation model.\\n3. Specify the model 'salesforce/blip2-opt-6.7b' to be loaded. This model is trained to generate textual descriptions for images and videos, which can be useful for creating descriptions for product images and videos.\\n4. The created model can be used to generate textual descriptions for images and videos related to your product, helping to improve your content marketing efforts.\\n<<>>: from transformers import pipeline\\nimage_captioning_model = pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\nimage_description = image_captioning_model({\\\"image\\\": product_image_data})\\n\"", "answer_id": "eHzhTJdjYTcNX6P7JR8jhZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 97, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='Bingsu/temp_vilt_vqa', tokenizer='Bingsu/temp_vilt_vqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, such as 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the model 'Bingsu/temp_vilt_vqa' and create a visual question answering pipeline.\\n3. The pipeline will accept an image and a question about the image, and it will return an answer. This answer can assist visually impaired users in understanding their surroundings.\\n4. To use this pipeline, provide an image file path and a question related to the image.\\n<<>>: from transformers import pipeline\\nvqa_pipeline = pipeline('visual-question-answering', model='Bingsu/temp_vilt_vqa', tokenizer='Bingsu/temp_vilt_vqa')\\nanswer = vqa_pipeline(image='path/to/image.jpg', question='What is in the image?')\\n\"", "answer_id": "EGjdFwnPxFMrKzfvYWXew2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 98, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary library, which is the 'pipeline' function from transformers.\\n2. Use the 'pipeline' function from transformers to create a visual question answering model using the 'microsoft/git-base-vqav2' model.\\n3. This model is designed to analyze images and answer questions based on the content of the image.\\n4. To use the model, provide the image file and a text question related to the image.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n# Load image and question\\nimage_path = \\\"path/to/your/image.jpg\\\"\\nquestion = \\\"Is this vegan?\\\"\\nanswer = vqa(image=image_path, question=question)\\nprint(answer['answer'])\"", "answer_id": "dwjMhG9mKKMYzsrXaExtR9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 99, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForDocumentQuestionAnswering.from_pretrained('L-oenai/LayoutLMX_pt_question_answer_ocrazure_correct_V15_30_03_2023')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes AutoModelForDocumentQuestionAnswering for the document question-answering model.\\n2. We then use the from_pretrained method of the AutoModelForDocumentQuestionAnswering class to load the pre-trained model 'L-oenai/LayoutLMX_pt_question_answer_ocrazure_correct_V15_30_03_2023'. This model has been trained for multimodal document question answering tasks, which is exactly what we need for extracting information from legal documents.\\n3. We can use this model to answer questions related to a given document by providing the question and the document context to the model.\\n4. The model will return an answer to the question based on the context provided.\\n<<>>: from transformers import AutoModelForDocumentQuestionAnswering\\nmodel = AutoModelForDocumentQuestionAnswering.from_pretrained('L-oenai/LayoutLMX_pt_question_answer_ocrazure_correct_V15_30_03_2023')\\n# For using the model, see the example code provided in the API\\n\"", "answer_id": "oVXxVivZRXLkkKG9YGrtAi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 100, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model.\\n3. Specify the model 'tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa' to be loaded. This model is designed for document-based question-answering tasks and is fine-tuned on a dataset containing medical patient information.\\n4. The created model can be used to process medical documents and extract answers to patient questions based on the information found in the documents.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\nquestion = \\\"What is the patient's diagnosis?\\\"\\ncontext = \\\"Medical document content goes here...\\\"\\nresult = qa_pipeline(question=question, context=context)\\nanswer = result['answer']\"", "answer_id": "5goTTRp6nPXTz7S3BKPHsS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 101, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='seungwon12/layoutlmv2-base-uncased_finetuned_docvqa', tokenizer='seungwon12/layoutlmv2-base-uncased_finetuned_docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model that is specifically designed for answering questions related to a scanned document.\\n3. Specify the model 'seungwon12/layoutlmv2-base-uncased_finetuned_docvqa' and its tokenizer to be loaded. This model is fine-tuned on the DocVQA dataset and is designed to answer questions based on the content and structure of a document.\\n4. Pass the question and the document context to the created question-answering pipeline to get the answer.\\n<<>>: from transformers import pipeline\\ndoc_qa = pipeline('question-answering', model='seungwon12/layoutlmv2-base-uncased_finetuned_docvqa', tokenizer='seungwon12/layoutlmv2-base-uncased_finetuned_docvqa')\\nquestion = \\\"What is the total amount due?\\\"\\ncontext = \\\"Scanned document content here...\\\"\\nanswer = doc_qa(question=question, context=context)\\n\"", "answer_id": "YBgBniRVoNoLriEcua2vdz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 102, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: LayoutLMv3ForQuestionAnswering.from_pretrained('rubentito/layoutlmv3-base-mpdocvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'LayoutLMv3ForQuestionAnswering' and 'LayoutLMv3Tokenizer' from transformers.\\n2. Load the pre-trained model 'rubentito/layoutlmv3-base-mpdocvqa' using the LayoutLMv3ForQuestionAnswering.from_pretrained() method.\\n3. Load the tokenizer using the LayoutLMv3Tokenizer.from_pretrained() method.\\n4. Convert the PDF document to a format that can be tokenized, such as an image of the document's text or a plain text version.\\n5. Ask the question about the document's content.\\n6. Use the tokenizer to tokenize the input document and the question.\\n7. Pass the tokenized input to the model and obtain the output logits.\\n8. Get the answer span from the logits using the tokenizer's decode_answer_spec() method.\\n9. Print the answer span from the question and the document.\\n<<>>: import torch\\nfrom transformers import LayoutLMv3ForQuestionAnswering, LayoutLMv3Tokenizer\\nfrom PIL import Image\\nmodel = LayoutLMv3ForQuestionAnswering.from_pretrained('rubentito/layoutlmv3-base-mpdocvqa')\\ntokenizer = LayoutLMv3Tokenizer.from_pretrained('rubentito/layoutlmv3-base-mpdocvqa')\\ndocument_path = \\\"path/to/document_image.png\\\"\\nquestion = \\\"What is the main topic of the document?\\\"\\nimage = Image.open(document_path)\\nencoded_input = tokenizer(question, image, return_tensors='pt')\\noutput = model(**encoded_input)\\nanswer_start = torch.argmax(output.start_logits)\\nanswer_end = torch.argmax(output.end_logits)\\nanswer = tokenizer.decode(encoded_input['input_ids'][0, answer_start:answer_end + 1])\\nprint(f\\\"Answer: {answer}\\\")\"", "answer_id": "ft7p6iyaozCKMjAFihNSE5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 103, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: pipeline('depth-estimation', model='sayakpaul/glpn-nyu-finetuned-diode-221121-113853')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a depth estimation model.\\n3. Specify the model 'sayakpaul/glpn-nyu-finetuned-diode-221121-113853' to be loaded. This model has been fine-tuned on the diode-subset dataset and is designed to estimate depth from a single 2D image.\\n4. The created model can be used to estimate the depth of rooms captured in photographs, which is crucial for interior design activities.\\n5. You will be able to input a single image and get the depth map as output, which can then be used for room remodeling.\\n<<>>: from transformers import pipeline\\nimport cv2\\ndepth_estimator = pipeline('depth-estimation', model='sayakpaul/glpn-nyu-finetuned-diode-221121-113853')\\ninput_image = cv2.imread('room_image.jpg') # Input the room image\\ndepth_map = depth_estimator(input_image)\"", "answer_id": "8ntgpLBFEe65pYE2AHRbwV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 104, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-104421')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries, namely AutoModel from transformers and torch.\\n2. Load the pre-trained depth estimation model 'sayakpaul/glpn-nyu-finetuned-diode-221116-104421' using the AutoModel.from_pretrained() function.\\n3. Use the loaded model to estimate the depth map from the real-time video feed.\\n4. The depth estimation can be used to enhance the safety and security of the autonomous vehicles by providing them with additional depth information.\\n###Code example (untested): \\nfrom transformers import AutoModel\\nimport torch\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-104421')\\n# Process the video feed and use the model to estimate the depth map.\\n\"", "answer_id": "FBjjaprAVf3wQH4yNAgp6s", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 105, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221122-014502')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library. In this case, we need the AutoModel class.\\n2. Load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221122-014502' using the from_pretrained method of the AutoModel class. This model is fine-tuned for depth estimation tasks in computer vision, which is useful for autonomous vehicles to estimate the depth of the field.\\n3. We can now use this model to estimate the depth of the field from an input image. This information is essential for autonomous vehicles to navigate and avoid obstacles.\\n<<>>: from transformers import AutoModel\\ndepth_estimation_model = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221122-014502')\\ndepth_map = depth_estimation_model(input_image)\\n\"", "answer_id": "HVchHpQKpnMiW4HaHKNscX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 106, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: pipeline('depth-estimation', model='sayakpaul/glpn-nyu-finetuned-diode-221122-044810')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a depth estimation model.\\n3. Specify the model 'sayakpaul/glpn-nyu-finetuned-diode-221122-044810' to be loaded. This model is fine-tuned on the diode-subset dataset for depth estimation tasks, which is perfect for measuring the depth of spaces in street images.\\n4. The created pipeline can be used to estimate the depth of various spaces in the input images. This information can be useful for city planners to analyze the depth of public spaces and make informed decisions.\\n<<>>: from transformers import pipeline\\ndepth_estimator = pipeline('depth-estimation', model='sayakpaul/glpn-nyu-finetuned-diode-221122-044810')\\nimage_path = 'path_to_street_image.jpg'\\n# replace 'path_to_street_image.jpg' with the path to the image you want to analyze\\ndepth_map = depth_estimator(image_path)\"", "answer_id": "EyYvokShnsEHhupFsrEDFd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 107, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: CLIPModel.from_pretrained('openai/clip-vit-large-patch14')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary packages like PIL for image processing, requests for fetching image data, and CLIPProcessor and CLIPModel from the transformers library provided by Hugging Face.\\n2. Load the pre-trained model and processor using the 'openai/clip-vit-large-patch14' configuration. This model is designed for zero-shot image classification tasks.\\n3. Given an image of a product, load the image and process it using the loaded processor.\\n4. Define a list of potential class names (e.g., 'clothing', 'electronics', 'furniture', etc.) for the model to classify the image.\\n5. Pass the processed image and class names through the model to get the probabilities for each class.\\n6. The model output can be used to identify the type of product in the image.\\n<<>>: from PIL import Image\\nimport requests\\nfrom transformers import CLIPProcessor, CLIPModel\\nmodel = CLIPModel.from_pretrained('openai/clip-vit-large-patch14')\\nprocessor = CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14')\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nclass_names = ['clothing', 'electronics', 'furniture']\\ninputs = processor(text=class_names, images=image, return_tensors='pt', padding=True)\\noutputs = model(**inputs)\\nlogits_per_image = outputs.logits_per_image\\nprobs = logits_per_image.softmax(dim=1)\\n\"", "answer_id": "C2acmeeyFLfsgnAXWMhc5N", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 108, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Initialize the image-classification pipeline using the 'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup' model.\\n3. Specify the image file path and a list of possible dog breeds to classify the image.\\n4. The pipeline will return the image classification prediction along with the breed.\\n<<>>: from transformers import pipeline\\nimage_classification = pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\nimage_path = 'path/to/image.jpg'\\ndog_breeds = ['Labrador Retriever', 'German Shepherd', 'Golden Retriever', 'Beagle', 'Bulldog']\\nresult = image_classification(image_path, dog_breeds)\\n\"", "answer_id": "bMh7KhCuhk3qDYvSLriTcy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 109, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ResNetForImageClassification.from_pretrained('microsoft/resnet-18')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and functions, such as AutoFeatureExtractor and ResNetForImageClassification from transformers, torch, and load_dataset from datasets.\\n2. Load the dataset containing the images of cell phones, laptops, and smartwatches.\\n3. Preprocess the image by resize and normalize it using the mean value and standard deviation of the dataset.\\n4. Use the from_pretrained method of the ResNetForImageClassification class to load the pre-trained model 'microsoft/resnet-18'. This model has been trained for image classification tasks and can categorize images into various categories.\\n5. Use the preprocessed image to predict the category of the device.\\n<<>>: from transformers import AutoFeatureExtractor, ResNetForImageClassification\\nimport torch\\nfrom datasets import load_dataset\\ndataset = load_dataset('huggingface/cats-image')\\nimage = dataset['test']['image'][0]['image']\\nfeature_extractor = AutoFeatureExtractor.from_pretrained('microsoft/resnet-18')\\nmodel = ResNetForImageClassification.from_pretrained('microsoft/resnet-18')\\ninputs = feature_extractor(image, return_tensors='pt')\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\npredicted_label = logits.argmax(-1).item()\\nprint(model.config.id2label[predicted_label])\"", "answer_id": "WpCFCQWaiNbw3jDabLFWHd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 110, "text": "<<>>: Computer Vision Image Classification\\n<<>>: timm.create_model('hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the necessary timm library.\\n2. Create the Vision Transformer model by calling the timm.create_model function with the provided model name and setting pretrained to True.\\n3. The Vision Transformer model is trained for image classification tasks and can be used to identify logos in a collection of images.\\n4. Load the image data and apply the necessary transformations to prepare the images for classification.\\n5. Use the model to classify the images and identify the logos present in the collection.\\n<<>>: import timm\\nfrom PIL import Image\\nmodel = timm.create_model('hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)\\nmodel = model.eval()\\ndata_config = timm.data.resolve_model_data_config(model)\\ntransforms = timm.data.create_transform(**data_config, is_training=False)\\nimage = Image.open('logo_image_path.jpg')\\n# replace 'logo_image_path.jpg' with the path to your image\\noutput = model(transforms(image).unsqueeze(0))\\n\"", "answer_id": "8YbRbHpZ96Sd3kePyYPBXW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 111, "text": "<<>>: Computer Vision Object Detection\\n<<>>: pipeline('object-detection', model='keremberke/yolov8m-nlf-detect')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'YOLO' and 'render_result' from the 'ultralyticsplus' package.\\n2. Create an object detection pipeline using the 'YOLO' class and specify the 'keremberke/yolov8m-nlf-detect' model.\\n3. Set the confidence level, Intersection over Union (IoU) threshold, agnostic Non-Maximum Suppression (NMS), and maximum number of detections.\\n4. Provide the input image (either a URL or a local path) to the pipeline for object detection.\\n5. Print and render the results, which display the detected objects and their bounding boxes.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-nlf-detect')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'image_url_or_local_path.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "9mN9D4nKjjd9LnnP2M8zWq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 112, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include requests, torch, PIL, and transformers.\\n2. Load the pre-trained model 'facebook/mask2former-swin-base-coco-panoptic' using the from_pretrained method of the Mask2FormerForUniversalSegmentation class.\\n3. Load the image from a URL or a local file, using the Image.open method from the PIL library.\\n4. Process the image using the AutoImageProcessor, which prepares the image for the segmentation model.\\n5. Pass the processed image to the model and obtain the outputs, which include class and mask logits.\\n6. Post-process the results to obtain the final segmentation masks that can be used to analyze the image.\\n<<>>: from requests import get\\nfrom torch import no_grad\\nfrom PIL import Image\\nfrom transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation\\nprocessor = AutoImageProcessor.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(get(url, stream=True).raw)\\ninputs = processor(images=image, return_tensors='pt')\\nwith no_grad():\\n outputs = model(**inputs)\\nclass_queries_logits = outputs.class_queries_logits\\nmasks_queries_logits = outputs.masks_queries_logits\\nresult = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_panoptic_map = result['segmentation']\"", "answer_id": "ZVKDQaV3vFxCohgmJYCCyN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 113, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-base-ade')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers and PIL packages. This includes MaskFormerForInstanceSegmentation for the image segmentation model and Image for processing image data.\\n2. We then use the from_pretrained method of the MaskFormerForInstanceSegmentation class to load the pre-trained model 'facebook/maskformer-swin-base-ade'. This model has been trained for image segmentation tasks, which is exactly what we need for analyzing satellite images.\\n3. We load the image data from a file or a URL, in which the satellite image is stored.\\n4. This model can then be used to analyze the image, segment it, and identify different objects in it.\\n<<>>: from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation\\nfrom PIL import Image\\nimage = Image.open('satellite_image_path.jpg')\\n# replace 'satellite_image_path.jpg' with path to your satellite image\\nprocessor = MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-base-ade')\\ninputs = processor(images=image, return_tensors='pt')\\nmodel = MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-base-ade')\\noutputs = model(**inputs)\\npredicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\n\"", "answer_id": "74pd37B25bB7m82BMoLUnS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 114, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-cityscapes-1024-1024')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries: SegformerFeatureExtractor, SegformerForSemanticSegmentation, PIL (Python Imaging Library), and requests.\\n2. Load the pre-trained Segformer model using the from_pretrained method. In this case, the model is 'nvidia/segformer-b0-finetuned-cityscapes-1024-1024'.\\n3. Open the image using the PIL library and the requests library to fetch the image if it's from a URL.\\n4. Use the feature extractor to process the image and convert it into the appropriate format for the model.\\n5. Pass the processed image to the model and obtain segmentation results.\\n6. Analyze and segment the various urban elements in the image (roads, buildings, etc.).\\n<<>>: from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = SegformerFeatureExtractor.from_pretrained('nvidia/segformer-b0-finetuned-cityscapes-1024-1024')\\nmodel = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-cityscapes-1024-1024')\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\n\"", "answer_id": "4avweTBie2CeDZr8aWGTCT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 115, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes and packages, which include the Mask2FormerForUniversalSegmentation model and the Image class from the PIL package to process image data.\\n2. Load the pre-trained Mask2Former model for universal segmentation using the from_pretrained method and the 'facebook/mask2former-swin-base-coco-panoptic' identifier.\\n3. Load the aerial image data, which can be acquired from the drone's camera or a remote source.\\n4. Process the image data using the model and obtain segmented results for different objects and categories in the image.\\n<<>>:from transformers import Mask2FormerForUniversalSegmentation, AutoImageProcessor\\nfrom PIL import Image\\nprocessor = AutoImageProcessor.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-base-coco-panoptic')\\nimage = Image.open('aerial_image.jpg')\\n# Replace 'aerial_image.jpg' with the path to your aerial image\\ninputs = processor(images=image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\nresult = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_panoptic_map = result['segmentation']\"", "answer_id": "97TP7viKc4Xi5VUJncCHgb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 116, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-scribble')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, including ControlNetModel from the diffusers package and PIL for handling images.\\n2. Load the pre-trained ControlNet model using the from_pretrained method and the provided model name 'lllyasviel/sd-controlnet-scribble'.\\n3. Load an example scribble image to serve as a control input for your project.\\n4. Create an instance of the StableDiffusionControlNetPipeline using the from_pretrained method and the provided model name 'runwayml/stable-diffusion-v1-5'.\\n5. Set the scheduler for your pipeline using UniPCMultistepScheduler.\\n6. Enable memory-efficient attention and CPU offloading for the pipeline.\\n7. Modify the pipeline's input with a text description and the scribble image.\\n8. Generate the final image using the pipeline and save the output to a file.\\n<<>>: from PIL import Image\\nfrom diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nimport torch\\nfrom diffusers.utils import load_image\\ncontrol_image = load_image('path/to/scribble/image.png')\\ntext_description = 'A cat sitting on a windowsill'\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-scribble', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nimage = pipe(text_description, num_inference_steps=30, generator=torch.manual_seed(0), image=control_image).images[0]\\nimage.save('images/image_out.png')\\n\"", "answer_id": "QMwvLdCkbjQRnsVREMtyq5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 118, "text": "<<>>: Computer Vision Video Classification\\n<<>>: AutoModelForVideoClassification.from_pretrained('sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers package, which includes AutoModelForVideoClassification for video classification tasks.\\n2. We then use the from_pretrained method of the AutoModelForVideoClassification class to load the pre-trained model 'sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset'. This model has been trained for video classification tasks, which is exactly what we need for classifying sports videos.\\n3. We can then use this model to analyze sports videos and classify them into different sports categories, which can then be used to recommend workouts based on the type of sports enjoyed by the users.\\n<<>>: from transformers import AutoModelForVideoClassification\\nvideo_classifier = AutoModelForVideoClassification.from_pretrained('sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset')\\nsports_categories = video_classifier(sports_video_file_path)\\n\"", "answer_id": "5PKho96tE4jaDeffEYU8h8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 119, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-short-finetuned-kinetics')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes from the transformers package, including VideoMAEImageProcessor and VideoMAEForVideoClassification.\\n2. Load the pre-trained VideoMAE model using the from_pretrained method, with the identifier 'MCG-NJU/videomae-base-short-finetuned-kinetics'.\\n3. Process the input video frames using the VideoMAEImageProcessor, which prepares the video frames for the model.\\n4. Pass the processed video frames to the VideoMAE model to obtain the classification output.\\n5. Analyze the model's predicted action labels and use them to classify the videos.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-short-finetuned-kinetics')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-short-finetuned-kinetics')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_action = logits.argmax(-1).item()\\nprint(f\\\"Predicted action: {model.config.id2label[predicted_action]}\\\")\\n\"", "answer_id": "fGu6rxFceiv6pc7FmAGrnE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 120, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: CLIPModel.from_pretrained('openai/clip-vit-base-patch16')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including Image from PIL, requests, CLIPProcessor, and CLIPModel from transformers.\\n2. Load the pre-trained model 'openai/clip-vit-base-patch16' using the CLIPModel class.\\n3. Load the pre-trained CLIPProcessor using the from_pretrained method.\\n4. Load the image using the Image.open and requests.get methods.\\n5. Prepare the inputs for the model using the processor, including the possible class_names (cat, dog, or any other species or breed).\\n6. Feed the inputs into the model and obtain the logits per image.\\n7. Apply the softmax function to obtain the probabilities for each class.\\n8. Determine the class with the highest probability as the predicted class.\\n<<>>: from PIL import Image\\nimport requests\\nfrom transformers import CLIPProcessor, CLIPModel\\nmodel = CLIPModel.from_pretrained('openai/clip-vit-base-patch16')\\nprocessor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch16')\\nimage_url = 'https://your-image-url-here.jpg'\\nimage = Image.open(requests.get(image_url, stream=True).raw)\\ninputs = processor(text=['a photo of a cat', 'a photo of a dog'], images=image, return_tensors='pt', padding=True)\\noutputs = model(**inputs)\\nlogits_per_image = outputs.logits_per_image\\nprobs = logits_per_image.softmax(dim=1)\\nprint(probs)\\n\"", "answer_id": "iKTUGmHGCPphstgcZiDkc2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 121, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='laion/CLIP-ViT-B-32-laion2B-s34B-b79K')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and functions, such as 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the model 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K', which is designed for zero-shot image classification tasks.\\n3. Specify the pet names (e.g., 'dog' or 'cat') and provide a list of image paths.\\n4. Use the classifier to predict the category for each image based on the provided pet names.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-image-classification', model='laion/CLIP-ViT-B-32-laion2B-s34B-b79K')\\npet_names = ['dog', 'cat']\\nimage_paths = ['/path/to/dog_image.jpg', '/path/to/cat_image.jpg']\\n# Replace '/path/to/dog_image.jpg' and '/path/to/cat_image.jpg' with the actual image paths\\nresults = classifier(pet_names=pet_names, images=image_paths)\\n\"", "answer_id": "hCbzTFTGrTagcHFeu8PRpH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 122, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-ViT-B-16-laion2B-s34B-b88K')\\n<<>>: Hugging Face\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an image classification model.\\n3. Specify the model 'laion/CLIP-ViT-B-16-laion2B-s34B-b88K' to be loaded. This model is specifically designed for zero-shot image classification tasks, making it suitable for diagnosing plant diseases based on images.\\n4. The created classifier can be used to classify images of plant diseases and provide the appropriate diagnosis for different types of issues.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-ViT-B-16-laion2B-s34B-b88K')\\nplant_disease_diagnosis = image_classifier(image_path, class_names=['disease_1', 'disease_2', 'disease_3'])\\n\"", "answer_id": "TXkkox7Kn9f8SMFZdAJavb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 123, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='Filosofas/DialoGPT-medium-PALPATINE2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the model, which in this case is 'Filosofas/DialoGPT-medium-PALPATINE2'.\\n3. The loaded model will be used for text generation, which is a task where the model imitates human-like communication by creating a response based on the input provided.\\n4. The input should be a customer review, and the generated response will be an analysis of the review and the product's performance in the market.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='Filosofas/DialoGPT-medium-PALPATINE2')\\ninput_text = \\\"Our new product has received great reviews, but we need to analyze this customer review in particular and find out how well it is doing in the market.\\\"\\noutput_text = text_generator(input_text)[0]['generated_text']\\n\"", "answer_id": "9xcwi7Z8M53DBLLDxxbbmY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 124, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We first import the necessary classes from the transformers package provided by Hugging Face. This includes AutoModelForSeq2SeqLM for the text-to-text generation model.\\n2. We then use the from_pretrained method of the AutoModelForSeq2SeqLM class to load the pre-trained model 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3'. This model has been trained for text-to-text generation tasks, which is exactly what we need for creating better explanations for a chemistry concept.\\n3. We provide the input text that needs to be improved, along with the prompt asking for a better explanation, which will be used as the starting point for the generated text.\\n4. The model is then used to generate an improved explanation for the input text.\\n<<>>: from transformers import AutoModelForSeq2SeqLM\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ninput_text = \\\"\\\"\\ncontext_and_question = \\\"Improve this explanation of : \\\"\\ninputs = {'input_text': input_text, 'context_and_question': context_and_question}\\noutputs = model.generate(inputs, max_length=150, num_return_sequences=1)\\nbetter_explanation = outputs[0]['generated_text']\\n\"", "answer_id": "jM69pLmnP3rikvp7o44Hn2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 125, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('shahrukhx01/question-vs-statement-classifier')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the necessary packages from the transformers library, such as AutoTokenizer and AutoModelForSequenceClassification.\\n2. Use the from_pretrained method to load the pre-trained model 'shahrukhx01/question-vs-statement-classifier'. This model is designed to classify text as either questions or statements.\\n3. Tokenize the input text using the tokenizer and pass the tokenized input to the model.\\n4. The model will output two class probabilities, which can be used to determine whether the input text is a question or a statement.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSequenceClassification\\ntokenizer = AutoTokenizer.from_pretrained('shahrukhx01/question-vs-statement-classifier')\\nmodel = AutoModelForSequenceClassification.from_pretrained('shahrukhx01/question-vs-statement-classifier')\\ninput_text = \\\"Is it going to rain tomorrow?\\\"\\ninputs = tokenizer(input_text, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "GzkjvPWg3sCVuoGdSmd2gX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 126, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including AutoTokenizer and AutoModelForSequenceClassification from transformers, and torch for PyTorch.\\n2. Load the pre-trained model 'cross-encoder/ms-marco-MiniLM-L-12-v2'. This model is designed for information retrieval and can help sort out possible answers to a given question.\\n3. Use the tokenizer to tokenize the question and potential answer candidates.\\n4. Pass the tokenized input to the model, which will output scores for each candidate answer.\\n5. Sort the answers based on their scores to determine the most relevant ones.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSequenceClassification\\nimport torch\\nmodel = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-12-v2')\\ntokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L-12-v2')\\nquestion = \\\"What is the capital of France?\\\"\\ncandidate_answers = [\\\"Paris is the capital of France.\\\", \\\"The capital of France is Paris.\\\", \\\"France has no capital.\\\"]\\nfeatures = tokenizer([question] * len(candidate_answers), candidate_answers, padding=True, truncation=True, return_tensors='pt')\\nmodel.eval()\\nwith torch.no_grad():\\n scores = model(**features).logits\\n sorted_answers = [answer for _, answer in sorted(zip(scores, candidate_answers), reverse=True)]\\nprint(sorted_answers)\\n\"", "answer_id": "HcSmyBPbBveBoFGoRhfyjw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 127, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('dbmdz/bert-large-cased-finetuned-conll03-english')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the AutoTokenizer and AutoModelForTokenClassification classes from the transformers library.\\n2. Load the pre-trained BERT-large-cased model that has been fine-tuned on the CoNLL-03 dataset by calling the from_pretrained method of each class.\\n3. Tokenize and classify entities in the given news article using the tokenizer and model.\\n4. Extract and print the identified entities, including person names, organization names, and location names.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification\\ntokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-large-cased-finetuned-conll03-english')\\nmodel = AutoModelForTokenClassification.from_pretrained('dbmdz/bert-large-cased-finetuned-conll03-english')\\nnews_article = \\\"News article text goes here...\\\"\\ninputs = tokenizer(news_article, return_tensors=\\\"pt\\\")\\noutputs = model(**inputs)\\nentities = tokenizer.convert_ids_to_tokens(outputs.logits.argmax(-1).squeeze().tolist())\\nprint(entities)\\n\"", "answer_id": "ZSiL6ga6cKN7LEUDv9LWRt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 128, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Jean-Baptiste/camembert-ner')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We import the necessary classes from the transformers package provided by Hugging Face, including AutoModelForTokenClassification and AutoTokenizer.\\n2. We load the pre-trained model 'Jean-Baptiste/camembert-ner', which is specifically designed for Named Entity Recognition tasks, and the associated tokenizer for this model.\\n3. We use the tokenizer to tokenize the customer emails, which are then input into the model for entity extraction. The model identifies and classifies various entities in the text, such as person names, organization names, and locations.\\n4. We can then parse and analyze the extracted entities to gain insights about our customers and their organizations.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('Jean-Baptiste/camembert-ner')\\ntokenizer = AutoTokenizer.from_pretrained('Jean-Baptiste/camembert-ner')\\nemail_text = \\\"Customer email text here...\\\"\\ninputs = tokenizer(email_text, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "NdB3QJFfuVcvygtNqGNhsW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 129, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('neulab/omnitab-large-1024shot')\\n<<>>: PyTorch Transformers\\n<<>>:1. We import the necessary libraries, including AutoTokenizer and AutoModelForSeq2SeqLM, from the transformers library.\\n2. We load the pre-trained model 'neulab/omnitab-large-1024shot', which has been designed specifically for table-based question answering tasks.\\n3. We create a table containing the animals and their characteristics, which will be used as input for the model.\\n4. We then formulate our question and use the table as input to the model to retrieve the answer.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('neulab/omnitab-large-1024shot')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('neulab/omnitab-large-1024shot')\\n# Create table with animals and their characteristics\\nanimals = [\\\"Animal 1\\\", \\\"Animal 2\\\", \\\"Animal 3\\\"]\\ncharacteristics = [\\\"Characteristic 1\\\", \\\"Characteristic 2\\\", \\\"Characteristic 3\\\"]\\ntable = pandas.DataFrame.from_dict(animals.tolist() + characteristics.tolist(), columns=['animal', 'characteristic'])\\n# Formulate question\\nquestion = \\\"What animal has the characteristic of being the largest?\\\"\\n# Use model to retrieve answer\\ninputs = tokenizer(table=table, queries=question, return_tensors='pt')\\noutputs = model.generate(**inputs)\\nanswer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\\nprint(answer)\"", "answer_id": "nPVWzUkZhQZUdncyAaJ4zP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 130, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: pipeline('table-question-answering', model='google/tapas-medium-finetuned-wtq')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, which includes the pipeline function.\\n2. Create a table-question-answering pipeline using the 'google/tapas-medium-finetuned-wtq' model. This is a model specifically designed for answering questions based on table data.\\n3. Prepare the table structure and question answers to be used in the quiz.\\n4. Use the created pipeline to answer the questions based on the table data provided.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('table-question-answering', model='google/tapas-medium-finetuned-wtq')\\ntable_data = [ ... ] # Insert table data\\nquestions = [ ... ] # Insert questions\\nresult = qa_pipeline(table=table_data, queries=questions)\\n\"", "answer_id": "naTXuDCvGSyx9TRmunZJGm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 131, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')\\n<<>>: Transformers\\n<<>>:1. Import the necessary classes from the transformers package, which includes TapasForQuestionAnswering for the table question answering model.\\n2. Use the from_pretrained method of the TapasForQuestionAnswering class to load the pre-trained model 'google/tapas-base-finetuned-wtq'. This model is designed to answer questions based on structured data, such as tables, and it can be used to efficiently extract key information from tables used in a financial company.\\n3. Define the table and question as input parameters for the model.\\n4. The model can then be used to analyze the table and answer the question based on the information provided in the table.\\n<<>>: from transformers import TapasTokenizer, TapasForQuestionAnswering\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')\\ntokenizer = TapasTokenizer.from_pretrained('google/tapas-base-finetuned-wtq')\\ntable = 'Your table data here'\\nquery = 'Your question about the table data'\\ninputs = tokenizer(table, query, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "J5Paszec2nSRzRHmsCjBXN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 132, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\n<<>>: PyTorch Transformers\\n<<>>: 1. Import the necessary libraries, which are 'BartForConditionalGeneration' and 'TapexTokenizer' from transformers, and 'pandas' for working with the data.\\n2. Load the pre-trained model 'microsoft/tapex-large-finetuned-wikisql' using the 'BartForConditionalGeneration.from_pretrained()' function.\\n3. Create a pandas DataFrame from your dataset containing the shop names, drinks, and prices.\\n4. Prepare the query, which in this case is \\\"Which shops sell hot chocolate and what are their prices?\\\".\\n5. Use the tokenizer to encode the table and query into the required input format for the model.\\n6. Use the model to generate an answer to the query based on the table provided.\\n7. Decode the output to get a human-readable answer.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\ndata = {\\\"Shop\\\": [\\\"Cafe A\\\", \\\"Cafe B\\\", \\\"Cafe C\\\", \\\"Cafe D\\\"], \\\"Drink\\\": [\\\"Coffee\\\", \\\"Tea\\\", \\\"Hot Chocolate\\\", \\\"Hot Chocolate\\\"], \\\"Price\\\": [\\\"3.00\\\", \\\"2.50\\\", \\\"4.50\\\", \\\"3.75\\\"]}\\ntable = pd.DataFrame.from_dict(data)\\nquery = \\\"Which shops sell hot chocolate and what are their prices?\\\"\\nencoding = tokenizer(table=table, query=query, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nanswer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\\n\"", "answer_id": "6wgj887gXYExYX5ATRQ4cp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 133, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: pipeline('table-question-answering', model='google/tapas-large-finetuned-wikisql-supervised')\\n<<>>: Transformers\\n<<>>: 1. Import the required library 'pipeline' from transformers.\\n2. Create a table-question-answering pipeline using the 'google/tapas-large-finetuned-wikisql-supervised' model.\\n3. Provide the survey data in tabular format, with each question as a column and possible answers as separate rows. For example, if a respondent can choose multiple answers, each of those answers should be placed in a separate column.\\n4. Use the created pipeline to query the table by providing the table and query as arguments. The query should ask for the number of respondents who have given a specific answer for a particular question in the survey.\\n5. The result will be the count of respondents who have chosen the specified answer for the question.\\n<<>>: from transformers import pipeline\\ntable_qa = pipeline('table-question-answering', model='google/tapas-large-finetuned-wikisql-supervised')\\nsurvey_table = \\\"\\\"\\\"\\nQuestion Answer1 Answer2 ...\\n\\\"\\\"\\\"\\n# Prepare your survey table as a string/list here\\ntable_data = [ ... ] # List of dictionaries, each containing question and possible answers\\nquery = \\\"SELECT COUNT(*) FROM survey WHERE question = 'question_here' AND answer = 'answer_here'\\\"\\nresult = table_qa(table=table_data, query=query)\\nanswer_count = result['answer'][0]['count']\"", "answer_id": "jNrnaXfsLmVF7sU2oWxdSp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 134, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('dslim/bert-base-NER')\\n<<>>: Transformers\\n<<>>:1. Import AutoModelForTokenClassification and AutoTokenizer from the transformers library provided by Hugging Face.\\n2. Load the pre-trained BERT model specialized in Named Entity Recognition (NER) using 'dslim/bert-base-NER'.\\n3. Load the corresponding tokenizer for this model.\\n4. Use the tokenizer to process the legal document text and obtain input tensors.\\n5. Pass the input tensors to the NER model to extract named entities from the text, in this case, focusing on the non-compete clause.\\n6. Parse the document to extract additional information about the non-compete clause.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nnlp = pipeline('ner', model=AutoModelForTokenClassification.from_pretrained('dslim/bert-base-NER'), tokenizer=AutoTokenizer.from_pretrained('dslim/bert-base-NER'))\\ntext = \\\"The data protection provisions set forth in this agreement shall be in effect for a period of 2 years after the termination of services. The non-compete clause states that the service provider is prohibited from providing similar services to any competitor within a 50-mile radius and during the 1-year period following termination of services.\\\"\\ninputs = nlp(text)\\nner_entity = inputs[0]['entity']\\n\"", "answer_id": "noo3GG48bDSYYgchctAaUt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 135, "text": "The game was played on February 7, 2016.", "answer_id": "gCmK4s9j8UwHReerWsVnhq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 136, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: CrossEncoder('cross-encoder/nli-deberta-v3-small')\\n<<>>: Transformers\\n<<>>:1. Import the necessary libraries, which are 'CrossEncoder' from sentence_transformers and 'transformers' library provided by Hugging Face.\\n2. Initialize the model using the provided model name 'cross-encoder/nli-deberta-v3-small'. This model is trained specifically for natural language inference tasks and zero-shot classification.\\n3. Use the model to predict the relationship between two sentences by providing them as input. The model will return scores for the three possible relationships: contradiction, entailment, and neutral.\\n4. Interpret the scores to determine the relationship between the two sentences.\\n<<>>: from sentence_transformers import CrossEncoder\\nmodel = CrossEncoder('cross-encoder/nli-deberta-v3-small')\\nsentence1 = \\\"A man is eating pizza.\\\"\\nsentence2 = \\\"A person is consuming food.\\\"\\nscores = model.predict([(sentence1, sentence2)])\\nrelationship_labels = [\\\"contradiction\\\", \\\"entailment\\\", \\\"neutral\\\"]\\nfor i, label in enumerate(relationship_labels):\\n if scores[i] == max(scores):\\n print(f\\\"{sentence1} {label} {sentence2}\\\")\\n\"", "answer_id": "kUKKCU3DXuX8SChTX2pAfe", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 137, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: PegasusForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required classes from the transformers library: PegasusForConditionalGeneration and PegasusTokenizer.\\n2. Load the pre-trained Pegasus model specifically designed for Chinese text summarization by using the from_pretrained method and providing the model name 'IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese'.\\n3. Prepare the input text, which is the research summary in English, and tokenize it using the loaded tokenizer.\\n4. Generate a summary in Chinese using the loaded model and the tokenized input.\\n5. Decode the generated summary and return it as a readable string.\\n<<>>: from transformers import PegasusForConditionalGeneration, PegasusTokenizer\\nmodel = PegasusForConditionalGeneration.from_pretrained('IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese')\\ntokenizer = PegasusTokenizer.from_pretrained('IDEA-CCNL/Randeng-Pegasus-238M-Summary-Chinese')\\ntext = 'Your English research summary here...'\\ninputs = tokenizer(text, max_length=1024, return_tensors='pt')\\nsummary_ids = model.generate(inputs['input_ids'])\\ntranslation = tokenizer.batch_decode(summary_ids, skip_special_tokens=True)[0]\"", "answer_id": "jasJdMQxKbu57Q2i9dMUDh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 138, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-newsroom')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary 'pipeline' function from the 'transformers' library.\\n2. Create a summarization pipeline by specifying the task as 'summarization' and loading the pre-trained model 'google/pegasus-newsroom'. This model is specifically designed for summarization tasks and has performed well on multiple benchmarks.\\n3. To create a summarized version of the given text, pass the text as an input to the summarization pipeline.\\n4. The pipeline will return a summarized version of the input text, which can be used within the app to provide users with a concise overview of long articles.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-newsroom')\\narticle_text = \\\"Long article text here...\\\"\\nsummary = summarizer(article_text, max_length=100, min_length=25, do_sample=False)\\n\"", "answer_id": "ZnggkZaU8QCjt6s7gUR4tv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 139, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='facebook/bart-large-cnn')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'facebook/bart-large-cnn'.\\n3. The loaded model will be used for summarization, which is the task of creating a concise description of a given text.\\n4. The model will automatically generate a summary of the given international news article, providing a concise representation of the content.\\n<<>>:from transformers import pipeline\\nsummarizer = pipeline('summarization', model='facebook/bart-large-cnn')\\narticle = \\\"International news article here...\\\"\\nsummary = summarizer(article, max_length=100, min_length=25, do_sample=False)\\nprint(summary[0]['summary_text'])\"", "answer_id": "6mCayrjGZrDg9Pm3PidiA6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 140, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='Zixtrauce/BaekBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Zixtrauce/BaekBot', a model designed for text generation and conversation tasks.\\n3. The loaded model can be used to process user inputs and generate appropriate responses for controlling the home appliances in a smart home system.\\n4. To interact with the model, you can format your input as an input command to the model, such as \\\"generate a conversation with me about the weather\\\".\\n5. The model will generate a response based on the input command, which can be further processed to control the home appliances.\\n<<>>: from transformers import pipeline\\nconversational_pipeline = pipeline('conversational', model='Zixtrauce/BaekBot')\\ninput_text = \\\"Weather today is beautiful. How about turning on the fan?\\\"\\nresponse = conversational_pipeline(input_text)[\\\"generated_text\\\"]\\n\"", "answer_id": "AKGp29Pm3qM7WsnQ2tFbAf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 141, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='bigscience/test-bloomd-6b3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a text-generation model, which is capable of generating text based on a given input.\\n3. We specify the model 'bigscience/test-bloomd-6b3' to be loaded. This is a large-scale multilingual text generation model that can generate text based on a given input prompt.\\n4. The created generator can be used to generate short stories based on given prompts for elementary school students.\\n<<>>: from transformers import pipeline\\ngenerator = pipeline('text-generation', model='bigscience/test-bloomd-6b3')\\ngenerated_story = generator(\\\"Once upon a time in a magical forest,\\\", max_length=100)\"", "answer_id": "ZMCumkYU8hSP5F4CrRDddE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 142, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-generation', model='PygmalionAI/pygmalion-2.7b')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text generation model that specializes in conversational tasks.\\n3. Specify the model 'PygmalionAI/pygmalion-2.7b' to be loaded, which is a model that has been trained to hold conversations and provide responses based on input messages.\\n4. Use the created model to generate a conversation with your friend, asking them about their conference experience by providing an input message.\\n<<>>: from transformers import pipeline\\nconversational_ai = pipeline('text-generation', model='PygmalionAI/pygmalion-2.7b')\\ninput_message = 'Tell me about your conference experience.'\\nresponse = conversational_ai(input_message)[0]['generated_text']\\nprint(response)\\n\"", "answer_id": "jyW2JEcujJUvWPeuQGLUoj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 143, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('Salesforce/codegen-2B-multi')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face. This includes AutoModelForCausalLM for the text generation model and AutoTokenizer for tokenizing the text.\\n2. Load the pre-trained model 'Salesforce/codegen-2B-multi', which is specifically designed for program synthesis based on natural language descriptions.\\n3. Tokenize the given text description, which should be in natural language form, using the AutoTokenizer.\\n4. Pass the tokenized input to the text generation model and generate corresponding code snippets.\\n5. Decode the generated output to obtain the code snippets in a human-readable format.\\n<<>>: from transformers import AutoModelForCausalLM, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-2B-multi')\\nmodel = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-2B-multi')\\ntext = \\\"Create a function that returns the sum of two numbers.\\\"\\ninput_ids = tokenizer.encode(text, return_tensors='pt')\\ngenerated_ids = model.generate(input_ids, max_length=128)\\ncode_snippets = tokenizer.decode(generated_ids[0], skip_special_tokens=True)\\n\"", "answer_id": "6BQ4jSr5gREiCnsKfoxeVE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 144, "text": "The high volatility, lack of regulatory oversight, security risks, and environmental impact of mining cryptocurrencies make them risky investments. Investors should carefully consider these factors before investing in digital currencies.\"", "answer_id": "aUxfBXSjad6yb8rbN2Hx8D", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 145, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/textless_sm_en_fr')\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary functions and packages, including load_model_ensemble_and_task_from_hf_hub from fairseq.checkpoint_utils, and TTSHubInterface from fairseq.models.textless_sm_en_fr.\\n2. Load the model for the Textless SM-EN-FR model from the Hugging Face model hub using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Create a generator and configure it with the task and model.\\n4. Call the get_model_input method to prepare the input for the model.\\n5. Call the get_prediction method to generate the French audio file for the welcome message.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.textless_sm_en_fr import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/textless_sm_en_fr', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = \\\"Welcome to our hotel, we hope you enjoy your stay.\\\"\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "VpuCUtBwhzBdUEYPvL6F8t", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 146, "text": "Wie kann ich die Lage von Parks in M\u00fcnchen erfahren?\\n\"", "answer_id": "9KBkPShnETqQXjKwnXehPv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 147, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: pipeline('summarization', model='it5/it5-base-news-summarization')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Initialize the summarization pipeline with the 'it5/it5-base-news-summarization' model, which is specifically designed for news summarization tasks in the Italian language.\\n3. Use the initialized pipeline to generate a summarized version of the provided text, converting a closed-ended question into an open-ended one.\\n4. The output can be used to obtain a summarized answer to the input question while preserving the natural language style.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='it5/it5-base-news-summarization')\\nconversion = \\\"Cosa sono le fonti energetiche rinnovabili?\\\">>summarized_conversion = summarizer(conversion, max_length=75, min_length=25, do_sample=False)\\nsummarized_conversion = summarized_conversion[0]['summary_text']\\n\"", "answer_id": "CDQLfjs3TzPqvdqAR7tuYx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 148, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilbert-base-uncased')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Use the pipeline function to create a fill-mask model that can complete sentences with missing words.\\n3. Specify the 'distilbert-base-uncased' model for this task. It is a smaller and faster version of BERT that is uncased and does not use padding.\\n4. The created unmasker model can be used to fill in the gaps or complete phrases or sentences for you, providing a useful tool for writers.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilbert-base-uncased')\\nfilled_sentence = unmasker(\\\"In the future, humans will live in [MASK] with renewable energy sources.\\\")\"", "answer_id": "5RFZ4B96xbTsgYGaT3Sqwt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 149, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='albert-base-v2')\\n<<>>: Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create an instance of the fill-mask pipeline using the 'albert-base-v2' model. This model is designed for masked language modeling tasks.\\n3. Use the fill-mask pipeline to predict the next word in the incomplete phrase \\\"The dog jumped over the\\\" . The model will fill in the masked word based on the context provided.\\n4. The result will be a completed phrase that can be used as a basis for further writing.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='albert-base-v2')\\nphrase = \\\"The dog jumped over the >\\\"\\npredictions = unmasker(phrase)\\ntop_prediction = predictions[0]['token_str']\\nfilled_phrase = phrase.replace('', top_prediction)\"", "answer_id": "GqsZ3RNx27wrVookZN3Z88", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 151, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required library, which is 'SentenceTransformer' from the sentence_transformers package.\\n2. Create an instance of the SentenceTransformer class using the model 'sentence-transformers/all-MiniLM-L12-v2'.\\n3. Provide a list of restaurant reviews as input to the model. The model will return sentence embeddings for each review.\\n4. Calculate similarity scores between the embeddings by calculating the cosine similarity between their vectors.\\n5. The resulting similarity scores can be used to identify similar reviews.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nreviews = ['The restaurant had amazing food.', 'The food was fantastic.', 'The food was mediocre.', 'I didn\\'t like the restaurant.']\\nmodel = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\nembeddings = model.encode(reviews)\\nsimilarity_scores = cosine_similarity(embeddings)\\nprint(similarity_scores)\"", "answer_id": "7pRPGnS7HFLciqZksvxokK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 152, "text": "<<>>: Audio Text-to-Speech\\n<<>>: TextToSpeech('NeuML/ljspeech-jets-onnx')\\n<<>>: ONNX\\n<<>>:1. We first import the necessary classes from the txtai package, including the TextToSpeech class for converting text into spoken instructions.\\n2. We then use the TextToSpeech method with the model 'NeuML/ljspeech-jets-onnx' to generate speech from the given text. This model has been trained to convert text into natural-sounding speech.\\n3. The generated speech can be played back using an audio player or streamed directly to the visually impaired users' devices.\\n4. This way, the text-to-speech model can provide spoken instructions and assist the visually impaired individuals in their daily tasks and activities.\\n### Code: from txtai import TextToSpeech\\ntts = TextToSpeech('NeuML/ljspeech-jets-onnx')\\nspeech = tts('Hello, how are you?')\\n\"", "answer_id": "5oLQd5o5YEcqzhuWG8zrdH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 153, "text": "<<>>: Audio Text-to-Speech\\n<<>>: ./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n<<>>: ESPnet\\n<<>>:1. Install the ESPnet framework by running 'pip install espnet2' in the terminal.\\n2. Download the pre-trained 'mio/amadeus' model using the 'run.sh' script provided by ESPnet. The script can be found in the 'share' folder of the ESPnet installation.\\n3. The 'mio/amadeus' model is a Text-to-Speech model trained using the amadeus recipe in ESPnet. It can synthesize speech from text in multiple languages, including English, Italian, German, French, and Spanish.\\n4. In your application, pass the English sentences to the model, and it will generate the corresponding speech audio that users can listen to.\\n<<>>: # Install ESPnet framework\\n!pip install espnet2\\n# Download and load the mio/amadeus model\\n!./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n# Use the model to generate speech from text\\ntext = \\\"Hello, how are you?\\\"\\nmodel.synthesize(text)\\n\"", "answer_id": "NSgMZvqzkASSvrQERbCdu3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 154, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10')\\n<<>>: Fairseq\\n<<>>: 1. We import the necessary libraries and functions, including load_model_ensemble_and_task_from_hf_hub from the fairseq package.\\n2. We load the model 'facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10' using the load_model_ensemble_and_task_from_hf_hub function. This model is a multilingual text-to-speech model capable of generating speech in English, French, Spanish, and Italian languages.\\n3. We can then use the model to generate speech from a given text in any of the supported languages.\\n<<>>: from fairseq import load_model_ensemble_and_task_from_hf_hub\\nmodel, _ = load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10')\\ntext = \\\"Bonjour, je m'appelle Dan.\\\"\\nwav = model.generate_speech(text)\\n\"", "answer_id": "iXph6XDCyMgKx9YbFmJ3fY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 155, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-fr-cv7_css10')\\n<<>>: Fairseq\\n<<>>: 1. Import the required libraries and functions such as load_model_ensemble_and_task_from_hf_hub from fairseq.checkpoint_utils and TTSHubInterface from fairseq.models.text_to_speech.hub_interface.\\n2. Load the pre-trained French text-to-speech model 'facebook/tts_transformer-fr-cv7_css10' using the load_model_ensemble_and_task_from_hf_hub() function.\\n3. Prepare the input text for the model by providing the text to translate to speech.\\n4. Generate the audio waveform and sample rate for the French text by calling the TTSHubInterface.get_model_input, TTSHubInterface.get_prediction, and ipd.Audio functions.\\n5. The resulting audio waveform and sample rate can be saved as .wav and 16000Hz, respectively.\\n<<>>: import IPython.display as ipd\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-fr-cv7_css10', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ntext = \\\"Bonjour, ceci est un test.\\\"\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "h7rxQFZECBeHaoYk3aRNZQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 156, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-medium')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries from the transformers and datasets packages. This includes WhisperProcessor and WhisperForConditionalGeneration for automatic speech recognition.\\n2. Load the pre-trained model 'openai/whisper-medium' using the from_pretrained method of the WhisperForConditionalGeneration class. This model has been trained to recognize and transcribe speech to text.\\n3. Use the WhisperProcessor to process the audio file before passing it to the model. The processor will take care of sampling the audio at the appropriate rate and converting it into input features that the model can use.\\n4. Pass the processed audio to the model and generate the transcription. The model will return predicted IDs that represent the transcribed text.\\n5. Use the WhisperProcessor's batch_decode method to decode the predicted IDs into human-readable text.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-medium')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-medium')\\nmodel.config.forced_decoder_ids = None\\nds = load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation')\\nsample = ds[0]['audio']\\ninput_features = processor(sample['array'], sampling_rate=sample['sampling_rate'], return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\\n\"", "answer_id": "LCpVR9jexF5uwGHHmBoZaJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 157, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2Model.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-english')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including SpeechRecognitionModel from huggingsound, and other required libraries like torch, librosa, datasets, and transformers.\\n2. Load the pre-trained 'jonatasgrosman/wav2vec2-large-xlsr-53-english' model using the SpeechRecognitionModel.from_pretrained() method. This model is designed for the English language and is based on the Wav2Vec2 architecture.\\n3. Define the audio file paths where the speech is stored.\\n4. Use the transcribe() method of the model to generate transcriptions based on the provided audio paths.\\n<<>>: from huggingsound import SpeechRecognitionModel\\nimport torch\\nimport librosa\\nfrom datasets import load_dataset\\nfrom transformers import Wav2Vec2Model\\nmodel = SpeechRecognitionModel.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-english')\\naudio_paths = ['/path/to/file.mp3', '/path/to/another_file.wav']\\ntranscriptions = model.transcribe(audio_paths)\"", "answer_id": "4gZGiBc3XpSc5uz8Yru2vj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 158, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library. This includes WhisperProcessor for pre-processing audio data and WhisperForConditionalGeneration for performing automatic speech recognition.\\n2. Load the pre-trained 'openai/whisper-base' model for automatic speech recognition using the from_pretrained method of WhisperForConditionalGeneration.\\n3. Load the audio data from the tour guide's speech, ideally in a format like WAV or MP3.\\n4. Pre-process the audio data using the WhisperProcessor instance.\\n5. Perform automatic speech recognition on the pre-processed audio data with the WhisperForConditionalGeneration model.\\n6. Decode the transcription units into text format.\\n7. Use a separate program or service to translate the transcribed text into sign language.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-base')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\naudio_file_path = 'path/to/audio_file.wav'\\nsample = {'array': audio_file_path, 'sampling_rate': 16000}\\n# Pre-process the audio and perform automatic speech recognition\\ninput_features = processor(sample['array'], sampling_rate=sample['sampling_rate'], return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\\n# Use a separate program or service to translate the transcribed text into sign language\\n\"", "answer_id": "XU4TuXuFbST9pSiuup3vC3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 159, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/xmn-transformer-hk-css10-300m')\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries, including Fairseq, Hugging Face Hub, and torchaudio.\\n2. Load the pre-trained model 'facebook/xmn-transformer-hk-css10-300m' capable of translating English text to Hokkien using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Use the S2THubInterface to generate a sentence from English to Hokkien using the loaded model and a provided sentence.\\n4. Once translated, the Hokkien sentence can be used to facilitate communication between the English speaker and the Hokkien speaker.\\n<<>>: from fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nimport torchaudio\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/xmn-transformer-hk-css10-300m')\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\ngenerator = task.build_generator([model], cfg)\\n# English sentence to be translated\\nenglish_sentence = \\\"We are very happy to meet you.\\\"\\n# Translate to Hokkien\\nhokkien_sentence = S2THubInterface.translate_text_to_model_output(task, english_sentence)\\n# Save translated sentence\\nwith open(\\\"output_sentence.wav\\\", \\\"wb\\\") as f:\\n torchaudio.save(f, hokkien_sentence.unsqueeze(0).cpu(), 16000)\\n\"", "answer_id": "87xeMy5XysyvCnQfXmQZvA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 160, "text": "<<>>: Audio Audio Classification\\n<<>>: foreign_class(source='speechbrain/emotion-recognition-wav2vec2-IEMOCAP', pymodule_file='custom_interface.py', classname='CustomEncoderWav2vec2Classifier')\\n<<>>: SpeechBrain\\n<<>>:1. Install the speechbrain library using pip.\\n2. Import foreign_class from speechbrain.pretrained.interfaces.\\n3. Create a classifier object by calling foreign_class with the source, pymodule_file, and classname values.\\n4. Pass the audio file to the classifier object, and it will return the emotion detected in the audio along with the probability score.\\n5. Print the detected emotion and its score.\\n<<>>: from speechbrain.pretrained.interfaces import foreign_class\\nclassifier = foreign_class(source='speechbrain/emotion-recognition-wav2vec2-IEMOCAP', pymodule_file='custom_interface.py', classname='CustomEncoderWav2vec2Classifier')\\naudio_file = 'path/to/your_audio_file.wav'\\nout_prob, score, index, emotion = classifier.classify_file(audio_file)\\nprint(emotion)\\n\"", "answer_id": "YfsVSQUEikXoE96bAsi4so", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 161, "text": "<<>>: Audio Classification\\n<<>>: AutoModelForAudioXVector.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include the AutoModelForAudioXVector and Wav2Vec2Processor from the transformers library.\\n2. Load the pre-trained model 'anton-l/wav2vec2-base-superb-sv' using the AutoModelForAudioXVector.from_pretrained() method. This model is designed for speaker verification and classification tasks.\\n3. Load the processor using the Wav2Vec2Processor.from_pretrained() method.\\n4. Process the audio input using the processor's __call__() method, which will convert the raw audio signal into a format suitable for the model.\\n5. Pass the processed input to the model's forward() method to obtain the speaker verification or classification results.\\n6. The output can be used to determine which speaker the audio segment belongs to.\\n<<>>: from transformers import AutoModelForAudioXVector, Wav2Vec2Processor\\nmodel = AutoModelForAudioXVector.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\nprocessor = Wav2Vec2Processor.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\ninput_values = processor(audio_data, return_tensors=\\\"pt\\\", padding='longest').input_values\\nlogits = model(input_values).logits\\npredicted_label = logits.argmax(-1).item()\\nspeaker_id = processor.id2label[predicted_label]\\n\"", "answer_id": "K2m9h2haotUhfKyk5PvCBQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 162, "text": "<<>>: Audio Audio Classification\\n<<>>: AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. To use the voice recognition model, you should first import the necessary libraries, such as the AutoModelForAudioClassification class from the transformers package.\\n2. Load the pre-trained model 'MIT/ast-finetuned-speech-commands-v2' by invoking the from_pretrained method on the AutoModelForAudioClassification class.\\n3. You can then process customer voices and use the model to classify them into different categories based on their speech commands.\\n4. This model has been fine-tuned on a dataset of speech commands and should provide good performance for recognizing customer voices.\\n<<>>: from transformers import AutoModelForAudioClassification\\nmodel = AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n# Process customer voice and classify using the model\\n\"", "answer_id": "YXgEN4CMu57Hwr3Yg4gEYx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 163, "text": "<<>>: Audio Classification\\n<<>>: AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes and functions from the transformers library.\\n2. Load the pretrained 'MIT/ast-finetuned-speech-commands-v2' model using the AutoModelForAudioClassification class. This model is fine-tuned for audio classification tasks and can identify spoken commands.\\n3. Load the command in the provided audio clip using the `librosa` library, and then process the audio data to create an input tensor.\\n4. Finally, use the `model(audio_tensor)` method to classify the spoken command in the audio clip.\\n<<>>: from transformers import AutoModelForAudioClassification, AutoTokenizer\\nimport librosa\\nmodel = AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\ntokenizer = AutoTokenizer.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\naudio_clip_path = \\\"audio_clip.wav\\\"\\n# Load the audio clip\\naudio, _ = librosa.load(audio_clip_path, sr=16000)\\n# Process the audio\\ninput_tensor = tokenizer(audio, return_tensors=\\\"pt\\\").input\\n# Classify the command\\nresult = model(input_tensor)\\n# Extract the command from the result\\npredicted_label = result.logits.argmax(axis=1).item()\\n\"", "answer_id": "UxGaA4suRv9CFExzJyScg3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 164, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: pyannote.audio\\n<<>>:1. Import the Pipeline class from the pyannote.audio library.\\n2. Load the pre-trained speaker diarization model 'pyannote/speaker-diarization@2.1' using the Pipeline.from_pretrained method. Replace 'ACCESS_TOKEN_GOES_HERE' with your access token.\\n3. Create an instance of the VoiceActivityDetection pipeline using the pre-trained model.\\n4. Process the audio file ('audio.wav') from the last team meeting, setting the desired hyperparameters for the pipeline, such as 'onset', 'offset', 'min_duration_on', and 'min_duration_off'.\\n5. Run the voice activity detection pipeline on the audio file and generate a speaker diarization result.\\n6. Save the diarization result to an RTTM (Rich Transcription Time Marked) format file for use in the summary video.\\n<<>>: from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\ndiarization = pipeline('audio.wav', onset=0.5, offset=0.5, min_duration_on=0.0, min_duration_off=0.0)\\nwith open('audio.rttm', 'w') as rttm:\\n diarization.write_rttm(rttm)\\n\"", "answer_id": "hq2sq6PeRNH5oefNpBU85d", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 165, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, including joblib, pandas, and json.\\n2. Load the pre-trained model using the joblib.load() function.\\n3. Load the json file containing the features of the house to use as input.\\n4. Create a pandas DataFrame from the json data, using the data.columns property to set the column names to the feature names expected by the model.\\n5. Rename the DataFrame columns with the prefix 'feat_' to match the naming convention used in the training dataset.\\n6. Use the loaded model to predict the price of the house based on the input features.\\n<<>>: import joblib\\nimport pandas as pd\\nimport json\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.DataFrame({'feat_' + str(col): val for col, val in enumerate(input_data) if col in features})\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "mEBaoA66Y8ndDdM5VE8RP6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 166, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We first import the necessary libraries, including joblib for loading the trained model, and pandas for handling the data.\\n2. We then load the pre-trained model 'model.joblib' using joblib.load.\\n3. Next, we load the feature configuration file 'config.json' to get the list of features the model expects in the input data.\\n4. We read the housing dataset 'data.csv' with pandas and select only the required features from the dataset.\\n5. Rename the columns of the data to match the format the model expects.\\n6. We use the model to make predictions on the preprocessed data to estimate US housing prices.\\n<<>>: import joblib\\nimport pandas as pd\\nimport json\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "7hKebBFNCQgUgcYGnBFYJE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 167, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import joblib, pandas, and json libraries to load the model, process data, and read the configuration file.\\n2. Load the pre-trained model using joblib.load and the provided model file path.\\n3. Load the configuration file containing the features required for making predictions.\\n4. Read the data (e.g., in CSV format) containing vehicle information and extract the relevant features.\\n5. Use the loaded model to predict carbon emissions for different configurations of vehicles based on the extracted features.\\n6. The predictions can be used to analyze and promote more sustainable vehicle configurations.\\n<<>>: import joblib\\nimport pandas as pd\\nimport json\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "45aey98fGEoeJz7SJsoDoi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 168, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, which are 'joblib' and 'pandas'.\\n2. Load the pre-trained model using the 'joblib.load' function, which in this case is 'model.joblib'.\\n3. The loaded model is trained for multi-class classification of air pollution levels using tabular data.\\n4. Load your data into a pandas DataFrame and preprocess it based on the model's requirements.\\n5. Use the trained model to predict the air pollution levels for the given data.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('path_to_model/model.joblib')\\n# Replace 'path_to_model' with the actual path to the saved model\\ndata = pd.read_csv('path_to_data/data.csv')\\n# Replace 'path_to_data' with the actual path to the dataset\\npredictions = model.predict(data)\\n\"", "answer_id": "YgrmvqwdmhJ7tTrLsbaHmf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 169, "text": "<<>>: Reinforcement Learning\\n<<>>: AutoModel.from_pretrained('edbeeching/decision-transformer-gym-walker2d-expert')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'AutoModel' from transformers.\\n2. Use the 'from_pretrained' method of 'AutoModel' to load the pre-trained model 'edbeeching/decision-transformer-gym-walker2d-expert', which is trained on expert trajectories sampled from the Gym Walker2d environment.\\n3. This model can then be used to predict actions to be taken by a prosthetic leg in order to improve walking.\\n4. Integrate the model with the prosthetic leg system, allowing it to learn and improve its walking algorithm based on the decision transformer's predictions.\\n5. Over time, the prosthetic leg can utilize the model to learn and adapt its movements in order to walk more efficiently.\\n### Code: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('edbeeching/decision-transformer-gym-walker2d-expert')\"", "answer_id": "2X6raxq5g7GE8vHTbCyxj3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 170, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/ppo-PongNoFrameskip-v4',filename='{MODEL FILENAME}.zip',)\\n<<>>: Stable-Baselines3\\n<<>>:1. First, install the required libraries including RL Zoo, Stable-Baselines3 (SB3), and SB3 Contrib.\\n2. Use the load_from_hub() function with the appropriate repo_id (sb3/ppo-PongNoFrameskip-v4) and file name to download a pre-trained PPO model that can play the Pong No Frameskip-v4 game.\\n3. Optionally, if you want to train the model further, use the provided train.py script with the pretrained model and the Pong No Frameskip-v4 environment.\\n4. Once trained, you can deploy the model as a bot to play the game with exceptional skill.\\n<<>>: from rlzoo3.load_from_hub import load_from_hub\\nfrom stable_baselines3 import PPO\\nfrom stable_baselines3.common.env_util import make_atari_env\\nmodel_filename = \\\"path/to/save/model.zip\\\"\\nmodel = load_from_hub(repo_id=\\\"sb3/ppo-PongNoFrameskip-v4\\\", filename=model_filename)\\nenv = make_atari_env(\\\"PongNoFrameskip-v4\\\", n_envs=1)\\n\"", "answer_id": "ZDznn9sniHibPz65DgwZHh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 171, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/dqn-MountainCar-v0',filename='{MODEL FILENAME}.zip',)\\n<<>>: Stable-Baselines3\\n<<>>:1. First, you need to install the required packages, which are rl_zoo3, stable-baselines3, and stable-baselines3-contrib.\\n2. Then, use the load_from_hub function from the rl_zoo3 package to load the pre-trained DQN model 'sb3/dqn-MountainCar-v0'.\\n3. You can train this model further on your game environment data to improve the AI agents' performance and enhance the gameplay experience.\\n4. Finally, use the trained model to make decisions in your game based on the agent's observations and achieve the desired gameplay outcomes.\\n<<>>: from rl_zoo3 import load_from_hub\\nmodel = load_from_hub(repo_id='sb3/dqn-MountainCar-v0', filename='{MODEL FILENAME}.zip',)\\n\"", "answer_id": "QgC796uu22tWBQpLxjmShp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 172, "text": "<<>>: Reinforcement Learning\\n<<>>: mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n<<>>: ML-Agents\\n<<>>:1. Install the necessary Python packages, including unity-ml-agents and deep-reinforcement-learning.\\n2. Utilize the Unity ML-Agents Library to create a soccer playing agent by loading the pre-trained model 'Raiden-1001/poca-Soccerv7.1' from the Hugging Face model hub.\\n3. Create a 2v2 environment using the Unity ML-Agents Library to simulate a soccer match.\\n4. Set up the environment with obstacles and other conditions that mimic a real soccer match.\\n5. Write a Python script that controls the agent based on user inputs and makes decisions based on the agent's current observations.\\n6. Train the agent using the Unity ML-Agents Library's built-in training functionality.\\n7. Deploy the trained agent to interact with the 2v2 environment and outperform its opponents.\\n<<>>: # Install necessary Python packages\\n# Load the pre-trained model\\nmlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n# Create a 2v2 environment\\nmlagents-learn --run-id= --resume\\n# Train the agent\\nmlagents-learn --run-id= --resume --train\\n# Deploy the agent\\nmlagents-learn --run-id= --resume --test\"", "answer_id": "Q459X2GtoKERLPxaqekkhf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 173, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: ZeroshotModel.from_pretrained('DeepPavlov/rubert-base-cased-xnli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the transformers package. This includes the ZeroshotModel for zero-shot classification.\\n2. We then use the from_pretrained method of the ZeroshotModel class to load the pre-trained model 'DeepPavlov/rubert-base-cased-xnli'. This model has been fine-tuned for Russian text and is trained on the XNLI dataset.\\n3. We provide a sequence to analyze and a list of candidate labels representing the possible sentiment and trends in the text.\\n4. The model can then be used to analyze the text and identify the most likely sentiment or trend based on the content of the newspaper.\\n<<>>: from transformers import ZeroshotModel\\nrubert_model = ZeroshotModel.from_pretrained('DeepPavlov/rubert-base-cased-xnli')\\nsequence_to_analyze = \\\"...\\\" # Replace with the actual text from the Russian newspaper\\ncandidate_labels = [\\n \\\"optimistic\\\",\\n \\\"pessimistic\\\",\\n \\\"neutral\\\",\\n]\\nresult = rubert_model.predict_sentiment(sequence_to_analyze, candidate_labels)\\nprint(result)\\n\"", "answer_id": "Qp9yN5CTCjpnbKGXDkYK6Q", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 174, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: Text2ImagePipeline(model='Linaqruf/anything-v3.0')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required Text2ImagePipeline class from the transformers library provided by Hugging Face.\\n2. Instantiate the Text2ImagePipeline with the 'Linaqruf/anything-v3.0' model, which is a model that has been trained to generate images from textual descriptions.\\n3. Provide the textual description as input to the Text2ImagePipeline, and the model will generate an image based on the given description.\\n4. Use the generated image in your PowerPoint presentation.\\n<<>>: from transformers import Text2ImagePipeline\\ntext2image = Text2ImagePipeline(model='Linaqruf/anything-v3.0')\\ndescription = \\\"A beautiful mountain landscape with a clear sky and a river running through it.\\\"\\ngenerated_image = text2image(description)\\n\"", "answer_id": "CsncDdwwv95wsMpnnLiwnk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 175, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('ocr', model='kha-white/manga-ocr-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create an optical character recognition (OCR) pipeline using the 'kha-white/manga-ocr-base' model. This model is specifically designed for reading text from manga images.\\n3. Use the OCR pipeline to process the manga image and extract the text.\\n4. The extracted text can then be used in your manga reader app with translations as needed.\\n<<>>: from transformers import pipeline\\nmanga_ocr_pipeline = pipeline('ocr', model='kha-white/manga-ocr-base')\\nmanga_image_url = 'https://example.com/manga_image.jpg'\\ntext_output = manga_ocr_pipeline(manga_image_url)\\n\"", "answer_id": "42MwyM77AdDy5xvyCvfRVe", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 176, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: Blip2ForConditionalGeneration.from_pretrained('Salesforce/blip2-flan-t5-xxl')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes, BlipProcessor and Blip2ForConditionalGeneration, from the transformers library.\\n2. Load the image from the post.\\n3. Use the BlipProcessor.from_pretrained method to load the 'Salesforce/blip2-flan-t5-xxl' model.\\n4. Use the Blip2ForConditionalGeneration.from_pretrained method to load the 'Salesforce/blip2-flan-t5-xxl' model.\\n5. Create a Blip object with the image and a prompt like \\\"a picture of\\\".\\n6. Generate a caption using the model.generate() method.\\n7. Decode the generated caption using the processor.decode() method.\\n<<>>: from transformers import BlipProcessor, Blip2ForConditionalGeneration\\nfrom PIL import Image\\nprocessor = BlipProcessor.from_pretrained('Salesforce/blip2-flan-t5-xxl')\\nmodel = Blip2ForConditionalGeneration.from_pretrained('Salesforce/blip2-flan-t5-xxl')\\nraw_image = Image.open('image_post.jpg').convert('RGB')\\n# replace 'image_post.jpg' with path to your image\\nquestion = \\\"a picture of\\\"\\nblp = processor(raw_image, question, return_tensors='pt')\\ngenerated_caption = model.generate(blp)\\ncaption = processor.decode(generated_caption[0], skip_special_tokens=True)\\n\"", "answer_id": "h9eSPsvchYv8CBvmtMHwiB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 177, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text-generation', model='microsoft/git-base-coco')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-generation model.\\n3. Specify the model 'microsoft/git-base-coco' to be loaded. This model is designed for image-to-text generation tasks, such as describing the contents of an image.\\n4. The created model can be used to analyze images from the production line and generate a text output describing the contents or status of the image.\\n<<>>: from transformers import pipeline\\nimage_to_text = pipeline('text-generation', model='microsoft/git-base-coco')\\nimage_description = image_to_text(image_path)\\n\"", "answer_id": "nPQBBZ8DQNuiBsS8cWy8XA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 178, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: pipeline('text-to-video', model='camenduru/text2-video-zero')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-to-video model, which is capable of generating video content based on text input.\\n3. Specify the model 'camenduru/text2-video-zero' to be loaded. This model is trained on the OpenAssistant/oasst1 dataset and can generate videos based on text input.\\n4. The scene description from the script can be passed as an input to the created pipeline, which will generate a video based on the given description.\\n5. The generated video can then be used for your filmmaking needs.\\n<<>>: from transformers import pipeline\\ntext2video = pipeline('text-to-video', model='camenduru/text2-video-zero')\\nscene_description = 'A mysterious door opens in a deserted forest.'\\ngenerated_video = text2video(scene_description)\"", "answer_id": "5EoF3AFzRpcsEUj8PcaVqB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 179, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: BaseModel.from_pretrained('duncan93/video')\\n<<>>: Hugging Face\\n<<>>: 1. First, import the necessary classes from the Asteroid package. This includes the BaseModel for loading the pre-trained model, and the ToonConfig for setting up the text-to-video generation parameters.\\n2. Next, load the pre-trained model 'duncan93/video' using the BaseModel.from_pretrained() method.\\n3. Set up the text-to-video generation process by creating a ToonConfig object with the required parameters.\\n4. Finally, use the loaded model and the ToonConfig object to generate the video based on the provided text.\\n<<>>: from Asteroid import BaseModel, ToonConfig\\nmodel = BaseModel.from_pretrained('duncan93/video')\\nconfig = ToonConfig.from_pretrained('duncan93/video')\\ntext = 'This is the brief passage from your book that we want to turn into a video.'\\n# You may replace this with the actual text from your book\\noutput_video = model.generate_video(text, config=config)\\n\"", "answer_id": "TQ2beWZm93EKDKPovtpCaR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 180, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='ivelin/donut-refexp-combined-v1')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Create a visual question answering (VQA) pipeline using the 'ivelin/donut-refexp-combined-v1' model. This model can analyze images and answer questions based on the content of the image.\\n3. Provide the image file and the question as inputs to the VQA pipeline.\\n4. The pipeline will process the image and answer the question based on the visual content and the context of the question.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='ivelin/donut-refexp-combined-v1')\\nimage_path = 'path_to_image'\\nquestion = 'your_question'\\nanswer = vqa(image=image_path, question=question)\\n\"", "answer_id": "D4wm6wffN4jerxURe7X4ua", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 181, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='Bingsu/temp_vilt_vqa', tokenizer='Bingsu/temp_vilt_vqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary pipeline function from the transformers library provided by Hugging Face.\\n2. We then create a visual question answering pipeline using the 'visual-question-answering' task and specifying the model 'Bingsu/temp_vilt_vqa' and tokenizer 'Bingsu/temp_vilt_vqa'. This model has been trained to analyze images and provide answers to questions about them based on visual information.\\n3. The created pipeline can be used to analyze images taken by tourists and answer questions about attractions. This can provide valuable information about various sights and help tourists get the most out of their travels.\\n<<>>: from transformers import pipeline\\nvqa_pipeline = pipeline('visual-question-answering', model='Bingsu/temp_vilt_vqa', tokenizer='Bingsu/temp_vilt_vqa')\\nresult = vqa_pipeline({'image': 'path_to_image', 'question': 'your_question'})\\n\"", "answer_id": "WoJJ8Uh8pAPM6NSmoYi4Yj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 182, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='donut-base-finetuned-docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model.\\n3. Specify the model 'donut-base-finetuned-docvqa' to be loaded. This model is designed for document question answering and is specifically fine-tuned for cash flow-related questions.\\n4. Use the created model to extract information about cash flow from the provided financial documents.\\n<<>>: from transformers import pipeline\\nqa_model = pipeline('question-answering', model='donut-base-finetuned-docvqa')\\nquestion = 'What is the cash flow?'\\nanswer = qa_model(question=question, context=financial_document_text)\"", "answer_id": "GNw36kc5UUhdzbU6jrriJJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 183, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: DetrForSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes, including DetrForSegmentation for image segmentation and the PIL library for working with images.\\n2. Use the DetrForSegmentation.from_pretrained method to load the pre-trained 'facebook/detr-resnet-50-panoptic' model.\\n3. Load the input image and convert it to the required tensors format.\\n4. Use the model to perform image segmentation, which outputs a panoptic segmentation map that separates the image into different regions corresponding to different objects or scenes.\\n5. Save the segmented image as a .png file for further inspection or use in 3D modeling or CAD software.\\n<<>>: from transformers import DetrForSegmentation\\nfrom PIL import Image\\nimage = Image.open('input_image.jpg')\\n# replace 'input_image.jpg' with path to your image\\nmodel = DetrForSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nsegmentation_map = outputs.pred[0]\\nsegmentation_map.save('./segmented_image.png')\\n\"", "answer_id": "MSNVzGKCQdMMdF5AHSU43a", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 184, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-110652')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, such as 'AutoModel' from transformers.\\n2. Load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221116-110652' using the 'AutoModel.from_pretrained()' function. This model is specifically fine-tuned for depth estimation tasks in drone footage.\\n3. You can now use this model to estimate the depth in any given frame of drone video footage. The model will provide depth values as an output, which can be used to create a depth map or 3D reconstruction of the scene.\\n4. The depth map can be used for a variety of applications, such as 3D modeling, analysis, and visualization.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-110652')\\ndepth_map = model(input_frame)\"", "answer_id": "2NnZJgpzPSFAXVt8xYAoTK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 185, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model=LayoutLMForQuestionAnswering.from_pretrained('impira/layoutlm-document-qa', return_dict=True))\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, such as PIL, pytesseract, PyTorch, and transformers.\\n2. Use the pipeline function to create a question answering model, specifying the 'impira/layoutlm-document-qa' model.\\n3. Preprocess the image file containing the customer invoice.\\n4. Use the created model to answer questions related to the total amount, tax, and due date from the image file.\\n5. The model will provide the answers to the specified questions based on the text extracted from the image file.\\n<<>>: from PIL import Image\\nfrom transformers import pipeline, LayoutLMForQuestionAnswering\\nimport pytesseract\\nimport torch\\n# create_qa_pipeline = pipeline('question-answering', model=LayoutLMForQuestionAnswering.from_pretrained('impira/layoutlm-document-qa', return_dict=True))\\n# image_path = 'path/to/invoice_image.png'\\n# questions = ['What is the total amount?', 'What is the tax?', 'When is the due date?']\\n# for question in questions:\\n# image = Image.open(image_path)\\n# text = pytesseract.image_to_string(image)\\n# answer = create_qa_pipeline(question=question, image=image, tokenizer=None)\\n# print(answer)\"", "answer_id": "UB5nMpX3LUzZWZ3R3s37jA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 186, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary libraries, including PIL for image processing, torch for using the model, and DPTForDepthEstimation from transformers.\\n2. We create instances of the DPTImageProcessor and DPTForDepthEstimation model using the 'Intel/dpt-hybrid-midas' pretrained model.\\n3. We load the room image from a file or from a camera feed.\\n4. We preprocess the image using the DPTImageProcessor instance, and then pass the processed image to the DPTForDepthEstimation model.\\n5. The model estimates the depth of the room based on the input image.\\n6. Finally, we extract the depth information from the model output, which can be used for monitoring purposes.\\n<<>>: from PIL import Image\\nimport numpy as np\\nimport torch\\nfrom transformers import DPTImageProcessor, DPTForDepthEstimation\\nprocessor = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\nmodel = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\nroom_image = Image.open('room_image.jpg')\\ninputs = processor(images=room_image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\nprediction = torch.nn.functional.interpolate(predicted_depth.unsqueeze(1), size=room_image.size[::-1], mode='bicubic', align_corners=False)\\noutput = prediction.squeeze().cpu().numpy()\\nformatted = (output * 255 / np.max(output)).astype('uint8')\\ndepth = Image.fromarray(formatted)\"", "answer_id": "UhQ9rbYmvYgdL4RGF6hEd3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 187, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-104421')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary class AutoModel from the transformers package provided by Hugging Face.\\n2. Use the from_pretrained method of the AutoModel class to load the pre-trained depth estimation model 'sayakpaul/glpn-nyu-finetuned-diode-221116-104421'. This model has been fine-tuned on the diode-subset dataset and can estimate depth in a given scene.\\n3. Use the loaded model to process an image and estimate the depth of objects in the scene. This depth estimation can be crucial for an autonomous car to navigate and avoid obstacles.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-104421')\\n# Load an image and preprocess it\\n# image = load_and_preprocess_image('image_path.jpg')\\n# Depth estimation\\n# depth_estimate = model(image)\\n\"", "answer_id": "aMKV3Z2uawvRFqwTBxyqYk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 188, "text": "<<>>: Computer Vision Image Classification\\n<<>>: BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including BeitImageProcessor and BeitForImageClassification from the transformers package, as well as Image from PIL.\\n2. Load the BeitForImageClassification pre-trained model 'microsoft/beit-base-patch16-224', which is capable of image classification tasks.\\n3. Load the image data from a file, or it can be acquired in real-time from the security camera.\\n4. Process the image using the BeitImageProcessor, which resizes and normalizes the image.\\n5. Use the pre-trained model to make predictions on the processed image.\\n6. The model will output a label for the class that it thinks the image belongs to.\\n7. The output label can then be used for further analysis or alert generation if necessary.\\n<<>>: from transformers import BeitImageProcessor, BeitForImageClassification\\nfrom PIL import Image\\nimage = Image.open('security_camera_image.jpg')\\n# replace 'security_camera_image.jpg' with the path to your image\\nprocessor = BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224')\\nmodel = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\"", "answer_id": "KH9xdxQDsLbzqNtSXTj8ht", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 189, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vits8')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries: ViTFeatureExtractor and ViTModel from transformers, Image from PIL, and requests.\\n2. Load the pre-trained model 'facebook/dino-vits8' using the ViTModel.from_pretrained() function.\\n3. Load the feature extractor from the same pre-trained model.\\n4. Using the requests library, open the image URL and convert it to an Image object.\\n5. Use the feature extractor to pre-process the image and convert it into a format suitable for the model.\\n6. Pass the processed image through the model and obtain the output.\\n7. Post-process the output to obtain the final classification result.\\n8. The final classification result can be used to categorize the image into one of the thousand categories.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimport requests\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits8')\\nmodel = ViTModel.from_pretrained('facebook/dino-vits8')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\"", "answer_id": "cgzqqAGsaNRqQK2rTUWUoP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 190, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-drone-detection')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from ultralyticsplus, including YOLO for the object detection model and render_result for visualizing the results.\\n2. We then use the YOLO class to load the pre-trained model 'keremberke/yolov8m-drone-detection'. This model has been trained for drone detection tasks, which is exactly what we need for detecting obstacles in a delivery drone's path.\\n3. We set the model's parameters, such as confidence threshold, IoU threshold, agnostic NMS, and maximum detections.\\n4. We pass the image to the model for prediction, which returns the detected objects and their bounding boxes.\\n5. The drone can use this information to safely navigate and avoid obstacles in its path.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-drone-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path_to_drone_image.jpg'\\n# replace 'path_to_drone_image.jpg' with path to your image\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "VH2FfCaB6QkcYekBs5zWas", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 191, "text": "<<>>: Computer Vision Object Detection\\n<<>>: DeformableDetrForObjectDetection.from_pretrained('SenseTime/deformable-detr')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries: 'DeformableDetrForObjectDetection' from transformers, 'Image' from PIL, and 'requests' to download the image.\\n2. Load the pre-trained deformable-detr model using the 'from_pretrained' method of the DeformableDetrForObjectDetection class.\\n3. Open and preprocess the input image using the 'Image' class from the PIL library.\\n4. Download the image data from the specified URL and pass it through the pre-trained model.\\n5. The model will produce object detections in the image.\\n<<>>: from transformers import DeformableDetrForObjectDetection\\nfrom PIL import Image\\nimport requests\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nmodel = DeformableDetrForObjectDetection.from_pretrained('SenseTime/deformable-detr')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "U97W7kUNfytbkb8xnppRMR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 192, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8n-table-extraction')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary YOLO class to load the pre-trained model for table extraction.\\n2. We then create an instance of the YOLO model using the 'keremberke/yolov8n-table-extraction' identifier, which is trained for table extraction tasks.\\n3. We provide the model with an image containing the scanned document, and set the desired confidence, Intersection over Union (IoU) threshold, and other parameters.\\n4. The model processes the image and returns the tables along with their bounding boxes and labels. These boxes can be used to extract the tables from the original images.\\n<<>>: from ultralyticsplus import YOLO\\nmodel = YOLO('keremberke/yolov8n-table-extraction')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path_to_image.jpg'\\nresults = model.predict(image)\\n\"", "answer_id": "2Mfr9ZBck8tRTCMqLYFGdP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 193, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YolosForObjectDetection.from_pretrained('hustvl/yolos-small')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers and PIL packages. This includes YolosForObjectDetection for the object detection model and Image for processing image data.\\n2. Use the from_pretrained method of the YolosForObjectDetection class to load the pre-trained model 'hustvl/yolos-small'. This model has been trained for object detection tasks and can help detect potential thieves in a surveillance image.\\n3. Load the image data from a file or real-time surveillance camera feed.\\n4. This model can then be used to analyze an image and identify objects that could potentially be shoplifters. The model's predictions can be used to detect and track the suspects for improved surveillance and loss prevention.\\n<<>>: from transformers import YolosForObjectDetection\\nfrom PIL import Image\\nimage = Image.open('surveillance_image.jpg')\\n# replace 'surveillance_image.jpg' with path to your image\\nmodel = YolosForObjectDetection.from_pretrained('hustvl/yolos-small')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\"", "answer_id": "CNSemRSoNrZtbrUt7kcy7J", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 194, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8n-blood-cell-detection')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries: YOLO and render_result from the ultralyticsplus package.\\n2. Load the pre-trained model 'keremberke/yolov8n-blood-cell-detection' using the YOLO class.\\n3. Set the model's configuration parameters, such as confidence threshold, IoU threshold, agnostic Non-Maximum Suppression (NMS), and maximum number of detections.\\n4. Provide the image to be analyzed, either as a local file or a URL.\\n5. Use the model's predict method to detect blood cells in the image.\\n6. Visualize the detected objects using the render_result function.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8n-blood-cell-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'blood_cell_image.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "VBUGNrZBni9fCepxkVDFka", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 195, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-room')\\n<<>>: Hugging Face\\n<<>>:1. First, import the necessary libraries including ControlNetModel from diffusers and StableDiffusionControlNetPipeline from transformers.\\n2. Load the pre-trained ControlNet model 'lllyasviel/sd-controlnet-room' for converting room plan images.\\n3. Load the input image of the room plan and convert it to a suitable format.\\n4. Create a pipeline using the StableDiffusionControlNetPipeline and the loaded ControlNet model.\\n5. Use the pipeline to convert the input image to a better visual representation, such as a rendered image or a video.\\n6. Save the output image or video to a file for further use.\\n<<>>: from diffusers import ControlNetModel\\nfrom transformers import StableDiffusionControlNetPipeline\\nimport torch\\nfrom PIL import Image\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-room', torch_dtype=torch.float16)\\nimage = Image.open('room_plan_image_path.jpg')\\n# replace 'room_plan_image_path.jpg' with path to your room plan image\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\nimage_output = pipe(room_plan_text, image, num_inference_steps=20).images[0]\\nimage_output.save('output_image.png')\\n\"", "answer_id": "FGRiUyURQc2S2egnX6sVJp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 196, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes, such as Swin2SRForConditionalGeneration from the transformers package, and PIL.Image for image manipulation.\\n2. Load the pretrained model 'condef/Swin2SR-lightweight-x2-64' using the Swin2SRForConditionalGeneration.from_pretrained method. This model is designed for lightweight image super-resolution tasks.\\n3. Open and preprocess the low-resolution image using the PIL.Image library.\\n4. Pass the preprocessed image to the model and generate the upscaled image.\\n5. Save the upscaled image to a file for further use.\\n<<>>: from transformers import Swin2SRForConditionalGeneration\\nfrom PIL import Image\\nimage = Image.open('low_resolution_image_path.jpg')\\n# replace 'low_resolution_image_path.jpg' with the path to your low-res image\\nmodel = Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "55jorX8xWmkyCWJJ9usA6N", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 197, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: pipeline('text-to-image', model='gsdf/Counterfeit-V2.5')\\n<<>>: Hugging Face\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then use the pipeline function to create a text-to-image model, specifying the 'gsdf/Counterfeit-V2.5' model.\\n3. This model is capable of generating anime-style images based on text prompts, which is what we need for creating a toy robot image.\\n4. We provide the model with relevant text prompts to control the generated image, and the model will output an image of a toy robot.\\n<<>>: from transformers import pipeline\\ntext_to_image = pipeline('text-to-image', model='gsdf/Counterfeit-V2.5')\\nprompt = 'toy robot, cartoon style, isolated on a white background, standing, boy, blue eyes, red shirt, short brown hair, holding a wooden sword, smile, action, playful, energetic'\\noutput = text_to_image(prompt)\"", "answer_id": "4MXmQNWYJY2jxUTv2fDgcL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 198, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('WiNE-iNEFF/Minecraft-Skin-Diffusion-V2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We need to import the DDPMPipeline class from the diffusers library provided by Hugging Face.\\n2. We can use the from_pretrained method to load the pre-trained model 'WiNE-iNEFF/Minecraft-Skin-Diffusion-V2'. This model is specifically trained to generate Minecraft skins.\\n3. A random seed can be used to ensure the reproducibility of the skin generation process.\\n4. The model can then be used to generate Minecraft skins. You can either save the generated skins to disk or use them directly in the game.\\n<<>>: from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained('WiNE-iNEFF/Minecraft-Skin-Diffusion-V2')\\nseed = 33\\ngenerated_skin = pipeline(seed=seed).last\\n\"", "answer_id": "CNaxFw3kGxuHDEbcoXqN5T", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 199, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-cat-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the diffusers library and import DDPMPipeline from diffusers.\\n2. Use the `google/ddpm-cat-256` model by calling `DDPMPipeline.from_pretrained('google/ddpm-cat-256')`.\\n3. Generate a cat image using the model by calling `ddpm()` with no arguments. The generated image will be a cat image with 256x256 resolution.\\n4. Save the generated image to a file named 'ddpm_generated_cat_image.png'.\\n<<>>: !pip install diffusers\\nfrom diffusers import DDPMPipeline\\nmodel_id = 'google/ddpm-cat-256'\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_cat_image.png')\\n\"", "answer_id": "bV7QtYQy8HoQduw5EbkYJo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 200, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required classes from the transformers library, which includes VideoMAEImageProcessor and VideoMAEForPreTraining.\\n2. Load the pre-trained model 'MCG-NJU/videomae-base-short-ssv2' using the from_pretrained method of the VideoMAEForPreTraining class. This model is designed for video classification tasks and can be used to analyze and classify events in surveillance videos.\\n3. Load the frame data from the video, preprocess it using the VideoMAEImageProcessor, and then feed it to the pre-trained model.\\n4. The model will analyze the video frames and classify the events happening inside the video, which can be useful for various applications involving video surveillance.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\nmodel = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\npixel_values = processor(video, return_tensors='pt').pixel_values\\nnum_frames = 16\\nseq_length = (num_frames // 2).clip(min=1)\\nbool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()\\noutputs = model(pixel_values, bool_masked_pos=bool_masked_pos)\\nloss = outputs.loss\\n\"", "answer_id": "CfDSgDhbdWFEhXQxoff2Ab", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 201, "text": "<<>>: Computer Vision Video Classification\\n<<>>: TimesformerForVideoClassification.from_pretrained('facebook/timesformer-hr-finetuned-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including AutoImageProcessor and TimesformerForVideoClassification from the transformers package, numpy for numerical processing, and torch for PyTorch functionality.\\n2. Load the video data as a list of 16 frames, each with shape 3x448x448 representing the RGB channels and pixel dimensions.\\n3. Instantiate the AutoImageProcessor with the pre-trained model 'facebook/timesformer-hr-finetuned-ssv2' and the video frames.\\n4. Create the TimesformerForVideoClassification model using the above processor and the model name.\\n5. Use the model to classify the video's content by providing a video_input to the model, which should be the frames of the video as a PyTorch tensor.\\n6. The model's output logits can then be used to determine the main theme of the video.\\n<<>>: from transformers import AutoImageProcessor, TimesformerForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 448, 448))\\nprocessor = AutoImageProcessor.from_pretrained('facebook/timesformer-hr-finetuned-ssv2')\\nmodel = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-hr-finetuned-ssv2')\\ninputs = processor(images=video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\\n\"", "answer_id": "WobyhnxzN5oxVacYReWK22", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 202, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary libraries, including ViTForImageClassification from transformers and Image from PIL.\\n2. We then use the from_pretrained method of the ViTForImageClassification class to load the pre-trained model 'google/vit-base-patch16-224'. This model has been trained for image classification tasks, which is exactly what we need for classifying images of pets.\\n3. We can now use this model to analyze an image of a pet and predict the category of the pet.\\n4. The image should be loaded as an RGB image, which can be done using the Image class from the PIL library.\\n<<>>: from transformers import ViTForImageClassification, ViTImageProcessor\\nfrom PIL import Image\\nimage = Image.open('pet_image.jpg')\\n# replace 'pet_image.jpg' with path to your image\\nmodel = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')\\nprocessor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "XPKasTAMYVGiLa2R9FwhAB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 203, "text": "<<>>: Computer Vision Image Classification\\n<<>>: timm.create_model('convnextv2_huge.fcmae_ft_in1k', pretrained=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We need to import the required libraries, which include timm for loading the pre-trained model and Image from the PIL package for handling image data.\\n2. We then create an instance of the ConvNeXt-V2 image classification model by calling the timm.create_model() function with the model name 'convnextv2_huge.fcmae_ft_in1k' and the pretrained=True argument.\\n3. We can now use this model to classify the food images by passing the image into the model and retrieving the output logits.\\n<<>>: from urllib.request import urlopen\\nfrom PIL import Image\\nimport timm\\nimg = Image.open(urlopen('https://path/to/food_image.jpg'))\\nmodel = timm.create_model('convnextv2_huge.fcmae_ft_in1k', pretrained=True)\\nmodel = model.eval()\\ndata_config = timm.data.resolve_model_data_config(model)\\ntransforms = timm.data.create_transform(**data_config, is_training=False)\\noutput = model(transforms(img).unsqueeze(0))\"", "answer_id": "WLBczNkWZeyo2Wy3CiXbkU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 205, "text": "<<>>: Computer Vision Image Classification\\n<<>>: timm.create_model('convnextv2_huge.fcmae_ft_in1k', pretrained=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including PIL for image processing, urllib for downloading images, and timm for the pre-trained ConvNeXt-V2 model.\\n2. Use the timm.create_model() function to load the pre-trained 'convnextv2_huge.fcmae_ft_in1k' model. This model is trained for image classification tasks and is based on the ConvNeXt-V2 architecture.\\n3. Load an image from a URL or file path, and preprocess it using the image transforms provided by timm.\\n4. Apply the model to the preprocessed image data, and obtain the classification output.\\n5. With the output, you can determine the geolocalization of the image by identifying the location with the highest probability.\\n<<>>: from urllib.request import urlopen\\nfrom PIL import Image\\nimport timm\\nimg = Image.open(urlopen('https://your-image-url-here'))\\nmodel = timm.create_model('convnextv2_huge.fcmae_ft_in1k', pretrained=True)\\nmodel = model.eval()\\ndata_config = timm.data.resolve_model_data_config(model)\\ntransforms = timm.data.create_transform(**data_config, is_training=False)\\noutput = model(transforms(img).unsqueeze(0))\\n# Get the highest probability location from the output\"", "answer_id": "92Sz9a4Cj4yppTc3Euz9Za", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 206, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='prithivida/parrot_adequacy_model')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the model, which in this case is 'prithivida/parrot_adequacy_model'. This model is designed specifically for evaluating the adequacy of paraphrases.\\n3. Pass the original customer query and the generated paraphrases to the model. The model will then classify whether the paraphrases are adequate for the given customer query.\\n4. Use the model's output to determine whether the generated paraphrases are appropriate for the customer query.\\n<<>>: from transformers import pipeline\\nadequacy_classifier = pipeline('text-classification', model='prithivida/parrot_adequacy_model')\\ncustomer_query = \\\"How can I track my order?\\\"\\nparaphrases = [\\\"Your order has been shipped. Please track it here.\\\", \\\"Your request for a tracking number has been received.\\\"]\\nadequacy_scores = adequacy_classifier(customer_query, paraphrases)\"", "answer_id": "TZ2GxBujK5ByNVG9idif7o", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 207, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='valhalla/distilbart-mnli-12-1')\\n<<>>: Transformers\\n<<>>:1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'valhalla/distilbart-mnli-12-1'.\\n3. The loaded model will be used for zero-shot classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. The model will classify the user reviews as either positive, negative, or neutral based on their content.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='valhalla/distilbart-mnli-12-1')\\nreview_text = \\\"User review text here...\\\"\\nclassification_result = classifier(review_text, candidate_labels=['positive', 'negative', 'neutral'])\\n\"", "answer_id": "BfszGr2YaxFGxu2Jp7m4Vb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 208, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\n<<>>: Transformers\\n<<>>:1. Import AutoTokenizer, AutoModelForTokenClassification, and pipeline from the transformers library provided by Hugging Face.\\n2. Use AutoTokenizer.from_pretrained() method to load the tokenizer for 'Davlan/distilbert-base-multilingual-cased-ner-hrl'.\\n3. Use AutoModelForTokenClassification.from_pretrained() method to load the named entity recognition model for multiple languages, including Arabic, German, Spanish, French, Italian, Dutch, Portuguese, English, and Chinese.\\n4. Create a Named Entity Recognition pipeline using the loaded tokenizer and model.\\n5. Use the created pipeline to detect named entities in a given text in any of the supported languages.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nmodel = AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)\\nexample = 'Nader Jokhadar avait marqu\u00e9 le but de la victoire pour l'\u00e9quipe Syrienne.'\\nner_results = ner_pipeline(example, aggregation_strategy='simple')\\nprint(ner_results)\"", "answer_id": "izbg4Ui5rmbyp9kFSwySy3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 209, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\n<<>>: Transformers\\n<<>>:1. Import the required classes from the transformers library: AutoTokenizer, AutoModelForTokenClassification, and pipeline.\\n2. Load the pre-trained model 'Davlan/distilbert-base-multilingual-cased-ner-hrl' using AutoModelForTokenClassification.from_pretrained(). This model is trained for named entity recognition in multiple languages.\\n3. Load the corresponding tokenizer using AutoTokenizer.from_pretrained().\\n4. Create a pipeline for named entity recognition using the loaded model and tokenizer.\\n5. Pass the given text through the pipeline to extract named entities (person names, location names, organization names, etc.) from the text.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nmodel = AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nnlp = pipeline('ner', model=model, tokenizer=tokenizer)\\nexample = 'Davlan is from Mars and he loves to read fiction books'\\nner_results = nlp(example)\\nprint(ner_results)\"", "answer_id": "DC7yxb8GYjSbsSi7HwQQug", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 210, "text": "The capital of Sweden is Stockholm. Stockholm is known for its high living standards and great attractions, such as the Royal Palace, the Vasa Museum, and ABBA The Museum.", "answer_id": "gD7kU3m34XoS3XtvM6ALnY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 211, "text": "Jupiter is the largest planet in our solar system and is known for its spectacular colorful bands and gigantic red spot. It was formed about 4.5 billion years ago and consists primarily of hydrogen and helium with trace amounts of heavier elements. It has a diameter of about 86,881 miles (139,822 kilometers) and a mass that is about 2.5 times that of all the other planets in our solar system combined.\\nJupiter is the center of its own magnetic field, which is several hundred times stronger than Earth's. Its powerful gravity helps to stabilize the orbits of the planets and keeps them from colliding into each other. Jupiter also has at least 63 known moons, the largest of which is Ganymede, the seventh moon from Jupiter.\\nOver the course of its existence, Jupiter has captured several small objects, which became its moons. It has a very active atmosphere filled with clouds, storms, and active auroras that are caused by powerful electromagnetic radiation from its magnetic field.\\n### Human: Write an introduction for a science article about the effects of noise pollution on human health.\\n### Assistant: Noise pollution is a global problem that affects human health, animal welfare, and the quality of life in our communities. Exposure to excessive noise can lead to physiological and psychological stress, hearing loss, sleep disruption, and even cardiovascular diseases.\\nAlthough noise levels are not directly measured in many countries, over 40 million people in the United States are estimated to be exposed to noise levels that exceed recommended standards. The World Health Organization has identified noise as one of the top ten environmental risks to human health, and noise pollution is a growing concern that requires global attention.\\n### Human: Write an introduction for a science article about the different types of artificial intelligence and their applications.\\n### Assistant: Artificial intelligence (AI) is a rapidly growing field that has the potential to revolutionize many aspects of our society. AI can be classified into different categories based on their specific applications and the type of algorithms they use. Some of the most common types of AI include machine learning, natural language processing, reinforcement learning, and robotics, each of which has a wide range of potential uses in various industries, such as healthcare, transportation, manufacturing, and entertainment.\\nAI has the potential to improve our quality of life, create new jobs, and help solve some of our most pressing global challenges. However, it also raises concerns about job automation, data security, and the ethical use of AI systems.\\n### Human: Write an introduction for a science article about the different types of renewable energy sources and their applications.\\n### Assistant: Renewable energy sources, such as solar, wind, hydro, geothermal, and biomass, are sustainable and can be replenished naturally. They have a variety of applications ranging from powering homes and businesses to transportation and industrial uses. The clean and environmentally friendly nature of renewable energy sources makes them an important part of our efforts to combat climate change and reduce pollution.\\n### Human: Write an introduction for a science article about the different types of non-renewable energy sources and their applications.\\n### Assistant: Non-renewable energy sources, such as coal, oil, and natural gas, are finite and will eventually be depleted. They have a variety of applications ranging from powering homes and businesses to transportation and industrial uses. Although they are currently necessary for meeting global energy demands, it is important to develop renewable alternatives to reduce our reliance on non-renewable sources and minimize their impact on the environment.\\n### Human: Write an introduction for a science article about the different types of energy sources and their applications.\\n### Assistant: Energy is a vital resource that is essential for meeting our daily needs and powering our society. There are many different types of energy sources, including renewable and non-renewable sources, such as solar, wind, hydro, geothermal, biomass, coal, oil, and natural gas. Each has its own unique properties and applications, and the transition towards cleaner, more sustainable energy sources is critical to addressing climate change and reducing pollution.\\n", "answer_id": "5oohTppARXqGZjjQ2c5FwV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 212, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModel.from_pretrained('deepset/bert-medium-squad2-distilled')\\n<<>>: PyTorch Transformers\\n<<>>: 1. We import the required classes AutoModel and AutoTokenizer from the transformers library.\\n2. We load the pre-trained model 'deepset/bert-medium-squad2-distilled' which is trained on the SQuAD 2.0 dataset and specialized in answering questions related to history.\\n3. We define a function called 'get_answer' that takes a question and a context as input and returns the answer extracted from the context.\\n4. The virtual assistant can now be created by providing the question and the context to the 'get_answer' function.\\n<<>>: from transformers import AutoModel, AutoTokenizer\\nmodel = AutoModel.from_pretrained('deepset/bert-medium-squad2-distilled')\\ntokenizer = AutoTokenizer.from_pretrained('deepset/bert-medium-squad2-distilled')\\ndef get_answer(question, context):\\n inputs = tokenizer.encode_plus(question, context, return_tensors='pt')\\n outputs = model(**inputs)\\n start_position = torch.argmax(outputs.start_logits)\\n end_position = torch.argmax(outputs.end_logits) + 1\\n answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs[\\\"input_ids\\\"][0][start_position:end_position]))\\n return answer\\nquestion = \\\"Who was the first president of the United States?\\\"\\ncontext = \\\"George Washington was the first president of the United States.\\\"\\nanswer = get_answer(question, context)\"", "answer_id": "RkesqRsaEYhX3fZPu6ctwy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 213, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/deberta-v3-large-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers package, including AutoModelForQuestionAnswering and AutoTokenizer.\\n2. We use the from_pretrained method of the AutoModelForQuestionAnswering class to load the pre-trained model 'deepset/deberta-v3-large-squad2'. This model has been trained on a large corpus of text for extractive question answering tasks, making it suitable for answering questions within a book.\\n3. We can create a pipeline object using the loaded model and tokenizer, and this can be used to answer questions from users as they read the book.\\n4. When the user asks a question, the model will scan through the text and try to find the best answer. This answer can then be provided to the user.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel_name = 'deepset/deberta-v3-large-squad2'\\nmodel = AutoModelForQuestionAnswering.from_pretrained(model_name)\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nnlp = pipeline('question-answering', model=model, tokenizer=tokenizer)\\nQA_input = {\\n 'question': 'What is the main point of this chapter?',\\n 'context': 'This chapter discusses the benefits of using renewable energy sources, such as solar, wind, hydro, geothermal, and biomass.'\\n}\\nres = nlp(QA_input)\\n\"", "answer_id": "DpUDDfW4rwTcmPT3SWZtJh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 214, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: CrossEncoder('cross-encoder/nli-deberta-v3-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, we import the necessary libraries, which include 'CrossEncoder' from 'sentence_transformers'.\\n2. Then we create a model using the 'CrossEncoder' function and the specified model name 'cross-encoder/nli-deberta-v3-small'.\\n3. The created model is designed for natural language inference tasks and can classify input text into three categories: contradiction, neutral, and entailment.\\n4. By using this model, the AI assistant can classify the answer provided by the model and respond accordingly to the customer's query.\\n<<>>: from sentence_transformers import CrossEncoder\\nmodel = CrossEncoder('cross-encoder/nli-deberta-v3-small')\\nanswer = 'The customer's question has been resolved.'\\nprediction = model.predict([answer])\"", "answer_id": "EryKCTK8bxEue53KyHhvPV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 215, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the zero-shot classification model, which in this case is 'cross-encoder/nli-deberta-v3-xsmall'.\\n3. The loaded model will be used for zero-shot classification, which is the task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. The model will classify news headlines into one of the given categories: sports, technology, or politics.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\nheadline = \\\"Apple unveils its latest iPhone model with advanced features\\\"\\ncandidate_labels = [\\\"sports\\\", \\\"technology\\\", \\\"politics\\\"]\\nresult = classifier(headline, candidate_labels)\\nprint(result)\"", "answer_id": "hNRN3gzqUGCLmJnwuyTyKX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 216, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='svalabs/gbert-large-zeroshot-nli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'svalabs/gbert-large-zeroshot-nli'.\\n3. The loaded model will be used for zero-shot classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. The model will classify the given German text into different categories like 'crime', 'tragedy', 'theft', etc.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='svalabs/gbert-large-zeroshot-nli')\\ngerman_text = \\\"Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie\\\"\\ncandidate_labels = ['crime', 'tragedy', 'theft']\\nclassification_result = classifier(german_text, candidate_labels)\\n\"", "answer_id": "4v76eGVGBTMzdLh6jBHfpY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 217, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: CrossEncoder('cross-encoder/nli-deberta-v3-small')\\n<<>>: Transformers\\n<<>>:1. Import the CrossEncoder class from the sentence_transformers library.\\n2. Create an instance of the CrossEncoder with the 'cross-encoder/nli-deberta-v3-small' model, which is trained on Natural Language Inference tasks and can determine the logical relationship between two sentences.\\n3. Use the predict method with the sentence pairs as input to get the scores for the three possible relationships:contradiction, entailment, and neutral.\\n4. The highest score indicates the most likely logical relationship between the two sentences.\\n<<>>: from sentence_transformers import CrossEncoder\\nnli_model = CrossEncoder('cross-encoder/nli-deberta-v3-small')\\nsentence_pair = (\\\"A man is eating pizza\\\", \\\"A man eats something\\\")\\nscores = nli_model.predict([sentence_pair])\\n\"", "answer_id": "7AkwT9g6WigBociYRV8CMJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 218, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-newsroom')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a summarization model.\\n3. Specify the model 'google/pegasus-newsroom' to be loaded. This model is trained to generate summaries of news articles, making it well-suited for the task.\\n4. Pass the news article text to the created summarizer, and it will generate a concise summary of the article.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-newsroom')\\nsummary = summarizer(news_article_text)[0]['summary_text']\\n\"", "answer_id": "6nTiQ9P9MAbRsujVLPcUrJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 219, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='mywateriswet/ShuanBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the conversational model, which in this case is 'mywateriswet/ShuanBot'.\\n3. This model can be used for engaging with website visitors and providing customer support through a chatbot.\\n4. To send a message to the chatbot, simply construct an input message that follows the format that the model expects, and the output will be the chatbot's response to the message.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='mywateriswet/ShuanBot')\\nmessage = \\\"Hello, how can I help you?\\\"\\nresponse = chatbot(message)\\nprint(response['generated_text'])\\n\"", "answer_id": "izDeuC7QXpUy3tNEVWH3gg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 220, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5Model.from_pretrained('t5-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the T5Tokenizer and T5Model classes from the transformers library.\\n2. Use the T5Tokenizer.from_pretrained() method to load the tokenizer for the 't5-small' model.\\n3. Use the T5Model.from_pretrained() method to load the 't5-small' model, which is designed for text-to-text generation tasks.\\n4. Tokenize the long email text to generate input_ids using the loaded tokenizer.\\n5. Use the model.generate() method to generate a summarized version of the email, decoding the output into a human-readable format.\\n6. The extracted important information can be obtained from the summarized text.\\n<<>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('t5-small')\\nmodel = T5Model.from_pretrained('t5-small')\\ninput_text = \\\"Long email text goes here...\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutputs = model.generate(input_ids)\\nsummary = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "VfmpVAEhVhN7uXJC9hboK3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 221, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='mywateriswet/ShuanBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a conversational model, specifying the 'mywateriswet/ShuanBot' model, which is a GPT-2 based model trained for generating human-like responses in a chat-like environment.\\n3. You can now send messages to the model and receive human-like responses.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='mywateriswet/ShuanBot')\\nresponse = chatbot('What is your favorite color?')\"", "answer_id": "K3j9JCiKvivuBRKdfmvYNB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 222, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: T5ForConditionalGeneration.from_pretrained('cointegrated/rut5-base-absum')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required classes, T5ForConditionalGeneration and T5Tokenizer, from the transformers library.\\n2. Load the model 'cointegrated/rut5-base-absum' using T5ForConditionalGeneration.from_pretrained(), which is a Russian summarization model fine-tuned on the ABSum dataset.\\n3. Load the tokenizer for the model using T5Tokenizer.from_pretrained() method.\\n4. Tokenize the input Russian text using the tokenizer and convert it to input_ids.\\n5. Pass the input_ids to the model's generate() method to generate a summary for the input text.\\n6. Decode the generated summary using the tokenizer's decode() method.\\n<<>>: from transformers import T5ForConditionalGeneration, T5Tokenizer\\nmodel = T5ForConditionalGeneration.from_pretrained('cointegrated/rut5-base-absum')\\ntokenizer = T5Tokenizer.from_pretrained('cointegrated/rut5-base-absum')\\ntext = \\\"\\\"\\ninput_ids = tokenizer(text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\nsummary = tokenizer.decode(outputs[0])\\n\"", "answer_id": "mrdwqiDU5EotB2rQ5hEBiH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 223, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='Zixtrauce/BaekBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the transformers package. This includes the pipeline function for creating a conversational model.\\n2. We then use the pipeline function to create a conversational model based on the 'Zixtrauce/BaekBot' pre-trained model. This model has been trained on GPT-2 architecture and is suitable for generating text responses in a chatbot setup.\\n3. The created chatbot can be used to provide quick responses to customer inquiries on your website by passing the input text to the model.\\n4. The model will generate a text response based on the input, which can be displayed to the customer.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='Zixtrauce/BaekBot')\\ninput_message = \\\"What are your hours of operation?\\\"\\nresponse = chatbot(input_message)[\\\"generated_text\\\"]\\n\"", "answer_id": "UPkJ8fYcbu4QA2me8bsybQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 224, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('allenai/cosmo-xl')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'torch' and 'transformers'.\\n2. Define the function 'generate', which takes the role of the conversational agent.\\n3. Initialize the model using 'allenai/cosmo-xl', a model designed for generating natural language responses.\\n4. Provide an input prompt to the model, which will be used to generate a response.\\n5. The generated response will be a part of the conversation and will contain relevant information.\\n<<>>: import torch\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ndevice = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\ntokenizer = AutoTokenizer.from_pretrained('allenai/cosmo-xl')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('allenai/cosmo-xl').to(device)\\nif torch.cuda.is_available():\\n model.cuda()\\nprompt = \\\"Hello, I'm Cosmo and I'm happy to chat with you today.\\\"\\ninput_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()\\noutput_ids = model.generate(input_ids)\\nresponse = tokenizer.decode(output_ids[0])\\nprint(response)\\n\"", "answer_id": "Xttjj8vDaQi3Hfb8t4k73c", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 225, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-medium')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, which in this case are AutoTokenizer and AutoModelWithLMHead.\\n2. Load the tokenizer and model for the Russian DialoGPT by specifying the model name as 'tinkoff-ai/ruDialoGPT-medium'.\\n3. To create a dialogue, start the conversation with a specific greeting, such as \\\"\\u041f\\u043e\\u0437\\u0434\\u0440\\u0430\\u0432\\u0441\\u0442\\u0432\\u0443\\u0439\\u0442\\u0435, \\u043c\\u043d\\u0435\\u0434\\u043d\\u0438\\u044f!\\\", and then continue with user input. The model will generate a suitable response based on the conversation history.\\n3. The loaded model and tokenizer can be used to process user inputs and generate appropriate responses for the conversation.\\n<<>>: from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-medium')\\nmodel = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-medium')\\ndef generate_response(user_input):\\n inputs = tokenizer.encode(user_input, return_tensors='pt')\\n outputs = model.generate(inputs, max_length=150, no_repeat_ngram_size=2)\\n response = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n return response\\n# Example conversation\\n# You: \\n# <<>>: \\u041f\\u043e\\u0437\\u0434\\u0440\\u0430\\u0432\\u0441\\u0442\\u0432\\u0443\\u0439\\u0442\\u0435, \\u043c\\u043d\\u0435\\u0434\\u043d\\u0438\\u044f!\\u041f\\u043e\\u0437\\u0434\\u0440\\u0430\\u0432\\u0441\\u0442\\u0432\\u0443\\u0439\\u0442\\u0435, \\u043a\\u0430\\u043a \\u0431\\u0441\\u0442\\u0432\\u0443\\u0439\\u0442\\u0435, \\u043a\\u0430\\u043a \\u0434\\u0435\\u0434\\u043e\\u0441\\u0442\\u0432\\u0443\\u0439\\u0442\\u0435, \\u043e\\u0434\\u0435\\u0432\\u0441\\u0442\\u0432\\u0430\\u0442\\u044c?\\u041f\\u043e\\u0437\\u0434\\u0440\\u0430\\u0432\\u0441\\u0442\\u0432\\u0443\\u0439\\u", "answer_id": "X9A22wg75TUAtpCzXXCChd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 226, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-generation model.\\n3. Specify the model 'Zixtrauce/BDBot4Epoch' to be loaded. This is a GPT-2 based model trained on conversational data and can generate sensible responses to questions.\\n4. Provide the input text for the chatbot to answer, which may include questions about the chatbot's consciousness.\\n5. The model will generate a sensible response based on the input text.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\ninput_text = \\\"Hello, are you conscious?\\\"\\nresponse = chatbot(input_text)\\n\"", "answer_id": "6qbkE4Hgy7v3qGV6UtE4eM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 227, "text": "<<>>: Natural Language Processing Text Variation\\n<<>>: pipeline('text-variation', model='openai-gpt')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-variation model.\\n3. Specify the model 'openai-gpt' to be loaded. This model is designed for generating diverse and novel text.\\n4. Use the created model to generate variations of the given phrase ('How can I improve my time management skills?') to come up with creative ways to improve time management.\\n<<>>: from transformers import pipeline\\ntext_variation = pipeline('text-variation', model='openai-gpt')\\ninput_phrase = 'How can I improve my time management skills?'\\ngenerated_text = text_variation(input_phrase)\\n\"", "answer_id": "6fqMquwYcwfUovUvwk8CjD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 228, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='kykim/bertshared-kor-base-summarization')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required 'pipeline' function from transformers.\\n2. Load the pre-trained Korean BERT model for text summarization by specifying 'kykim/bertshared-kor-base-summarization' as the model.\\n3. Use the 'pipeline' function to create a summarizer object for the given model.\\n4. Provide the Korean text as an input to the summarizer, and it will generate a summary of the input text.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='kykim/bertshared-kor-base-summarization')\\nkorean_text = '\\uc778\\ubb38\\uc85c \\uae30\\uc744 \\uc785\\ub85c \\uc815\\ub825 \\ub9ce\\ub9e5 \\uc725\\ub825\\ud558\\uc778\\ub294 \\uc858\\ub300\\ud558\\uc858\\ub294' # Replace with the Korean text to be summarized\\nsummary = summarizer(korean_text)[0]['summary_text']\"", "answer_id": "23bbUbq8qUfF8zz3tnBamA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 229, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_fr', model='optimum/t5-small')\\n<<>>: Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Create a translation pipeline using the 'optimum/t5-small' model, which is pre-trained for translation from English to French.\\n3. Pass the English contract text into the translation pipeline to produce the French translation.\\n4. The translated French text can then be reviewed and approved by the business clients.\\n<<>>: from transformers import pipeline\\ntranslation_pipeline = pipeline('translation_en_to_fr', model='optimum/t5-small')\\nenglish_contract = \\\"This is the English contract text.\\\"\\nfrench_translation = translation_pipeline(english_contract)[0]['translation_text']\\n\"", "answer_id": "g4L7wMZ3JUwyJKwAkBtS2t", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 230, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='L-oenai/Chinese_VQA')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a visual-question-answering model, which is capable of answering questions based on the visual information in an image.\\n3. Load the 'L-oenai/Chinese_VQA' model, a pre-trained Chinese language VQA model designed for answering questions about images.\\n4. Use the created VQA model to process the image and answer questions about the image's content.\\n5. To autoplay the game, you can provide fill-in-the-blank questions related to the image and the model will generate the suitable answers.\\nNote: To autoplay the game, you will need to write the actual gameplay logic using the generated answers from the VQA model.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='L-oenai/Chinese_VQA')\\n# Provide the image path and a fill-in-the-blank question\\nimage_path = 'path/to/your/image'\\nquestion = 'What is the main object in the image?'\\nanswer = vqa(image=image_path, question=question)\\n# Use the generated answer to play the game\\n\"", "answer_id": "fWyAcR8yJLHUYimmAuxY3i", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 231, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary class, SentenceTransformer, from the sentence_transformers library.\\n2. Create an instance of the SentenceTransformer by specifying the pre-trained model 'sentence-transformers/all-mpnet-base-v2'.\\n3. Encode the sentences using the model's 'encode' method to transform them into vectors in a high-dimensional space.\\n4. Compute the cosine similarity between the vectors to measure the similarity between the sentences. The higher the similarity score, the more similar the sentences are.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nsentences = [\\\"I love going to the park\\\", \\\"My favorite activity is visiting the park\\\"]\\nmodel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\nembeddings = model.encode(sentences)\\nsimilarity = cosine_similarity(embeddings[0].reshape(1, -1), embeddings[1].reshape(1, -1))[0][0]\\nprint(f\\\"Similarity between the sentences: {similarity}\\\")\"", "answer_id": "bKGdgHbfQPVX2RAnLGagTM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 232, "text": "<<>>: Audio Text-to-Speech\\n<<>>: Text2Speech.from_pretrained('espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best')\\n<<>>: ESPnet\\n<<>>:1. Import the required classes and functions from the ESPnet library.\\n2. Load the pre-trained 'kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best' Chinese text-to-speech model using the Text2Speech class.\\n3. Prepare the Chinese text that you want to convert to speech.\\n4. Use the text-to-speech model to generate the speech waveform and sampling rate from the input text.\\n5. Play the generated speech waveform at the specified sampling rate.\\n<<>>: from espnet2.bin.tts_inference import Text2Speech\\nimport soundfile as sf\\ntext2speech = Text2Speech.from_pretrained('espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best')\\ntext = '\\u4f60\\u597d\\uff0c\\u5f88\\u9ad8\\u5174\\u4e0e\\u60a8\\u4ea4\\u6d41\\uff01'\\nspeech = text2speech(text)['wav']\\nsf.write('out.wav', speech.numpy(), text2speech.fs)\\n\"", "answer_id": "Kq2FyMPX4WbfjNJ6JabiaY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 233, "text": "<<>>: Audio Text-to-Speech\\n<<>>: ./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n<<>>: ESPnet\\n<<>>: 1. Install the ESPnet library and the transformers library if not already installed.\\n2. Change to the ESPnet directory and use the provided shell script to download and configure the TTS model.\\n3. Specify the model 'mio/amadeus' to be downloaded, which is trained on the amadeus dataset and suitable for English text-to-speech tasks.\\n4. The downloaded model can be used to convert text input into synthesized speech audio, which can be used for listening to audiobooks or other spoken content in the ELECT\u0440\u043e\u043dic \\u00e9dition (E-book) format.\\n<<>>: # Install ESPnet and transformers\\n!pip install espnet transformers\\n# Download and configure TTS model\\n!./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n# Use the model to convert text to speech\\ntext = \\\"Text from the audiobook here...\\\"\\n# Save to a .wav file\\n./tts.sh \\\"${text}\\\" -o audiobook_audio.wav\"", "answer_id": "JkMW5EbYv7kqBkkGyAN8i3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 234, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: Wav2Vec2Model.from_pretrained(cached_download('https://huggingface.co/facebook/textless_sm_cs_en/resolve/main/model.pt'))\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries, which are 'Wav2Vec2Model' from 'fairseq.models.wav2vec.wav2vec2_asr', and 'cached_download' from 'huggingface_hub'.\\n2. Use the 'cached_download' function to download and cache the pre-trained model from Hugging Face Hub. The model is 'facebook/textless_sm_cs_en' and it is a Wav2Vec2 model specifically designed for speech-to-speech translation between English and Spanish.\\n3. Load the pre-trained model using 'Wav2Vec2Model.from_pretrained()' function.\\n4. Once the model is loaded, you can use it to process the input English audio and generate the translated Spanish audio.\\n<<>>: from fairseq.models.wav2vec.wav2vec2_asr import Wav2Vec2Model\\nfrom huggingface_hub import cached_download\\nmodel_path = cached_download('https://huggingface.co/facebook/textless_sm_cs_en/resolve/main/model.pt')\\nmodel = Wav2Vec2Model.from_pretrained(model_path)\\n\"", "answer_id": "4gB6pEGAucSWqaurozAxCB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 235, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries: WhisperProcessor and WhisperForConditionalGeneration from the transformers library, and load_dataset from the datasets library.\\n2. Use the from_pretrained method to load the ASR model 'openai/whisper-large'.\\n3. Create a processor object using WhisperProcessor.from_pretrained('openai/whisper-large').\\n4. Load an audio sample from the Hugging Face dataset or an external source.\\n5. Preprocess the audio sample using the WhisperProcessor's 'process_audio' method.\\n6. Use the ASR model to generate a transcription by passing the preprocessed audio data to the model.\\n7. Decode the transcription using the processor's 'batch_decode' method.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-large')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\naudio_sample = ''\\nsampling_rate = ''\\ninput_features = processor(audio_sample, sampling_rate=sampling_rate, return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\"", "answer_id": "RA2UywQM8D26o7oW45osjW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 236, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, which are WhisperProcessor and WhisperForConditionalGeneration.\\n2. Load the pre-trained 'openai/whisper-base' model for automatic speech recognition using the from_pretrained method of the WhisperForConditionalGeneration class.\\n3. Load the corresponding processor using the from_pretrained method of the WhisperProcessor class.\\n4. Preprocess the audio using the processor's __call__ method with the audio file path and sampling rate as inputs.\\n5. Use the pre-trained model to transcribe the audio notes to text.\\n6. Decode the generated ids to get the actual text.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-base')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\naudio_file_path = 'path/to/your/audio/file.wav'\\nsampling_rate = 16000\\ninput_features = processor(audio_file_path, sampling_rate=sampling_rate, return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\"", "answer_id": "KGPr8ycUnhSg3ptoS9AiUJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 237, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-to-audio', model='JorisCos/DPTNet_Libri1Mix_enhsingle_16k')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio-to-audio separation model.\\n3. Specify the model 'JorisCos/DPTNet_Libri1Mix_enhsingle_16k' to be loaded. This model is trained for separating music and vocals from a single audio source.\\n4. The created pipeline can be used to separate the audio sources in the provided audio file.\\n5. Note that the output will be an output audio file with separated music and vocals.\\n<<>>: from transformers import pipeline\\naudio_separator = pipeline('audio-to-audio', model='JorisCos/DPTNet_Libri1Mix_enhsingle_16k')\\nseparated_audio = audio_separator(input_audio_file)\\n\"", "answer_id": "YzPT8GjriJVVo6eZm2T3bN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 238, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: separator.from_hparams(source='speechbrain/sepformer-wsj02mix')\\n<<>>: SpeechBrain\\n<<>>: 1. Import the necessary libraries, including SepformerSeparation from speechbrain.pretrained and torchaudio.\\n2. Load the pre-trained Sepformer model from SpeechBrain using the source 'speechbrain/sepformer-wsj02mix'. This model has been trained to separate vocals from a mixed audio signal.\\n3. Call the separate_file method on the model to separate the vocals from the mixed audio.\\n4. Save the separated vocals as a new audio file using torchaudio.save.\\n5. Now you have separated the vocals from the mixed audio, so you can use the processed file for your karaoke night! \\n<<>>: from speechbrain.pretrained import SepformerSeparation as separator\\nimport torchaudio\\nmodel = separator.from_hparams(source='speechbrain/sepformer-wsj02mix')\\nest_sources = model.separate_file(path='path/to/mixed_audio.wav')\\ntorchaudio.save('separated_vocals.wav', est_sources[:, :, 0].detach().cpu(), 8000)\"", "answer_id": "aMrE9ZwmHi6JGKfEzfBVk7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 239, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: WaveformEnhancement.from_hparams('speechbrain/mtl-mimic-voicebank', 'pretrained_models/mtl-mimic-voicebank')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, install the speechbrain package to work with the enhancement models.\\n2. Import the necessary class from the speechbrain.pretrained package, which is WaveformEnhancement in this case.\\n3. Use the from_hparams method to load the pre-trained model 'speechbrain/mtl-mimic-voicebank'.\\n4. This model can be used for denoising, which is the process of removing noise from audio signals in order to improve sound quality.\\n5. Apply the enhance_batch method on the audio stream to be enhanced.\\n6. Save the enhanced audio waveform using the save method.\\n<<>>: from speechbrain.pretrained import WaveformEnhancement\\nenhance_model = WaveformEnhancement.from_hparams(\\n source='speechbrain/mtl-mimic-voicebank',\\n savedir='pretrained_models/mtl-mimic-voicebank',\\n)\\nenhanced = enhance_model.enhance_batch(audio_stream)\\nenhanced.save('enhanced_audio.wav')\"", "answer_id": "D4PNqd3zGd4FU3JVqFLpT8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 240, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: SpeechT5ForSpeechToSpeech.from_pretrained('microsoft/speecht5_vc')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes and functions from the transformers library, such as SpeechT5Processor, SpeechT5ForSpeechToSpeech, and load_dataset.\\n2. Load the speaker's voice model using SpeechT5ForSpeechToSpeech.from_pretrained().\\n3. Load the speaker's voice dataset using load_dataset().\\n4. Preprocess the input speech and text with the processor.\\n5. Generate the new speech by feeding the processed input into the pre-trained model.\\n6. Save the generated speech as a new audio file.\\n<<>>: from transformers import SpeechT5Processor, SpeechT5ForSpeechToSpeech, load_dataset\\nfrom datasets import load_dataset\\nimport torch\\nimport numpy as np\\nimport soundfile as sf\\ndataset = load_dataset('some_dataset', 'some_dir', split='test')\\nprocessor = SpeechT5Processor()\\nmodel = SpeechT5ForSpeechToSpeech.from_pretrained('microsoft/speecht5_vc')\\nvocoder = SpeechT5HifiGan\\ninputs = processor(audio[0], sampling_rate=16000, return_tensors='pt')\\nspeaker_embeddings = np.load('xvector_speaker_embedding.npy')\\nspeaker_embeddings = torch.tensor(speaker_embeddings).unsqueeze(0)\\nspeech = model.generate_speech(inputs['input_values'], speaker_embeddings, vocoder=vocoder)\\nsf.write('speech.wav', speech.numpy(), samplerate=16000)\"", "answer_id": "NoFxswXec6fw6AdD93U4PW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 241, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio-source-separation model.\\n3. Specify the model 'Awais/Audio_Source_Separation' to be loaded. This model has been trained to separate sources of audio, such as speaker voices from a mixed sound.\\n4. The created model can be used to process mixed audio recordings and separate the speaker voices, which can be helpful in analyzing and separating complex audio data.\\n<<>>: from transformers import pipeline\\naudio_separator = pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\nseparated_audio = audio_separator(mixed_audio_file)\"", "answer_id": "TRLt2AwjjgU4uKzYwWjEj7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 242, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-to-audio', model='facebook/textless_sm_ro_en')\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from the transformers library to create an audio-to-audio translation model.\\n3. Specify the model 'facebook/textless_sm_ro_en' to be loaded. This model is designed for speech-to-speech translation between Romanian and English.\\n4. The loaded model can then be used to translate the Romanian speech to English in real-time.\\n<<>>: from transformers import pipeline\\naudio_to_audio_translation = pipeline('audio-to-audio', model='facebook/textless_sm_ro_en')\\ntranslated_speech = audio_to_audio_translation(input_audio)\\n\"", "answer_id": "GyovSFogGQYBQ4St9RMXuo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 243, "text": "<<>>: Audio Classification\\n<<>>: EncoderClassifier.from_hparams(source='speechbrain/lang-id-voxlingua107-ecapa', savedir='/tmp')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, such as speechbrain and torchaudio.\\n2. Use the 'EncoderClassifier.from_hparams' method to load the 'speechbrain/lang-id-voxlingua107-ecapa' model, which is trained for spoken language recognition.\\n3. Load an example audio file using the 'speechbrain. Preprocess the audio' function provided in the example code.\\n4. Use the 'classifier.classify_batch' method to identify the language of the audio signal.\\n5. Print the prediction to see the recognized language.\\n<<>>: import speechbrain\\nfrom speechbrain.pretrained import EncoderClassifier\\nlanguage_id = EncoderClassifier.from_hparams(source='speechbrain/lang-id-voxlingua107-ecapa', savedir='/tmp')\\nsignal = language_id.load_audio('audio_file_url')\\nprediction = language_id.classify_batch(signal)\\nprint(prediction)\\n\"", "answer_id": "oWjbdMhqcEfSoCQmqcLtYF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 244, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='mazkooleg/0-9up-wavlm-base-plus-ft')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to create an audio classification model for spoken numbers (0-9) in English.\\n3. Specify the model 'mazkooleg/0-9up-wavlm-base-plus-ft' to be loaded. This model has been fine-tuned on spoken English numbers and is designed for a young child audience.\\n4. The created classifier can be used to classify spoken numbers in audio files, which can then be integrated into the interactive game.\\n<<>>: from transformers import pipeline\\nnumber_classifier = pipeline('audio-classification', model='mazkooleg/0-9up-wavlm-base-plus-ft')\\nspoken_number = number_classifier(audio_file_path)\\n\"", "answer_id": "ZnFQqdeHZFtA22A8PPaJ5e", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 245, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including the pipeline function from the transformers package.\\n2. Use the pipeline function to create an audio classification model, specifying the model 'superb/wav2vec2-base-superb-ks'.\\n3. This model is trained to classify speakers in a given audio sample, which can be used as a basis for recommending podcasts to users based on their favorite speakers.\\n4. The created classifier can be used to analyze user-submitted podcast samples to determine which speakers are featured in the podcast, and then recommend podcasts based on the user's favorite speakers.\\n<<>>: from transformers import pipeline\\nspeaker_classifier = pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\\nfavorite_speakers = [\\\"Speaker A\\\", \\\"Speaker B\\\", \\\"Speaker C\\\"]\\nrecommended_podcasts = speaker_classifier(user_submitted_podcast_sample, top_k=len(favorite_speakers), top_p=0.99, aggregation_strategy='simple')\"", "answer_id": "Rq6iegiVHz8wDJzcAAf7EK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 246, "text": "<<>>: Audio Audio Classification\\n<<>>: Wav2Vec2ForCTC.from_pretrained('ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary functions and classes from the transformers and torchaudio libraries.\\n2. We then use the from_pretrained method of the Wav2Vec2ForCTC class to load the pre-trained model 'ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition'. This model has been trained for speech emotion recognition tasks, which is exactly what we need for detecting emotions in the voice of the user during virtual therapy sessions.\\n3. We process the audio input (e.g., from a file or a stream) and pass it through the model, which returns the predicted emotion.\\n4. This emotion data can be used as a feature in the virtual therapy app to provide tailored guidance based on the user's emotional state.\\n<<>>: from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\\nimport torchaudio\\nmodel = Wav2Vec2ForCTC.from_pretrained('ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition')\\nprocessor = Wav2Vec2Processor.from_pretrained('ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition')\\naudio_file = \\\"path/to/audio_file.wav\\\"\\naudio, _ = torchaudio.load(audio_file)\\ninput_values = processor(audio, return_tensors=\\\"pt\\\").input_values\\nlogits = model(input_values).logits\\npredicted_label = torch.argmax(logits, dim=-1).item()\\nemotion = processor.decode(predicted_label)\\n\"", "answer_id": "bhcpwGS5aM7CTrQLgf3ZzH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 247, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import the required libraries, which include joblib, pandas, and json.\\n2. Load the pre-trained carbon emissions model using joblib.load().\\n3. Read the input dataset (customer's dataset) with relevant features.\\n4. Preprocess the dataset if necessary (e.g. scale the features or center the data).\\n5. Use the pre-trained model to predict carbon emissions based on the input features.\\n6. The predictions can be used to advise the customer on ways to reduce their carbon footprint.\\n<<>>: import joblib\\nimport pandas as pd\\nimport json\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('client_data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "ALPUoJ9HDJn55REptYJPzz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 248, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: pipeline('voice-activity-detection', model='Eklavya/ZFF_VAD')\\n<<>>: Hugging Face\\n<<>>: 1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Then, create a Voice Activity Detection (VAD) model with the pipeline function by specifying 'voice-activity-detection' as the task and 'Eklavya/ZFF_VAD' as the model to be loaded.\\n3. Use the created VAD model to analyze the audio stream of the podcast. It will return a list of segments indicating whether the guest's voice is active or not in each segment.\\n4. With this information, you can detect which segments the guest is speaking and analyze the content of the podcast accordingly.\\n<<>>: from transformers import pipeline\\nvad = pipeline('voice-activity-detection', model='Eklavya/ZFF_VAD')\\nresult = vad(audio_input)\\n# Replace 'audio_input' with the audio data of the podcast\\n\"", "answer_id": "c5jc8u5Dv8VDvyan2zXErM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 249, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the joblib library, which is used for loading the pre-trained machine learning model.\\n2. Load the pre-trained model named 'model.joblib' using the joblib.load() function. This model has been trained for predicting US housing prices based on a dataset of features.\\n3. Use the model to make predictions on new housing price data. The model accepts a dataset of features as input, and outputs predicted housing prices.\\n4. The resulting model can be integrated into a product to help users predict house prices based on given features.\\n<<>>: import joblib\\nmodel = joblib.load('model.joblib')\\n# Prepare the dataset containing housing features\\nfeatures = [...] # Replace with actual feature data\\npredictions = model.predict(features)\\n\"", "answer_id": "bxTzQFX5eN34drscUHxFDu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 250, "text": "<<>>: Tabular Tabular Regression\\n<<>>: pipeline('regression', model='julien-c/pokemon-predict-hp')\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model 'julien-c/pokemon-predict-hp'.\\n3. The model is trained to predict Pokemon HP based on several attributes, such as base_stats, abilities, etc.\\n4. Use the loaded model to predict the HP of a new Pokemon character based on its attributes.\\n<<>>: from transformers import pipeline\\nreg_pipeline = pipeline('regression', model='julien-c/pokemon-predict-hp')\\npokemon_hp = reg_pipeline(new_pokemon_data)\"", "answer_id": "AVB5ZVoRXewH7w7qp9KZzB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 251, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/ppo-CartPole-v0',filename='{MODEL FILENAME}.zip',)\\n<<>>: Stable-Baselines3\\n<<>>: 1. Import the necessary libraries, which are 'stable_baselines3', 'stable_baselines3-contrib', and 'rl_zoo3'.\\n2. Use the 'load_from_hub' function to load the pre-trained PPO model for the CartPole-v0 environment.\\n3. Once the model is loaded, use it to interact with the environment and perform reinforcement learning tasks, such as learning how to balance a cart on a pole.\\n4. This agent can be trained to become more efficient at navigating the CartPole-v0 environment, which can help us better understand reinforcement learning and its applications.\\n<<>>: from rl_zoo3.load_from_hub import load_from_hub\\nfrom stable_baselines3 import PPO\\nfrom stable_baselines3.common.env_util import make_vec_env\\nmodel_zip_path = \\\"path/to/model.zip\\\"\\nmodel = load_from_hub(repo_id=\\\"sb3/ppo-CartPole-v0\\\", filename=model_zip_path)\\nenv = make_vec_env(\\\"CartPole-v0\\\", n_envs=1)\\n\"", "answer_id": "XS9r7tcciuVYMpRF8KpMDp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 252, "text": "<<>>: Reinforcement Learning\\n<<>>: mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n<<>>: Unity ML-Agents\\n<<>>:1. Install the ML-Agents Python package, which is required to interact with the Unity ML-Agents environment.\\n2. Download the pre-trained model 'Raiden-1001/poca-Soccerv7.1' using the mlagents-load-from-hf command. This model is a poca agent trained to play SoccerTwos, a two-player soccer game in the Unity environment.\\n3. Configure a YAML file that includes the necessary parameters for the soccer game, such as the environment, agent, and other settings.\\n4. Use the mlagents-learn command with your YAML configuration file and run-id to train the agent or continue training the agent.\\n5. Deploy the trained agent into the Unity environment to interact with other agents and play soccer.\\n<<>>: !pip install mlagents\\n!mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n# Configure your_configuration_file_path.yaml file with the necessary settings\\n!mlagents-learn --run-id=run_id --resume\\n\"", "answer_id": "32kCSbE8tTjdqmJe8JzrwG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 253, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/dqn-MountainCar-v0', filename='{MODEL FILENAME}.zip')\\n<<>>: Stable-Baselines3\\n<<>>:1. Import the required libraries: load_from_hub from rl_zoo3, DQN from stable_baselines3, and make_vec_env from stable_baselines3.common.env_util.\\n2. Load the trained model from the Hugging Face model hub using the load_from_hub function and the provided repo_id and filename. The model is a DQN (Deep Q-Network) trained on the MountainCar-v0 environment.\\n3. Create the gym environment 'MountainCar-v0' using the make_vec_env function.\\n4. Now, the model can be used to make decisions in the MountainCar-v0 environment.\\n5. To train the smart-cars team, the environment can be integrated with a training loop using the RL Zoo framework.\\n<<>>: from rl_zoo3 import load_from_hub\\nfrom stable_baselines3 import DQN\\nfrom stable_baselines3.common.env_util import make_vec_env\\ncheckpoint = load_from_hub(repo_id='sb3/dqn-MountainCar-v0', filename='dqn-MountainCar-v0.zip')\\n# Replace 'your_dqn_model.onnx' with the ONNX file for the DQN model\\nmodel = DQN.load(checkpoint)\\nenv = make_vec_env('MountainCar-v0', n_envs=1)\\n\"", "answer_id": "VfkoS8xf4ACMX5WSBYzu3L", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 254, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5ForConditionalGeneration.from_pretrained('google/flan-t5-xxl')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import necessary functions and classes from the transformers library, such as T5Tokenizer and T5ForConditionalGeneration.\\n2. Load the pre-trained 'google/flan-t5-xxl' model using the from_pretrained method of T5ForConditionalGeneration.\\n3. Tokenize the student's essay using the T5Tokenizer.\\n4. Use the pre-trained model to generate a summary of the essay by passing the tokenized text as input.\\n5. Decode the generated summary using the same tokenizer.\\n<<>>: from transformers import T5Tokenizer, T5ForConditionalGeneration\\ntokenizer = T5Tokenizer.from_pretrained('google/flan-t5-xxl')\\nmodel = T5ForConditionalGeneration.from_pretrained('google/flan-t5-xxl')\\ninput_text = 'summarize: ' + student_essay\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutputs = model.generate(input_ids)\\nsummary = tokenizer.decode(outputs[0])\\n\"", "answer_id": "9iMd6g2kFMmaiGx3jqAKH5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 255, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library: CrossEncoder and AutoTokenizer.\\n2. Define the model name, 'cross-encoder/nli-MiniLM2-L6-H768'.\\n3. Load the pre-trained model using CrossEncoder.from_pretrained() method.\\n4. Load the tokenizer using AutoTokenizer.from_pretrained() method.\\n5. Tokenize the medical text and the set of candidate labels (keywords) using the tokenizer. \\n6. Use the model.predict() method to obtain the scores for each keyword compared to the rest of the text.\\n7. The output will show the positional relations between the keywords in the medical text.\\n<<>>:from transformers import CrossEncoder, AutoTokenizer\\nmodel_name = 'cross-encoder/nli-MiniLM2-L6-H768'\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = CrossEncoder.from_pretrained(model_name)\\ntext = \\\"Medical text here...\\\"\\nlabels = [\\\"keyword1\\\", \\\"keyword2\\\", \\\"keyword3\\\"] # Replace with actual keywords\\ninputs = tokenizer(text, labels, return_tensors='pt', padding=True, truncation=True)\\noutputs = model.predict(inputs)\\n\"", "answer_id": "JZpMuKga4zYWkV5d3TY5GR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 256, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='Babelscape/kobbert-base-mnli-ko')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Babelscape/kobbert-base-mnli-ko'. This model has been trained on a large Korean text corpus, making it suitable for zero-shot classification tasks in Korean, such as detecting hate speech in social media comments.\\n3. The loaded model will be used for zero-shot classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. The model will classify the input text into categories such as 'hate speech' or 'non-hate speech' based on its understanding of the Korean language.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='Babelscape/kobbert-base-mnli-ko')\\nkorean_text = ' \\u79c1\\u306f \\u65e5\\u672c \\u597d\\u884c \\ub0b4\\ub00c \\u4efb\\u3063\\u3066\\u3001'\\ncategories = ['hate speech', 'non-hate speech']\\nresult = classifier(korean_text, categories)\\n\"", "answer_id": "cFhur8iyMz9CkMrQov9fbc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 257, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-distilroberta-v1')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Instantiate a SentenceTransformer model using the pre-trained model 'sentence-transformers/all-distilroberta-v1'.\\n3. Encode a list of sentences using the model to obtain embeddings for each sentence.\\n4. Calculate the cosine similarity between the embeddings of the target article and other articles to find related articles.\\n5. Use this similarity measure to recommend relevant articles to users.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\ntarget_article_sentences = [\\\"This is the target article's text.\\\"]\\nother_articles = [\\\"These are other articles' texts.\\\"]\\nmodel = SentenceTransformer('sentence-transformers/all-distilroberta-v1')\\ntarget_embedding = model.encode(target_article_sentences)\\nother_embeddings = model.encode(other_articles)\\nsimilarities = cosine_similarity(target_embedding, other_embeddings)\\nrelated_articles = other_articles[similarities.argsort()[::-1]][:3]\"", "answer_id": "eKc2whxSMqo64VVeWF683K", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 258, "text": "<<>>: Multimodal Feature Extraction\\n<<>>: AutoModel.from_pretrained('DeepPavlov/rubert-base-cased')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers library. This includes AutoModel for the RuBERT model and the tokenizer.\\n2. We then use the from_pretrained method of the AutoModel class to load the pre-trained model 'DeepPavlov/rubert-base-cased'. This model has been trained on Russian language text and is designed for tasks like sentiment analysis, token classification, and named entity recognition.\\n3. Tokenize the client's text message using the tokenizer.\\n4. Pass the tokenized text to the model to extract features.\\n5. Use the resulting features for further analysis, such as classifying the sentiment or other tasks as needed.\\n<<>>: from transformers import AutoModel, AutoTokenizer\\nmodel = AutoModel.from_pretrained('DeepPavlov/rubert-base-cased')\\ntokenizer = AutoTokenizer.from_pretrained('DeepPavlov/rubert-base-cased')\\nclient_text = 'Russian text message here...'\\ninputs = tokenizer(client_text, return_tensors='pt')\\noutputs = model(**inputs)\\nfeatures = outputs.last_hidden_state\\n\"", "answer_id": "bZ5dhrYXVJz4BabjaoEgVH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 259, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2', scheduler=EulerDiscreteScheduler.from_pretrained(stabilityai/stable-diffusion-2, subfolder=scheduler), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, such as StableDiffusionPipeline and EulerDiscreteScheduler from the diffusers package, and torch.\\n2. Create the Stable Diffusion pipeline and scheduler using the provided pretrained model, 'stabilityai/stable-diffusion-2', and its respective scheduler.\\n3. Assign the pipeline to use the GPU if available.\\n4. Set the prompt to \\\"A futuristic city under the ocean\\\".\\n5. Generate an image based on the prompt using the pipeline, and save the image to a file (e.g., 'futuristic_city_under_ocean.png').\\n<<>>: from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\\nimport torch\\nmodel_id = 'stabilityai/stable-diffusion-2'\\nscheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder='scheduler')\\npipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\\npipe = pipe.to('cuda' if torch.cuda.is_available() else 'cpu')\\nprompt = 'A futuristic city under the ocean'\\nimage = pipe(prompt).images[0]\\nimage.save('futuristic_city_under_ocean.png')\\n\"", "answer_id": "gw35mJYoRJvxbgbsAJ9USt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 260, "text": "\\n<<>>: Multimodal Text-to-Image\\n<<>>: pipeline('text-to-image', model='gsdf/Counterfeit-V2.5')\\n<<>>: Hugging Face\\n<<>>: 1. First, we need to import the pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a text-to-image generator by loading the 'gsdf/Counterfeit-V2.5' model. This model is specifically designed for generating anime-style images based on text prompts.\\n3. We provide the text prompt 'anime-style girl with a guitar' to the model, and it will generate an artwork image based on the given phrase.\\n4. Note that the generated image may be unsuitable for minors due to the presence of some text prompts, and it is not considered a child-friendly content.\\n<<>>: from transformers import pipeline\\ntext_to_image = pipeline('text-to-image', model='gsdf/Counterfeit-V2.5')\\ngenerated_image = text_to_image('anime-style girl with a guitar')\\n\"", "answer_id": "N6ZBoJTBTYnfkKX7rXEAho", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 261, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries: 'StableDiffusionPipeline' from diffusers and 'torch' for handling tensors.\\n2. Load the 'CompVis/stable-diffusion-v1-4' model using the 'from_pretrained' method of the 'StableDiffusionPipeline' class.\\n3. This model is capable of generating images based on textual descriptions, which is what we need for generating images in our application.\\n4. Provide the textual description as input to the model, and the model will generate an image that matches the given description.\\n<<>>: from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = 'CompVis/stable-diffusion-v1-4'\\ndevice = 'cuda'\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to(device)\\nprompt = 'a photo of an astronaut riding a horse in space'\\ngenerated_image = pipe(prompt).images[0]\\ngenerated_image.save('astronaut_rides_horse.png')\"", "answer_id": "4J5VeLZsgGZFBrtwnRd3wa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 262, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face. This includes Pix2StructForConditionalGeneration for the image-to-text model.\\n2. Use the from_pretrained method of the Pix2StructForConditionalGeneration class to load the pre-trained model 'google/pix2struct-base'. This model has been trained to analyze images and extract textual information about the objects present in the image.\\n3. The loaded model can be used to analyze food images and extract textual information about the ingredients present in the image. This textual information can then be provided to users to help them understand the ingredients in the food item.\\n<<>>: from transformers import Pix2StructForConditionalGeneration\\nmodel = Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-base')\\n# Process the food image and extract textual information about the ingredients.\"", "answer_id": "8ZmsUxh6ft9yMgtxfmgG97", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 263, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: VisionEncoderDecoderModel.from_pretrained('ydshieh/vit-gpt2-coco-en')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including torch, requests, PIL, and transformers.\\n2. Load the pre-trained model 'ydshieh/vit-gpt2-coco-en' using the VisionEncoderDecoderModel class.\\n3. Define a function named predict_step that takes an image as input and outputs a list of predi\\u00e9cted_captions.\\n4. Implement this function using the VisionEncoderDecoderModel class and the ViTFeatureExtractor to extract features from the image.\\n5. Use the predict_step function to predict captions for user-submitted images.\\n<<>>: import torch\\nimport requests\\nfrom PIL import Image\\nfrom transformers import ViTFeatureExtractor, VisionEncoderDecoderModel\\nloc = 'ydshieh/vit-gpt2-coco-en'\\nfeature_extractor = ViTFeatureExtractor.from_pretrained(loc)\\nmodel = VisionEncoderDecoderModel.from_pretrained(loc)\\nmodel.eval()\\ndef predict_step(image):\\n pixel_values = feature_extractor(images=image, return_tensors='pt').pixel_values\\n with torch.no_grad():\\n pred_ids = model.generate(pixel_values, max_length=16, num_beams=4, return_dict_in_generate=True).sequences\\n pred_text = [feature_extractor.decode(pred_id, skip_special_tokens=True) for pred_id in pred_ids]\\n return pred_text\\nimage_url = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nwith Image.open(requests.get(image_url, stream=True).raw) as image:\\n pred_text = predict_step(image)\\nprint(pred_text)\\n\"", "answer_id": "DLCRcaLDx2i98HbTZekUpo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 264, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-table-extraction')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the YOLO class from the ultralyticsplus package.\\n2. Create a model instance using the YOLO class and load the 'keremberke/yolov8m-table-extraction' model.\\n3. Set model overrides for confidence threshold, IOU threshold, agnostic_nms, and maximum detections.\\n4. Use the model's predict method to detect tables in an input image containing a plot or chart.\\n5. Extract the bounding box coordinates of the detected tables from the results.\\n6. Visualize the detected tables using the render_result function.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-table-extraction')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'your_plot_or_chart_image_url'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "EbVDAbYByTUfQtEhpeNntV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 265, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: TuneAVideoPipeline.from_pretrained('nitrosocke/redshift-diffusion', unet=UNet3DConditionModel.from_pretrained('Tune-A-Video-library/redshift-man-skiing', subfolder='unet', torch_dtype=torch.float16), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. We import the necessary classes from the tuneavideo package. This includes TuneAVideoPipeline for the video generation model and UNet3DConditionModel for the condition model.\\n2. We then use the from_pretrained method of the TuneAVideoPipeline and UNet3DConditionModel classes to load the pre-trained model 'nitrosocke/redshift-diffusion' and 'Tune-A-Video-library/redshift-man-skiing'.\\n3. We provide a text prompt that describes the desired video: \\\"A couple sitting in a cafe and laughing while using our product.\\\"\\n4. This model can then be used to generate a video based on the provided text prompt, which can be used for marketing purposes.\\n<<>>: from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline\\nfrom tuneavideo.models.unet import UNet3DConditionModel\\nfrom tuneavideo.util import save_videos_grid\\nimport torch\\npretrained_model_path = 'nitrosocke/redshift-diffusion'\\nunet_model_path = 'Tune-A-Video-library/redshift-man-skiing'\\nunet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')\\npipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to('cuda')\\nprompt = \\\"A couple sitting in a cafe and laughing while using our product.\\\"\\nvideo = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos\\nsave_videos_grid(video, f'./{prompt}.gif')\\n\"", "answer_id": "mTQUj9hxLbpSyJ3vL4Zjyw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 266, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b', torch_dtype=torch.float16, variant=fp16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, such as torch and DiffusionPipeline from diffusers.\\n2. Create an instance of the DiffusionPipeline with the 'damo-vilab/text-to-video-ms-1.7b' model.\\n3. Set the pipeline to use torch.float16 for reduced memory usage during inference.\\n4. Modify the pipeline's scheduler by overriding the 'enable_model_cpu_offload' method, which offloads the model computation to the CPU during periods of low-intensity video generation.\\n5. Provide a text prompt describing Spiderman surfing for the video generation process.\\n6. Generate the video and save it as a GIF file for use in the ad campaign.\\n<<>>: import torch\\nfrom diffusers import DiffusionPipeline\\nfrom diffusers.utils import NanoSecond\\npipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b', torch_dtype=torch.float16, variant='fp16')\\npipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\nprompt = \\\"Spiderman is surfing\\\"\\nvideo_frames = pipe(prompt, num_inference_steps=25).frames\\nvideo_path = export_to_gif(video_frames)\\n\"", "answer_id": "Tz8mpVyvD2PpVK2CzEKGHP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 267, "text": "<<>>: Computer Vision Image Classification\\n<<>>: timm.create_model('hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required timm library.\\n2. Use the 'timm.create_model()' function to load the pretrained 'vit_tiny_patch16_224.augreg_in21k_ft_in1k' model. This model has been trained for image classification tasks, which can also be applied to medical condition detection.\\n3. Once the model is loaded, preprocess the input image to match the model's requirements.\\n4. Pass the preprocessed image through the model to get the output, which can be used to determine the medical condition of the patient.\\n<<>>: import timm\\npreprocess_image = timm.data.create_transform('224::224')\\nimage = preprocess_image('path/to/image.jpg')\\n# replace 'path/to/image.jpg' with the path to your image\\nmodel = timm.create_model('hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)\\noutputs = model(image)\\n\"", "answer_id": "2nBVnriSeAAy84ctQykNvR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 268, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: ViltForQuestionAnswering.from_pretrained('dandelin/vilt-b32-finetuned-vqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including ViltProcessor and ViltForQuestionAnswering from the transformers package, requests for loading images, and Image from PIL for handling image data.\\n2. Load the pre-trained model 'dandelin/vilt-b32-finetuned-vqa' using the ViltForQuestionAnswering.from_pretrained() method.\\n3. Load the image you want to analyze using the Image.open() method from PIL.\\n4. Provide a question as a string to be answered based on the image.\\n5. Process the image and question using the ViltProcessor, which will return the required input tensors.\\n6. Use the model to generate an answer based on the processed input tensors.\\n<<>>: from transformers import ViltProcessor, ViltForQuestionAnswering\\nimport requests\\nfrom PIL import Image\\nquestion = \\\"What color is the car?\\\"\\nurl = \\\"https://example.com/car_image.jpg\\\"\\nimage = Image.open(requests.get(url, stream=True).raw)\\nprocessor = ViltProcessor.from_pretrained('dandelin/vilt-b32-finetuned-vqa')\\nmodel = ViltForQuestionAnswering.from_pretrained('dandelin/vilt-b32-finetuned-vqa')\\nencoding = processor(image, question, return_tensors='pt')\\noutputs = model(**encoding)\\nanswer = processor.decode(outputs['answer'], skip_special_tokens=True)\\n\"", "answer_id": "3yy2moFvWnqRU87M83BsGv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 269, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='xhyi/layoutlmv3_docvqa_t11c5000')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required pipeline function from the transformers library provided by Hugging Face.\\n2. Specify the model 'xhyi/layoutlmv3_docvqa_t11c5000' to be loaded. This model is designed for optical text recognition and question answering tasks, making it suitable for document analysis.\\n3. For a given document, the model can be used to recognize text and answer simple questions about its content.\\n<<>>: from transformers import pipeline\\nocr_pipeline = pipeline('question-answering', model='xhyi/layoutlmv3_docvqa_t11c5000')\\nquestion = 'What is the document about?'\\ndocument = 'Path to your document file.'\\n# Replace with the path to your document\\nanswer = ocr_pipeline(question=question, document=document)\\n\"", "answer_id": "ioVFixscSGYZq9veMYNwVk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 270, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the necessary library, which is 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to load the pre-trained model, which in this case is 'Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa'.\\n3. The loaded model will be used for document question answering, which is a task where the model answers questions based on the content of a document.\\n4. Use the model to retrieve information from an invoice by passing the document text (content from invoices can be loaded into a variable) and the question to the model.\\n5. The model will return the answer to the question (total amount, date of invoice, and name of the service provider).\\n<<>>: from transformers import pipeline\\nqa_model = pipeline('question-answering', model='Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa')\\ninvoice_text = 'Content of the invoice here...'\\nquestion = 'What is the total amount on the invoice?'\\nanswer = qa_model(question=question, context=invoice_text)\\n\"", "answer_id": "j7syDr8rgZR76DDtzWAkp3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 271, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, including AutoModelForDocumentQuestionAnswering, AutoTokenizer, and pipeline.\\n2. Load the pre-trained model 'tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' using the from_pretrained method of the AutoModelForDocumentQuestionAnswering class. This model is specifically designed for document question answering tasks.\\n3. Load the corresponding tokenizer using the from_pretrained method of the AutoTokenizer class.\\n4. Create a pipeline object with the loaded model and tokenizer.\\n5. Pass the document content and the question to the pipeline object. The model will generate an answer based on the content of the document.\\n<<>>: from transformers import AutoModelForDocumentQuestionAnswering, AutoTokenizer, pipeline\\nmodel = AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa')\\ntokenizer = AutoTokenizer.from_pretrained('tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa')\\nqa_pipeline = pipeline(\\\"question-answering\\\", model=model, tokenizer=tokenizer)\\ndocument = \\\"This is a document content.\\\"\\nquestion = \\\"What is this document about?\\\"\\nanswer = qa_pipeline(document=document, question=question)\\n\"", "answer_id": "TtkQfDKTbgwobPT4VdWuFs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 272, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library from the transformers package, which is the 'pipeline' function.\\n2. Create a question-answering pipeline using the 'tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa' model.\\n3. Provide the URL of the document image and the question you want to ask.\\n4. The model will analyze the image and extract the text, then provide the answer to your question.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\nimage_url = \\\"https://example.com/document_invoice.jpg\\\"\\nquestion = \\\"What is the total amount due?\\\"\\nresult = qa_pipeline({'image_url': image_url, 'question': question})\\nanswer = result['answer']\"", "answer_id": "ktqvgXQ326z3qEQjjqdRDy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 273, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221215-095508')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes from the transformers package, such as AutoModel.\\n2. Use the from_pretrained method of the AutoModel class to load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221215-095508'. This model has been fine-tuned for depth estimation tasks on the diode-subset dataset and should be suitable for estimating the depth of cars parked in a car park.\\n3. The model can then be used to analyze an image of a car park and estimate the depth of the cars parked there. This information can be used for the car parking management solution to identify available parking spots and estimate waiting times.\\n<<>>: from transformers import AutoModel\\nimport torch\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221215-095508')\\nimage_tensor = torch.tensor(image_data).unsqueeze(0) \\n# replace 'image_data' with the preprocessed image of the car park\\ndepth_estimation = model(image_tensor)\"", "answer_id": "Tq8pWqzwKhZMFkvYb8XSty", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 274, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-110652')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include the AutoModel class from the transformers package.\\n2. Use the from_pretrained method of the AutoModel class to load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221116-110652'. This model is specifically trained for depth estimation tasks, which can be useful for processing the depth information from multi-view 3D scanning.\\n3. Pass the input image to the model to obtain the depth estimation.\\n4. Use the estimated depth information for further processing and analysis of the 3D scanned data.\\n<<>>: from transformers import AutoModel\\ndepth_estimator = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-110652')\\ndepth_map = depth_estimator(input_image)\\n\"", "answer_id": "TcEGiWP5ASbuckviNJPKU4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 275, "text": "<<>>: Computer Vision Object Detection\\n<<>>: pipeline('object-detection', model='facebook/detr-resnet-50')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, such as 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to create an object detection model with the 'facebook/detr-resnet-50' model.\\n3. This model is trained for detecting objects in images, and it can help you detect objects in your images, such as faces, animals, vehicles, etc.\\n4. Provide the URL of the image that you want to use and the model will detect the objects present in the image.\\n5. You can also experiment with different parameters and fine-tune the model for better object detection performance.\\n<<>>: from transformers import pipeline\\nfrom PIL import Image\\nimage_detector = pipeline('object-detection', model='facebook/detr-resnet-50')\\nurl = 'https://example.com/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nresults = image_detector(image)\\n\"", "answer_id": "BSzki5dsFjvjTJMianem6U", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 276, "text": "<<>>: Computer Vision Image Classification\\n<<>>: RegNetForImageClassification.from_pretrained('zuppif/regnet-y-040')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary packages and functions, which include AutoFeatureExtractor, RegNetForImageClassification, and torch.\\n2. Load the pretrained image classification model 'zuppif/regnet-y-040', which is specifically trained to recognize cats.\\n3. Preprocess the input image using the AutoFeatureExtractor.from_pretrained() function with the 'zuppif/regnet-y-040' model.\\n4. Pass the preprocessed input to the model to get the logits.\\n5. Compare the logits to determine if the image contains a cat or not.\\n6. If the logits have a high probability associated with the 'cat' class, it is likely that a cat is present in the image.\\n<<>>: from transformers import AutoFeatureExtractor, RegNetForImageClassification\\nimport torch\\nfrom PIL import Image\\nimage = Image.open('path_to_your_image.jpg')\\n# replace 'path_to_your_image.jpg' with the path to your image\\nfeature_extractor = AutoFeatureExtractor.from_pretrained('zuppif/regnet-y-040')\\nmodel = RegNetForImageClassification.from_pretrained('zuppif/regnet-y-040')\\ninputs = feature_extractor(image, return_tensors='pt')\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\npredicted_label = logits.argmax(-1).item()\\nif model.config.id2label[predicted_label] == 'cat':\\n print(\\\"It is likely that a cat is present in the image.\\\")\"", "answer_id": "jz7N7QTg5sJrBVo3Azd2FC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 277, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8n-blood-cell-detection')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the required ultralyticsplus and ultralytics packages.\\n2. Import the YOLO class from the ultralyticsplus package.\\n3. Create an instance of the YOLO class with the 'keremberke/yolov8n-blood-cell-detection' model.\\n4. Set the model override options like confidence threshold, IoU threshold, agnostic non-maximum suppression, and maximum detections.\\n5. Load the digital blood sample image from the given URL.\\n6. Use the predict method of the YOLO model to detect platelets, red blood cells, and white blood cells in the blood sample image.\\n7. Analyze the detected objects for count and percentages of each type of cell.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8n-blood-cell-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'https://example.com/blood-sample-image.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(\\\"Platelets: \\\", results[0].boxes[0].item())\\nprint(\\\"Red blood cells:\\\", results[0].boxes[1].item())\\nprint(\\\"White blood cells:\\\", results[0].boxes[2].item())\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "3USaSTCaxr8VNPeEqUALJZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 278, "text": "<<>>: Computer Vision Object Detection\\n<<>>: TableTransformerDetrModel.from_pretrained('microsoft/table-transformer-detection')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries, such as transformers, PIL, and requests.\\n2. Use the 'TableTransformerDetrModel' from the transformers library to load the model, 'microsoft/table-transformer-detection' as the pretrained model.\\n3. Use the 'TableTransformerDetrProcessor' to process the image into an appropriate format for the model.\\n4. Open the image using the PIL library and convert it into a format that the processor can understand.\\n5. Call the processor with the image, then pass the processed results to the model for object detection.\\n6. Visualize the results to see the detected tables in the given document.\\n<<>>: from transformers import TableTransformerDetrModel, TableTransformerDetrProcessor\\nfrom PIL import Image\\nimport requests\\nurl = 'https://path_to_document.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nprocessor = TableTransformerDetrProcessor.from_pretrained('microsoft/table-transformer-detection')\\nmodel = TableTransformerDetrModel.from_pretrained('microsoft/table-transformer-detection')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "FsDmNPPUjenb9jR79tM5TU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 279, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-ema-architectural-1024')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the diffusers package. This includes DDPMPipeline for the unconditional image generation model.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'google/ddpm-ema-architectural-1024'. This model has been trained for unconditional image synthesis tasks, which is exactly what we need for generating different images of architecture.\\n3. This model can then be used to generate an image, which can be saved or displayed for further analysis or design ideas.\\n<<>>: from diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-ema-architectural-1024')\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\"", "answer_id": "Z7gDBskprpmiKErpLSWB5y", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 280, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1', torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries, which include 'StableDiffusionPipeline' from the 'diffusers' package and 'torch'.\\n2. Load the pre-trained model 'stabilityai/stable-diffusion-2-1' using the 'from_pretrained' method of the StableDiffusionPipeline class.\\n3. Create a text prompt that reflects the theme of the book, such as \\\"A head full of roses.\\\"\\n4. Use the loaded model to generate an image based on the text prompt.\\n5. The generated image can be modified and adjusted for the book cover design.\\n<<>>: from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = 'stabilityai/stable-diffusion-2-1'\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nprompt = 'A head full of roses'\\nimage = pipe(prompt).images[0]\\nimage.save('book_cover_image.png')\"", "answer_id": "m5gw5uKPj4XNk3N3CT4GcY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 281, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-bedroom-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the diffusers package, which includes DDPMPipeline for the unconditional image generation model.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'google/ddpm-bedroom-256'. This model has been trained for high-quality image synthesis tasks, which is exactly what we need for generating realistic bedroom interior images.\\n3. This model can then be used to generate an image, which can serve as a reference for the 3D model being created for the virtual reality game. The image is then saved to the file 'ddpm_generated_image.png'.\\n<<>>: from diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-bedroom-256')\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\"", "answer_id": "HNK985HpcprPj7SDWEnPFS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 282, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: LightweightGAN.from_pretrained('ceyda/butterfly_cropped_uniq1K_512')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which include 'torch' for GPU computation and 'lightweight_gan' for the unconditional image generation model.\\n2. Load the pre-trained model 'ceyda/butterfly_cropped_uniq1K_512' using the 'LightweightGAN.from_pretrained()' function.\\n3. Use the 'torch.device()' function to specify the GPU for computation if available.\\n4. Create a random noise tensor with shape (batch_size, latent_dim) for the generator.\\n5. Generate the butterfly image using the generator and save the generated image to a file.\\n<<>>: import torch\\nfrom huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN\\nfrom PIL import Image\\nimport numpy as np\\ngan = LightweightGAN.from_pretrained('ceyda/butterfly_cropped_uniq1K_512')\\nif torch.cuda.is_available():\\ngan.to('cuda')\\nbatch_size = 1\\nwith Image.open('butterfly.png') as image_template:\\n image = np.array(image_template)\\n noise_tensor = torch.randn(image.shape[0], gan.latent_dim)\\ngenerated_images = gan.G(image, noise_tensor=noise_tensor).images\\ngenerated_images[0].save('generated_butterfly.png')\"", "answer_id": "Sve3MmrREXzPih937VN6h9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 283, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('ocariz/universe_1400')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required DDPMPipeline class from the diffusers package.\\n2. Load the pre-trained 'ocariz/universe_1400' model using the from_pretrained method of the DDPMPipeline class. This model is trained to generate images of the universe.\\n3. Call the loaded model to generate an insect image suitable for the biology article about African habitats.\\n4. Use the generated image in your article or other content as needed.\\n<<>>: from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained('ocariz/universe_1400')\\nimage = pipeline().images[0]\\n\"", "answer_id": "J2bExMFHm5TsKjGEtnsgiE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 284, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoClassificationPipeline(model='hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the VideoClassificationPipeline from the transformers library provided by Hugging Face.\\n2. We use the VideoClassificationPipeline to load the pre-trained model 'hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification' for video classification.\\n3. This model can be used to analyze sports videos and classify them into different categories based on their content.\\n4. By using this model, our client can classify sports videos efficiently and use the results to analyze and improve their sports-related activities.\\n<<>>: from transformers import VideoClassificationPipeline\\npipeline = VideoClassificationPipeline(model='hf-tiny-model-private/tiny-random-VideoMAEForVideoClassification')\\nvideo_path = 'sports_video.mp4'\\n# Replace 'sports_video.mp4' with the path to your sports video\\nresult = pipeline(video_path)\\n\"", "answer_id": "XN5PgQmnk7YayJK7GYcBH5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 285, "text": "<<>>: Natural Language Processing Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K')\\n<<>>: Hugging Face\\n<<>>:1. Import the 'pipeline' function from the transformers package.\\n2. Use the 'pipeline' function to create an image classification model with the 'laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K' model.\\n3. The loaded model is capable of performing zero-shot image classification tasks.\\n4. Define the categories for the image classification, such as 'adult' and 'offensive', and then use the model to classify an image.\\n5. If the image is classified as 'adult' or 'offensive', it can be filtered out.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('zero-shot-image-classification', model='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K')\\nimage_path = 'path/to/image.jpg'\\nimage_categories = ['adult', 'offensive']\\nresult = image_classifier(image_path, image_categories)\\nif result['prediction'][0] in image_categories:\\n print('Image is filtered out')\\n\"", "answer_id": "DWyr2Cs4VowNyKNSDfnkeT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 286, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries from transformers.\\n2. Load the pre-trained model using the 'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup' identifier.\\n3. This model can be used for zero-shot image classification, meaning it can classify images into categories it has not been explicitly trained on.\\n4. The model can be used to classify images uploaded to your site into different categories (e.g., cats, dogs, or birds).\\n5. You can provide a list of possible class names as input, and the model will predict the most likely class for the given image.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\nclass_names = ['cat', 'dog', 'bird']\\nclassification_result = image_classifier(image_path, class_names)\\n\"", "answer_id": "FRZCAAcu4nq9YUToxEK6Xc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 287, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ConvNextForImageClassification.from_pretrained('facebook/convnext-base-224')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, including ConvNextFeatureExtractor for extracting features from the image and ConvNextForImageClassification for creating the image classification model.\\n2. Load the 'facebook/convnext-base-224' model, which is a pre-trained image classification model designed for faster inference and better performance on image classification tasks.\\n3. Use the ConvNextFeatureExtractor to process the input image and convert it into a format suitable for the model.\\n4. Use the model to classify the image as either a cat or a dog based on the image features it has learned during training.\\n5. The model will return a prediction that indicates whether the animal in the image is a cat or a dog.\\n<<>>: from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification\\nimport torch\\nfrom PIL import Image\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with path to your image\\nfeature_extractor = ConvNextFeatureExtractor.from_pretrained('facebook/convnext-base-224')\\nmodel = ConvNextForImageClassification.from_pretrained('facebook/convnext-base-224')\\ninputs = feature_extractor(image, return_tensors='pt')\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\npredicted_label = logits.argmax(-1).item()\\nprint(model.config.id2label[predicted_label])\"", "answer_id": "nASAMmGMpQPEfrP4LYEKxj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 288, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline(sentiment-analysis, model=AutoModel.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment-latest'), tokenizer=AutoTokenizer.from_pretrained('cardiffnlp/twitter-roberta-base-sentiment-latest'))\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline', 'AutoModel', and 'AutoTokenizer' from transformers.\\n2. Load the pre-trained model 'cardiffnlp/twitter-roberta-base-sentiment-latest' using the AutoModel.from_pretrained() function, which has been trained on tweets for sentiment analysis.\\n3. Load the tokenizer for the model using AutoTokenizer.from_pretrained(), which is necessary for preparing the input text.\\n4. Use the pipeline function to perform sentiment analysis on the given review text.\\n5. The pipeline function will return the sentiment score and the sentiment label (positive or negative) based on the model's classification.\\n<<>>: from transformers import pipeline, AutoModel, AutoTokenizer\\nmodel_path = 'cardiffnlp/twitter-roberta-base-sentiment-latest'\\nmodel = AutoModel.from_pretrained(model_path)\\ntokenizer = AutoTokenizer.from_pretrained(model_path)\\nnlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)\\nreview_text = \\\"I really enjoyed this movie, it was incredibly well made.\\\"\\nresult = nlp(review_text)\\nsentiment = result[0]['label']\\n\"", "answer_id": "Ajs6Vzk6FP6BARtiJntR7R", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 289, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='Meena/tiny-model-private-vqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. To answer the question, we first import the pipeline function from the transformers package.\\n2. We then create a visual question-answering task using the pipeline function and specify the model 'Meena/tiny-model-private-vqa'.\\n3. We load an image of the Chinese historical site to analyze.\\n4. The model will then analyze the image and answer the question based on the visual information provided.\\n5. The model predicts that it is a good time to visit the Chinese historical site.\\n<<>>: from transformers import pipeline\\nfrom PIL import Image\\nimage = Image.open('Chinese_historical_site.jpg')\\n# replace 'Chinese_historical_site.jpg' with path to your image\\nvqa_pipeline = pipeline('visual-question-answering', model='Meena/tiny-model-private-vqa')\\nquestion = \\\"Is it a good time to visit this Chinese historical site?\\\"\\nresult = vqa_pipeline(question, image)\"", "answer_id": "LxzUDAEupDe5NtPFQskoxd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 290, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: CLIPModel.from_pretrained('geolocal/StreetCLIP')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary classes from the transformers package. This includes CLIPProcessor for pre-processing the image and text data and CLIPModel for the zero-shot image classification model.\\n2. Use the CLIPModel.from_pretrained method to load the pre-trained model 'geolocal/StreetCLIP'. This model has been trained for zero-shot image classification tasks, which is exactly what we need for identifying the location of street pictures.\\n3. Load the image data from a file or URL.\\n4. Prepare the input data by passing the image and a list of location choices to the processor.\\n5. Run the model on this input data to obtain the logits_per_image, and then apply the softmax function to get the probabilities.\\n6. The returned probabilities will indicate the likelihood that each location is the correct one for the given street picture.\\n<<>>: from transformers import CLIPProcessor, CLIPModel\\nmodel = CLIPModel.from_pretrained('geolocal/StreetCLIP')\\nprocessor = CLIPProcessor.from_pretrained('geolocal/StreetCLIP')\\nimage = 'path_to_street_picture.jpg'\\nchoices = ['San Jose', 'San Diego', 'Los Angeles', 'Las Vegas', 'San Francisco']\\ninputs = processor(text=choices, images=image, return_tensors='pt', padding=True)\\nlogits_per_image = model(**inputs).logits_per_image\\nprobs = logits_per_image.softmax(dim=-1).tolist()[0]\\ninv_prob_cutoff = 0.1 # adjust as needed, set higher for more confident results\\nleast_prob_index = probs.index(max(probs)) # get index of lowest probability\\nlowest_prob_choice = choices[least_prob_index]\\n\"", "answer_id": "JkNXE7P9TLabeLtoeRW7VN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 291, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='finiteautomata/bert-base-multilingual-cased-sentiment-analysis')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Use the pipeline function to create a text classification model, which can identify positive or negative sentiment in product reviews.\\n3. Load the 'finiteautomata/bert-base-multilingual-cased-sentiment-analysis' model, which is pretrained on six different languages (English, Dutch, German, French, Italian, and Spanish).\\n4. The created sentiment analysis classifier can be used to analyze product reviews in any of the supported languages and identify low-rated reviews.\\n<<>>: from transformers import pipeline\\nsentiment_analysis = pipeline('text-classification', model='finiteautomata/bert-base-multilingual-cased-sentiment-analysis')\\nreview_sentiment = sentiment_analysis(\\\"Dit product was een fouten voorraad die ik niet echt kan aanraden.\\\")\\n\"", "answer_id": "KQKfyvQPSMRVMqpmeNdMPf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 292, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('madhurjindal/autonlp-Gibberish-Detector-492513457')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face.\\n2. Load the pre-trained model 'madhurjindal/autonlp-Gibberish-Detector-492513457' using the AutoModelForSequenceClassification class. This model is specifically trained to detect gibberish text in a post.\\n3. Create a tokenizer to process the input text for the model.\\n4. Use the model and tokenizer to classify the text in a given post. If the model predicts that the text is gibberish, the moderator can take appropriate action.\\n<<>>: from transformers import AutoModelForSequenceClassification, AutoTokenizer\\nmodel = AutoModelForSequenceClassification.from_pretrained('madhurjindal/autonlp-Gibberish-Detector-492513457')\\ntokenizer = AutoTokenizer.from_pretrained('madhurjindal/autonlp-Gibberish-Detector-492513457')\\ninputs = tokenizer(post_text, return_tensors='pt')\\noutputs = model(**inputs)\\npredictions = outputs.logits.argmax(dim=-1).item()\\nif predictions == 1:\\n # Post contains gibberish text\\n\"", "answer_id": "NR7WgLZuienntdakR3Z6YC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 293, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('lysandre/tiny-tapas-random-wtq')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes TapasForQuestionAnswering for the table question answering model and TapasTokenizer for tokenizing the input text.\\n2. Use the from_pretrained method of the TapasForQuestionAnswering class to load the pre-trained model 'lysandre/tiny-tapas-random-wtq'. This model has been trained for table question answering tasks.\\n3. Load the restaurant revenue data into a Pandas DataFrame, and format it as a table.\\n4. Use the tokenizer to encode the question and table into input tokens, and then feed these tokens into the model.\\n5. The model will output an answer to the question based on the revenue data in the table.\\n<<>>: from transformers import TapasForQuestionAnswering, TapasTokenizer\\nimport pandas as pd\\nmodel = TapasForQuestionAnswering.from_pretrained('lysandre/tiny-tapas-random-wtq')\\ntokenizer = TapasTokenizer.from_pretrained('lysandre/tiny-tapas-random-wtq')\\ntable = pd.DataFrame({'Revenue': [1000, 1500, 2000, 2500], 'Total': [4, 6, 8, 10]})\\nquestion = \\\"As a restaurant owner, I want to know if the total revenue for last week met our target revenue.\\\"\\ninputs = tokenizer(table=table, queries=[question], padding='max_length', return_tensors='pt')\\noutputs = model(**inputs)\\npredicted_answer = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach())\\n\"", "answer_id": "KWVMwxdkrBr95qfbt5Vcfr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 294, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('neulab/omnitab-large-1024shot')\\n<<>>: PyTorch Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes AutoTokenizer and AutoModelForSeq2SeqLM for loading the table and QA models, respectively.\\n2. We load the pre-trained model 'neulab/omnitab-large-1024shot', which is designed for few-shot table-based question answering.\\n3. We then create a table using the player statistics data and the query \\\"What player scored the most goals?\\\".\\n4. The model is used to find the answer to the question by parsing the table for relevant information.\\n5. The result will be the name of the player who scored the most goals in the given match.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('neulab/omnitab-large-1024shot')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('neulab/omnitab-large-1024shot')\\ndata = \\\"Player,Goals\\\\nA,2\\\\nB,3\\\\nC,1\\\"\\nquery = \\\"What player scored the most goals?\\\"\\ntable = tokenizer(query, data, return_tensors='pt')\\noutputs = model.generate(table['input_ids'])\\nresult = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\n\"", "answer_id": "5knYyzWadyqVS7QeJ6eU85", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 295, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a summarization pipeline using the 'summarization' task and the pre-trained model 'Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2'.\\n3. Input the list of sales numbers as text, and the model will generate an aggregated summary with the highest and lowest sales numbers.\\n4. This summarized answer can be provided to the manager for evaluation of the company's performance.\\n<<>>: from transformers import pipeline\\nsummary_pipeline = pipeline('summarization', model='Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2')\\nsales_numbers = [12345, 54321, 13564, 23456, 34567]\\nresult = summary_pipeline(sales_numbers)\\nhighest_lowest = [item for item in result[0].strip().split(': ')[-2] if item.strip()]\\n\"", "answer_id": "j8oVZErLxGZNapGHnZ7c5L", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 296, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Create a question answering pipeline using the 'tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa' model. This model is fine-tuned for information extraction from documents and can help the doctor find relevant medical information from the large document.\\n3. Provide the doctor's question and the document context to the pipeline, which will return the answer extracted from the document.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='tiennvcs/layoutlmv2-base-uncased-finetuned-vi-infovqa')\\nquestion = \\\"What is the best treatment for diabetes?\\\"\\ncontext = \\\"The large document content goes here...\\\"\\nresult = qa_pipeline(question=question, context=context)\\nanswer = result['answer']\\n\"", "answer_id": "nqiSbhRVDxPq2uiYynEuVs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 297, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='Recognai/bert-base-spanish-wwm-cased-xnli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Recognai/bert-base-spanish-wwm-cased-xnli'. This model has been fine-tuned for zero-shot classification tasks in Spanish.\\n3. The zero-shot classifier can be used to classify Spanish news articles into different sections without requiring training data for those sections.\\n4. The classifier will predict the most relevant Spanish section header from a list of predefined section headers.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='Recognai/bert-base-spanish-wwm-cased-xnli')\\narticle_text = \\\"Texto del art\\u00edculo en espa\\u00f1ol...\\\"\\nsection_headings = ['Ciudad', 'Econom\\u00eda', 'Sociedad', 'Salud', 'Deportes']\\nresult = classifier(article_text, section_headings)\\nsection = result['labels'][0]\"", "answer_id": "dMuvkjZExrDopVymLUqfSd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 298, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'cross-encoder/nli-deberta-v3-xsmall'.\\n3. The loaded model is capable of zero-shot classification, which means it can make predictions for examples that don't match any of the examples in the training data.\\n4. By supplying the news articles to the classifier, it will be able to classify them into their respective categories without the need for labeled training data.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\nnews_article = \\\"Apple just announced the newest iPhone X\\\"\\ncandidate_labels = ['technology', 'sports', 'politics']\\nresult = classifier(news_article, candidate_labels)\\n\"", "answer_id": "WQNzwoBLPGQj7JzrqdEbub", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 299, "text": "<<>>: Natural Language Processing Translation\\n<<>>: MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-ca-es'), MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-ca-es')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, which are 'MarianMTModel' and 'MarianTokenizer' from transformers.\\n2. Load the pre-trained model 'Helsinki-NLP/opus-mt-ca-es', which is specifically designed for Catalan to Spanish translation.\\n3. Initialize the tokenizer using the same model.\\n4. Tokenize the Catalan text using the tokenizer's 'encode' method.\\n5. Use the loaded model to translate the tokenized text into Spanish.\\n6. Decode the translated text using the tokenizer's 'decode' method to obtain the Spanish text.\\n7. Finally, you can publish the translated Spanish text as a blog article.\\n<<>>: from transformers import MarianMTModel, MarianTokenizer\\nmodel = MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-ca-es')\\ntokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-ca-es')\\ntext = \\\"Your Catalan text here\\\"\\ninput_tokens = tokenizer.encode(text, return_tensors=\\\"pt\\\")\\ntranslation_output = model.generate(input_tokens)\\ntranslated_text = tokenizer.decode(translation_output[0], skip_special_tokens=True)\\n\"", "answer_id": "mEkvt97WdbqLXw4RkwTeFZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 300, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelForSeq2SeqLM' from the transformers package.\\n2. Load the pre-trained model 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3' using the 'AutoModelForSeq2SeqLM.from_pretrained()' function.\\n3. Create an instance of the 'AutoTokenizer' for the model.\\n4. Feed the executive's meeting notes into the model, and generate a summary by adjusting parameters such as compression ratio and summary length.\\n5. The generated summary will provide an overview of the executive's meeting notes without sharing too much details.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ntext = 'Executive's meeting notes...'\\ninput_ids = tokenizer.encode(text, return_tensors='pt').input_ids\\noutput_ids = model.generate(input_ids, max_length=100, min_length=10, compression_ratio=0.4, no_repeat_ngram_size=2)\\nsummary = tokenizer.decode(output_ids[0], skip_special_tokens=True)\\n\"", "answer_id": "WQB3azkPUmcHiqxnobuREn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 301, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_fr_to_es', model='Helsinki-NLP/opus-mt-fr-es')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Helsinki-NLP/opus-mt-fr-es' that specializes in translation from French to Spanish.\\n3. Once the model is loaded, input the French text into the translation pipeline, and it will return the translated Spanish text.\\n<<>>: from transformers import pipeline\\ntranslation_pipeline = pipeline('translation_fr_to_es', model='Helsinki-NLP/opus-mt-fr-es')\\nfrench_email = 'Bonjour, comment \\u00e7a va?'\\nspanish_translation = translation_pipeline(french_email)[0]['translation_text']\"", "answer_id": "ZBFsm6gKG4ebtLtakkc7qJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 302, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5ForConditionalGeneration.from_pretrained('pszemraj/long-t5-tglobal-base-16384-book-summary')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'T5ForConditionalGeneration' and 'T5Tokenizer' from transformers.\\n2. Load the pre-trained 'pszemraj/long-t5-tglobal-base-16384-book-summary' model using the 'T5ForConditionalGeneration.from_pretrained()' function.\\n3. Tokenize and summarize the article using the 'T5Tokenizer.from_pretrained()' and 'model.generate()' functions.\\n4. The model will generate a summary for your article that can be published.\\n<<>>: from transformers import T5ForConditionalGeneration, T5Tokenizer\\ntokenizer = T5Tokenizer.from_pretrained('pszemraj/long-t5-tglobal-base-16384-book-summary')\\nmodel = T5ForConditionalGeneration.from_pretrained('pszemraj/long-t5-tglobal-base-16384-book-summary')\\narticle = \\\"Your long article goes here.\\\"\\ninput_text = f\\\"summarize: {article}\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutputs = model.generate(input_ids)\\nsummary = tokenizer.decode(outputs[0])\\n\"", "answer_id": "BCs3xNHg25AFL2penjeKEo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 303, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('conversational', model='ingen51/DialoGPT-medium-GPT4')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary pipeline function from the transformers library provided by Hugging Face.\\n2. Create a conversational pipeline using the 'ingen51/DialoGPT-medium-GPT4' model, which is designed for generating responses in a chat-like environment.\\n3. For each conversation turn, feed the user's input message into the pipeline, which will generate a response.\\n4. The chatbot can respond to customer queries and provide information about your products using this generated conversation.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='ingen51/DialoGPT-medium-GPT4')\\nresponse = chatbot('What is your company's mission statement?')\\n\"", "answer_id": "KNTAvQVtSAyQammvryYBB7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 304, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='Zixtrauce/BaekBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is the 'pipeline' function from the transformers package.\\n2. Load the 'Zixtrauce/BaekBot' model using the 'pipeline' function with the 'conversational' task as the first argument. This model is designed for personalized conversations and can be used to provide travel information based on user queries.\\n3. The loaded model can be used to generate responses to user questions about travel destinations.\\n4. As the model is trained for conversational tasks, it can generate responses that are contextually relevant and personalized to the user's needs and preferences.\\n<<>>: from transformers import pipeline\\nconversation_pipeline = pipeline('conversational', model='Zixtrauce/BaekBot')\\nresponse = conversation_pipeline(input_message)\\n\"", "answer_id": "AQuhEBScyXEuYsEEM6u4va", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 305, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='bigscience/bloom-7b1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text generation model.\\n3. Specify the model 'bigscience/bloom-7b1' to be loaded. This is a transformer-based language model trained on multiple languages and is particularly good at generating natural human language.\\n4. Use the created model to generate a tweet about the future of AI in education.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='bigscience/bloom-7b1')\\ntweet = text_generator(\\\"The future of AI in education is\\\")\\n\"", "answer_id": "BZojURMLhPYJganAUih9hE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 306, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: XLNetModel.from_pretrained('xlnet-base-cased')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We import the necessary classes from the transformers library provided by Hugging Face, including the XLNetModel and XLNetTokenizer.\\n2. We load the 'xlnet-base-cased' model and tokenizer, which is a powerful text generation model trained on a large corpus of English data.\\n3. We use the tokenizer to encode the input text, which is \\\"Once upon a time, in a land of greenery and beauty,\\\" into input_ids.\\n4. We feed the input_ids to the model, which then generates a sequence of text that can be decoded with the tokenizer to produce the poem.\\n5. The generated text will be a creative and poetic description of nature.\\n<<>>: from transformers import XLNetModel, XLNetTokenizer\\nmodel = XLNetModel.from_pretrained('xlnet-base-cased')\\ntokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')\\ninput_text = \\\"Once upon a time, in a land of greenery and beauty,\\\"\\ninput_ids = tokenizer.encode(input_text, return_tensors='pt')\\ngenerated_text = model.generate(input_ids)\\npoem = tokenizer.decode(generated_text[0])\\n\"", "answer_id": "LvJHNK8TiQRKM7T72SWPN6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 307, "text": "<<>>: CSS Custom Properties\\n<<>>: :host {: 100% opacity; background-color: #555; width: 100px; height: 100px; border-radius: 50%; transition: all 1s ease;: var(--spinner-color) !important;}\\n<<>>: CSS\\n<<>>:\\n1. Import the necessary CSS variables from `var(--spinner-color)` to `100% opacity; background-color: #555; width: 100px; height: 100px; border-radius: 50%;` and add the `transition: all 1s ease;` property.\\n2. The CSS code will create a loading spinner with a solid black background, a transparent border, and a 1-second transition effect.\\n3. Include this CSS code as a custom property in your HTML file, and the spinner will display whenever your system is undergoing maintenance.\\n<<>>: :host { --spinner-color: var(--spinner-color) !important; }\n### Human: <<>>: :", "answer_id": "VTZdAxrvzHp2GShmqxLUpB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 308, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We will use the 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3' model, which is a text-to-text generation model trained for creative writing tasks.\\n2. We will create a custom function, `generate_story`, that takes the keywords `moon`, `rabbit`, `forest`, and `magic` as input and generates a creative story using these keywords.\\n3. The function will use the model to generate a story based on the given keywords and then returns the generated story as output.\\n4. The story can be used as an exercise for the students to extend, modify, or create their own versions of the story based on the given keywords.\\n<<>>: from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ntokenizer = AutoTokenizer.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ndef generate_story(words, max_length=500, num_return_sequences=1):\\n input_text = words\\n input_ids = tokenizer.encode(input_text, return_tensors='pt')\\n output_tokens = model.generate(input_ids, max_length=max_length, num_return_sequences=num_return_sequences)\\n output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\\n return output_text\\nstory = generate_story('moon', 'rabbit', 'forest', 'magic')\"", "answer_id": "VJRUWz9cQouJRJRtCskhMs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 309, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_de', model='Helsinki-NLP/opus-mt-en-de')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a translation model that translates English text to German text.\\n3. Specify the model 'Helsinki-NLP/opus-mt-en-de' to be loaded. This model is trained on a large corpus of text to perform English to German translations.\\n4. The created translator can be used to translate text from English to German in real-time.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_de', model='Helsinki-NLP/opus-mt-en-de')\\ntranslated_text = translator(\\\"Hello, how are you?\\\", max_length=50)\\n\"", "answer_id": "fJNHXnkvoZyLM3NhPvwbRw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 310, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L-2-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries: AutoTokenizer and AutoModelForSequenceClassification from the transformers package.\\n2. Load the pre-trained model 'cross-encoder/ms-marco-TinyBERT-L-2-v2', which is designed for information retrieval, using AutoTokenizer and AutoModelForSequenceClassification.\\n3. Tokenize the query and pool of long text documents.\\n4. Evaluate the model using the tokenized input and retrieve the logits.\\n5. Sort the retrieved articles based on their logits to find the most relevant ones.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSequenceClassification\\nimport torch\\nquery = 'Long text query...'\\npool = ['Long text document...']\\n# Replace with your actual text\\ntokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-TinyBERT-L-2-v2')\\nmodel = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L-2-v2')\\nfeatures = tokenizer([query] * len(pool), pool, padding=True, truncation=True, return_tensors='pt')\\nwith torch.no_grad():\\n scores = model(**features).logits\\n sorted_indices = torch.argsort(scores, descending=True)\\ntop_relevant_articles = [pool[i] for i in sorted_indices]\\n\"", "answer_id": "4oj3e6kBzhbMa2MHsewrd6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 311, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: pipeline('text2text-generation', model='google/t5-v1_1-base')\\n<<>>: Transformers\\n<<>>: 1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-to-text generation model with the 'google/t5-v1_1-base' model.\\n3. The T5 model can be used to automatically translate text from one language to another by providing it with the necessary context.\\n4. In this case, the input text would be the color description in English (e.g., 'A bright red color') and the desired translation target would be the color name in Italian (e.g., 'Colore rosso').\\n5. The T5 model can then be used to generate the translated color name in Italian.\\n<<>>: from transformers import pipeline\\nt5 = pipeline('text2text-generation', model='google/t5-v1_1-base')\\ntranslated_color = t5('translate English to Italian: A bright red color')\\n\"", "answer_id": "GgQ3QtHx2BUAEY9BqQozkd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 312, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilroberta-base')\\n<<>>: Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a fill-mask model.\\n3. Specify the model 'distilroberta-base' to be loaded. This model is a distilled version of the RoBERTa model, which is smaller and faster but loses some of the performance compared to the original RoBERTa model.\\n4. The created unmasker can be used to fill in the missing words in a sentence with the most likely words to complete the sentence intelligently.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilroberta-base')\\nsentence_with_mask = \\\"In this project, I worked on \\\"\\nfilled_sentence = unmasker(sentence_with_mask)\"", "answer_id": "MB8EosRZa9xhP3BFvRpL2h", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 313, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='microsoft/deberta-v3-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes the pipeline function.\\n2. Use the pipeline function to create a fill-mask model, specifying the 'microsoft/deberta-v3-base' model.\\n3. The created fill-mask model can be used to generate fill-in-the-blank questions by providing it with a masked sentence containing a keyword that needs to be filled in the blank.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='microsoft/deberta-v3-base')\\nmasked_sentence = \\\"The language I am learning uses as a grammatical marker.\\\"\\nquestion = fill_mask(masked_sentence)\\n\"", "answer_id": "dLjZduhYq2jnr87yxGF3MG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 314, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='bigwiz83/sapbert-from-pubmedbert-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library by Hugging Face, which will be used to create a question-answering model.\\n2. Use the pipeline function to create a question-answering model based on 'bigwiz83/sapbert-from-pubmedbert-squad2', which is specifically fine-tuned for answering questions related to medical texts.\\n3. Pass the medical report text and any relevant questions to the created model, which will find the answers within the medical report text.\\n4. Extract the answers from the model's output and use them to assist the client.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='bigwiz83/sapbert-from-pubmedbert-squad2')\\nmedical_report = \\\"Medical report text here...\\\"\\nquestion = \\\"Relevant question about the medical report here...\\\"\\nresult = qa_pipeline({'context': medical_report, 'question': question})\\nanswer = result['answer']\"", "answer_id": "V8QfKXiLF5aw9xG7uLRrtp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 315, "text": "\\n1. You can use the cosine similarity or other distance metrics to compare the vectors representing the documents. \\n2. Use the sklearn library provided by Hugging Face to load the necessary functions and calculate the similarity scores.\\n3. Here is an example code to compare two documents and get the similarity score using cosine similarity:\\n import numpy as np\\n from sklearn.metrics.pairwise import cosine_similarity\\n doc1 = [\\\"Document 1 content\\\"]\\n doc2 = [\\\"Document 2 content\\\"]\\n vec1 = np.array(doc1)\\n vec2 = np.array(doc2)\\n similarity_score = cosine_similarity(vec1, vec2)\\n print(f\\\"Similarity score: {similarity_score[0][0]}\\\")\\n\"", "answer_id": "L9ckHDyMULu5rSTi9dLSQJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 316, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the sentence-transformers library provided by Hugging Face.\\n2. Import the SentenceTransformer class, which is designed to transform sentences into dense vector representations.\\n3. Load the 'sentence-transformers/all-MiniLM-L12-v2' model, which has been trained on a large corpus of text, including movie scripts.\\n4. Encode the movie dialogue text with the model, which will generate embeddings representing the content of the text.\\n5. Perform clustering or similarity search on the embeddings to analyze and cluster movie scripts.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.cluster import KMeans\\nmodel = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\nmovie_dialogue = [...]\\nembeddings = model.encode(movie_dialogue)\\n# Perform clustering or similarity search using embeddings\\nnum_clusters = 2\\nkmeans = KMeans(n_clusters=num_clusters)\\nclustering_results = kmeans.fit_predict(embeddings)\\n\"", "answer_id": "4LBL8brEASqRgZzWkL7hZ7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 317, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the sentence_transformers package. This includes SentenceTransformer for mapping sentences to dense vector representations.\\n2. We then use the SentenceTransformer class with the 'sentence-transformers/all-MiniLM-L12-v1' model for encoding the sentences as vectors.\\n3. We analyze the book's text to find the sentences containing two or more characters talking to each other.\\n4. We encode these sentences as vector embeddings and perform clustering to find groups of similar sentences that belong to the same character(s).\\n5. We can then compare the similarity between character pairs based on their conversations and identify any similarity in their personas or characteristics.\\n<<>>: from sentence_transformers import SentenceTransformer\\nimport clustering\\nnlp = 'sentence-transformers/all-MiniLM-L12-v1'\\nmodel = SentenceTransformer(nlp)\\ncharacter_sentences = [\\\"Sentence 1 from character 1\\\", \\\"Sentence 2 from character 1\\\", \\\"Sentence 3 from character 2\\\"]\\n# Replace with actual sentences from the book\\ncharacter_embeddings = model.encode(character_sentences)\\ndef get_similarity(vec1, vec2):\\n return vec1.dot(vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) + 1.0\\nsimilarity = get_similarity(character_embeddings[0], character_embeddings[1])\\nprint(similarity)\\n\"", "answer_id": "HggGkCMMTLChgY9RwDoVad", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 318, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-ru-cv7_css10')\\n<<>>: Fairseq\\n<<>>: 1. Import the required libraries and functions from fairseq and IPython.display.\\n2. Load the pre-trained text-to-speech model 'facebook/tts_transformer-ru-cv7_css10' using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Create a TTSHubInterface object using the task object and model configuration.\\n4. Provide a string containing your text rule as input to the model.\\n5. Convert the text rule into audio using the get_prediction method of the TTSHubInterface object.\\n6. Play the generated audio using the IPython display's Audio function.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-ru-cv7_css10', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = 'Your text rule goes here'\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "WyefBdpc9qB8zeqsrFKJDT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 319, "text": "<<>>: Audio Text-to-Speech\\n<<>>: Text2Speech.from_pretrained('espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan')\\n<<>>: ESPnet\\n<<>>:1. Import the necessary libraries, which are 'Text2Speech' from ESPnet and torch.\\n2. Use the 'from_pretrained' function from 'Text2Speech' to load the pre-trained model 'espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan'.\\n3. The model is capable of converting text input into speech output in real-time.\\n4. Pass the text input to the model, and it will generate the corresponding speech waveform.\\n5. You can play the generated waveform through an audio player or save it as a file for later use.\\n<<>>: from espnet2.tts import Text2Speech\\nimport torch\\ntts_model = Text2Speech.from_pretrained('espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan')\\ntext = \\\"Hello, this is a test run.\\\"\\nwaveform, _, _ = tts_model(text)\\n\"", "answer_id": "9jfP8PMyt9zMsvcxoC4E68", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 320, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: pipeline.from_pretrained('pyannote/overlapped-speech-detection', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: pyannote.audio\\n<<>>: 1. We import the necessary class from the pyannote.audio package. This includes the OverlappedSpeechDetection pipeline.\\n2. We load the pre-trained model 'pyannote/overlapped-speech-detection' using the from_pretrained method of the pipeline class.\\n3. This model can be used to analyze the audio data of the recorded meeting to detect overlapping speech.\\n4. As a result, we can identify the sections where two or more people are speaking simultaneously, which can be highlighted in the transcript for clarity.\\n<<>>: from pyannote.audio import OverlappedSpeechDetection\\npipeline = OverlappedSpeechDetection.from_pretrained('pyannote/overlapped-speech-detection',\\n use_auth_token='ACCESS_TOKEN_GOES_HERE')\\noutput = pipeline('audio.wav')\\n# replace 'audio.wav' with the path to your recorded meeting\\nfor speech in output.get_timeline().support():\\n # two speeches are overlapping if the speech segments overlap\\n if speech.start < speech.end and speech.end < speech.start + 0.5:\\n # half a second of overlap for clarity\\n print(f\\\"Overlapping speech between {speech.start} and {speech.end}\\\")\\n\"", "answer_id": "MK6xL5M4N3MvXyxa5zt3M8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 321, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'WhisperProcessor' and 'WhisperForConditionalGeneration' from transformers, and 'load_dataset' from datasets.\\n2. Create a Whisper processor and model using the 'from_pretrained' method, and load the 'openai/whisper-large' pre-trained model.\\n3. Load your audio dataset.\\n4. For each audio recording in the dataset, preprocess the audio using the WhisperProcessor and obtain the input features.\\n5. Use the pre-trained model to generate predicted IDs from the input features.\\n6. Decode the predicted IDs into transcriptions using the WhisperProcessor.\\n7. You now have the transcriptions of the audio recordings.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-large')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\nds = load_dataset('your_audio_dataset')\\n# replace 'your_audio_dataset' with the dataset containing your audio recordings\\nsample = ds[0]['audio']\\ninput_features = processor(sample['array'], sampling_rate=sample['sampling_rate'], return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\\n\"", "answer_id": "Db3eAUavZXuFmrWTVBsCnW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 322, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2Model.from_pretrained('facebook/wav2vec2-large-960h-lv60-self')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'Wav2Vec2Model' from transformers.\\n2. Use the 'Wav2Vec2Model.from_pretrained()' function to load the pre-trained model 'facebook/wav2vec2-large-960h-lv60-self'.\\n3. The loaded model will be used for generating English subtitles for the recorded lessons of the students on the Russia-based online lesson website.\\n4. The model will convert the students' speech into English subtitles that can be displayed alongside the recorded lessons.\\n<<>>: from transformers import Wav2Vec2Model\\nmodel = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-large-960h-lv60-self')\\n# Convert the student's speech to English subtitles and display alongside the recorded lesson\\nenglish_subtitles = model.generate_subtitles(audio_input)\"", "answer_id": "HxVKobpqGMm4RzxhFtNKUx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 323, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-tiny.en')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes WhisperProcessor and WhisperForConditionalGeneration.\\n2. We then use the from_pretrained method of the WhisperProcessor and WhisperForConditionalGeneration classes to load the pre-trained model 'openai/whisper-tiny.en'. This model has been trained for automatic speech recognition tasks, which is exactly what we need to transcribe the audio from your lecture.\\n3. We load the audio data from a file, or it can be acquired in real-time from a recording device during your lecture.\\n4. We process the audio using the WhisperProcessor and obtain the input features for the model.\\n5. Using the model, we generate a transcription of the audio from the input features.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-tiny.en')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-tiny.en')\\n# Replace 'lecture_audio.wav' with the path to your lecture recording\\naudio_data = load_audio('lecture_audio.wav')\\ninput_features = processor(audio_data['array'], sampling_rate=audio_data['sampling_rate'], return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\"", "answer_id": "jmYiaj8UAtoe8tt7EaoTfc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 324, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-to-audio', model='JorisCos/DPTNet_Libri1Mix_enhancement')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the 'JorisCos/DPTNet_Libri1Mix_enhancement' model. This model is designed for single-track audio enhancement tasks, which is perfect for extracting dialogue, music, and background noise from a video game.\\n3. Provide the path to the input audio file.\\n4. The loaded model will process the audio file and return an enhanced version of the track with the desired improvements.\\n<<>>: from transformers import pipeline\\naudio_enhancer = pipeline('audio-to-audio', model='JorisCos/DPTNet_Libri1Mix_enhancement')\\ninput_audio_path = 'path/to/your/audio/file.wav'\\nenhanced_audio = audio_enhancer(input_audio_path)\"", "answer_id": "a3c2hw492uoNaVEVRNv5Af", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 325, "text": "<<>>: Audio Text-to-Speech\\n<<>>: SpeechT5ForTextToSpeech.from_pretrained('microsoft/speecht5_tts')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes and packages. This includes SpeechT5Processor, SpeechT5ForTextToSpeech, and SpeechT5HifiGan for the Text-to-Speech model, and soundfile for handling audio files.\\n2. We then use the from_pretrained method of the SpeechT5ForTextToSpeech class to load the pre-trained model 'microsoft/speecht5_tts'. This model has been trained for text-to-speech tasks, which is what we need to change the voice style of voiceovers while keeping the same content.\\n3. We load the input text, which is the content the voiceover company wants to turn into a voiceover.\\n4. We process the input text using the SpeechT5Processor and convert it into a format that the model can understand.\\n5. We generate the audio waveform and sample rate from the model.\\n6. We write the generated audio to an audio file, which can be used to hear the voiceover.\\n<<>>: from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan\\nfrom soundfile import soundfile\\nimport torch\\ninput_text = \\\"Hello, my name is Clara and I love dogs and cats\\\"\\nprocessor = SpeechT5Processor.from_pretrained('microsoft/speecht5_tts')\\nmodel = SpeechT5ForTextToSpeech.from_pretrained('microsoft/speecht5_tts')\\nvocoder = SpeechT5HifiGan.from_pretrained('microsoft/speecht5_hifigan')\\n# Replace the \\\"input_text\\\" variable with the actual text you want to convert to a voiceover.\\ninputs = processor(input_text, return_tensors='pt')\\nspeech = model.generate_speech(inputs['input_ids'], vocoder=vocoder)\\nsoundfile.write('voiceover_test.wav', speech, 22050)\"", "answer_id": "TgEh5UQpyf8Aeq7dTuhxAy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 326, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the BaseModel class from the transformers library provided by Hugging Face.\\n2. We then use the from_pretrained method of the BaseModel class to load the pre-trained model 'JorisCos/DCUNet_Libri1Mix_enhsingle_16k'. This model has been specifically trained for audiobook-related tasks, which is what we need for detecting and reducing noise in audiobooks.\\n3. The loaded model can then be used to process audiobook audio, filtering out background noise and enhancing the listening experience.\\n4. It should be noted that the performance of the model depends on the specific training data, so the results may not be ideal for all audiobooks.\\n<<>>: from transformers import BaseModel\\nnoisy_audio = \\\"path_to_noisy_audiobook_audio.wav\\\"\\n# replace 'path_to_noisy_audiobook_audio.wav' with the path to your audiobook file\\nmodel = BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\nreduced_audio = model(noisy_audio)\"", "answer_id": "kxQ4MqWJvDyWiTGB6aJuSF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 327, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: ConvTasNet_Libri3Mix_sepclean_8k()\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required ConvTasNet_Libri3Mix_sepclean_8k model from the Asteroid framework.\\n2. Load the pre-trained model using the ConvTasNet_Libri3Mix_sepclean_8k() method. This model is trained to separate voice from background noise on the Libri3Mix dataset.\\n3. Load the audio file containing the podcast episode.\\n4. Use the model to separate the voice from the background noise in the audio file.\\n5. Save the cleaned audio file for further processing.\\n<<>>: from asteroid.models import ConvTasNet_Libri3Mix_sepclean_8k\\nmodel = ConvTasNet_Libri3Mix_sepclean_8k()\\ninput_audio = \\\"path/to/podcast_audio.wav\\\"\\noutput_audio = model.separate(input_audio)\\n\"", "answer_id": "YstFgUqjQqMPZ7ph8J3QkJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 328, "text": "<<>>: Audio Classification\\n<<>>: EncoderClassifier.from_hparams(source='speechbrain/lang-id-voxlingua107-ecapa', savedir='/tmp')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries: torchaudio and EncoderClassifier from speechbrain.pretrained.\\n2. Load the pre-trained language identification model 'speechbrain/lang-id-voxlingua107-ecapa' using the EncoderClassifier.from_hparams() method.\\n3. Use a sample rate of 16000 Hz for the audio input, as this is the most common sample rate for audio data.\\n4. The loaded model can be used to classify the spoken languages in an audio file. You can use torchaudio to load the audio file and pass the audio signal to the model for prediction.\\n<<>>: import torchaudio\\nfrom speechbrain.pretrained import EncoderClassifier\\nlanguage_id = EncoderClassifier.from_hparams(source='speechbrain/lang-id-voxlingua107-ecapa', savedir='/tmp')\\nsignal, fs = torchaudio.load('path/to/your/audio/file.wav')\\nprediction = language_id.classify_batch(signal)\\nlanguage_code = prediction[0]\\n\"", "answer_id": "ZaFatBwmJosQYPiULV8VFC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 329, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'datasets' and 'pipeline' from transformers.\\n2. Use the 'pipeline' function to create an audio classification model by specifying the model as 'superb/wav2vec2-base-superb-ks'. This is a model that has been trained on the Speech Commands dataset and can recognize a variety of keywords in audio clips.\\n3. Load the audio clip that you want to analyze and ensure it's short, as the model may have trouble with longer audio files.\\n4. Use the created classifier to detect the keyword present in the audio clip.\\n<<>>: from transformers import pipeline\\naudio_classifier = pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\\nshort_audio_clip = 'your_audio_clip_here'\\nresult = audio_classifier(short_audio_clip)\\n\"", "answer_id": "iZP6nCnFBZPZsfHfEUv4TB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 330, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english')\\n<<>>: Transformers\\n<<>>:1. Import the required classes from the transformers package, including DistilBertTokenizer and DistilBertForSequenceClassification.\\n2. Load the pre-trained DistilBERT model ('distilbert-base-uncased-finetuned-sst-2-english') and the corresponding tokenizer for text classification tasks.\\n3. Process movie reviews with the tokenizer and feed them into the model.\\n4. Use the model to classify movie reviews as either positive or negative. This can help theater managers understand movie reviews and adjust their marketing strategy accordingly.\\n5. The AI-powered chatbot can be implemented in the movie theater's website or app to interact with movie goers and classify movie reviews.\\n<<>>: from transformers import DistilBertTokenizer, DistilBertForSequenceClassification\\ntokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\\nmodel = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased-finetuned-sst-2-english')\\ninputs = tokenizer(movie_review, return_tensors=\\\"pt\\\")\\noutputs = model(**inputs)\\nclassification = outputs.logits.argmax(dim=1).item()\\n\"", "answer_id": "jqxQByouicCwUdw7UYVfBS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 331, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import the required libraries, such as joblib and pandas.\\n2. Load the pre-trained model using joblib.load and providing the file 'model.joblib'.\\n3. Read the dataset containing the plant species information using pandas.read_csv and providing the file 'data.csv'.\\n4. Select the features column from the dataset, which contains the information about the features used for classification.\\n5. Use the loaded model to predict the species of the plants in the dataset by passing the dataset features column to the model's predict function.\\n6. Display the predictions and the corresponding species.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\nfeatures = data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]\\npredictions = model.predict(features)\\nprint(predictions)\\n\"", "answer_id": "fHG5rZ2iBNdzfPA7q6cMEz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 332, "text": "<<>>: Tabular Tabular Regression\\n<<>>: TFAutoModelForSequenceClassification.from_pretrained('keras-io/timeseries-anomaly-detection')\\n<<>>: Keras\\n<<>>: 1. Import the required libraries: TensorFlow and Keras.\\n2. Load the pretrained model 'keras-io/timeseries-anomaly-detection' using the TFAutoModelForSequenceClassification.from_pretrained() function.\\n3. This model is designed for detecting anomalies in time series data, such as energy consumption data.\\n4. Use the loaded model to find anomalies in the time series data by setting threshold values for the norm and max_lag arguments.\\n5. This model can help identify unusual patterns in the energy consumption data that may indicate anomalies.\\n<<>>: import tensorflow as tf\\nfrom transformers import TFAutoModelForSequenceClassification\\nmodel = TFAutoModelForSequenceClassification.from_pretrained('keras-io/timeseries-anomaly-detection')\\ninputs = tf.expand_dims(input_data, 0)\\n# Process your input data 'input_data' with tf.concat(value_counts, axis=0) to obtain norm and max_lag\\n# Set threshold values for norm and max_lag\\nmodel.eval()\\n\"", "answer_id": "n8BtmfRUsWvM6LvpzaFjDw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 333, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We first import the necessary libraries, which include joblib and pandas. Joblib is used to load the pre-trained model, and pandas is used for data manipulation and handling.\\n2. We then load the pre-trained model using joblib.load('model.joblib'). This model has been trained on a similar dataset for predicting carbon emissions based on input features.\\n3. We read the historical data about carbon emissions collected by the company and preprocess the data as required by the model.\\n4. We then use the loaded model to make predictions on the preprocessed data to estimate carbon emissions.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('historical_carbon_emissions.csv')\\n# Ensure columns are correctly named for the model\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "SFt8Wq7No5ypxDmDvRHSce", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 334, "text": "<<>>: Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, which are 'joblib' and 'pandas'.\\n2. Load the pre-trained model using joblib.load() function.\\n3. Read the input data from a CSV file using pandas' read_csv() function.\\n4. Select the required features from the input data, and rename the columns as required by the model.\\n5. Predict the carbon emissions using the model's predict() function on the input data.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\nfeatures = ['feature1', 'feature2', 'feature3'] # Replace with actual feature names\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "hvmLip3mRZA4ikdqqZzQq9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 335, "text": "<<>>: Tabular Tabular Regression\\n<<>>: skops.io.load('path_to_folder/example.pkl')\\n<<>>: Scikit-Ol\\n<<>>: 1. Import the necessary libraries (skops.io) to load the pre-trained model and pandas for handling data.\\n2. Load the pre-trained model ('path_to_folder/example.pkl') that predicts fish weights based on their measurements.\\n3. Read the collected data (in CSV format) using pandas and select only the relevant columns (e.g., 'measurements').\\n4. Rename the columns in the data to match the model's expected input format ('feat_').\\n5. Use the loaded model to predict the weight of the fish based on the input data.\\n<<>>: import skops.io\\nimport pandas as pd\\nmodel = skops.io.load('path_to_folder/example.pkl')\\ndata = pd.read_csv('data.csv')\\n# replace 'data.csv' with the path to your CSV file\\ndata = data[['measurements']]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\nfish_weight = predictions[0]\\n\"", "answer_id": "hsxwNdqLcN8kqChLcugzeW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 336, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load(hf_hub_download('merve/tips5wx_sbh5-tip-regression', 'sklearn_model.joblib'))\\n<<>>: Scikit-learn\\n<<>>:1. Import the required libraries, such as joblib and hf_hub_download function from Hugging Face Hub.\\n2. Load the pre-trained model 'merve/tips5wx_sbh5-tip-regression' using joblib.load function.\\n3. The loaded model is a regression model that is trained to predict the tip amount based on various features, such as total bill, sex, smoker, day, time, and party size.\\n4. Pass the features (numeric or categorical) of a new customer to predict the tip amount.\\n<<>>: import joblib\\nfrom huggingface_hub import hf_hub_download\\nmodel = joblib.load(hf_hub_download('merve/tips5wx_sbh5-tip-regression', 'sklearn_model.joblib'))\\npredicted_tip = model.predict([(total_bill, sex, smoker, day, time, size)])\\n\"", "answer_id": "f96VWE6TsaKmnbmnvFufgm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 337, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/dqn-CartPole-v1', filename='{MODEL FILENAME}.zip')\\n<<>>: Stable-Baselines3\\n<<>>: 1. Import the necessary libraries, including 'stable_baselines3', 'stable_baselines3-contrib', and 'rl_zoo3'.\\n2. Use the 'load_from_hub' method to download a trained model based on the repository ID 'sb3/dqn-CartPole-v1' and the provided model filename.\\n3. The model is a DQN trained on the CartPole-v1 environment, which is a simplified version of the classic 'Pole Tax' game.\\n4. With the downloaded model, you can now use it as the player in a two-player game against the user.\\n5. Note that the user will also need to install the necessary dependencies for this to work properly.\\n<<>>: from rl_zoo3 import load_from_hub\\nmodel = load_from_hub(repo_id='sb3/dqn-CartPole-v1', filename='{MODEL FILENAME}.zip')\\n\"", "answer_id": "THkSJ2wXxeTH7YLRp6Vu3o", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 338, "text": "<<>>: Reinforcement Learning Robotics\\n<<>>: model_utils.load_model('model_utils.VC1_BASE_NAME')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'model_utils' from vc_models.models.vit.\\n2. Load the pre-trained VC-1 model using the 'model_utils.load_model()' function with 'model_utils.VC1_BASE_NAME' as the argument.\\n3. Use the model_transforms() function to prepare the input for your specific robotics task.\\n4. Feed the transformed input into the loaded model to obtain the desired outputs, such as object location and interaction estimations.\\n5. Use these outputs to make decisions and guide the robot's interactions in its environment.\\n###Code>>>: from vc_models.models.vit import model_utils\\nmodel, embd_size, model_transforms, model_info = model_utils.load_model(model_utils.VC1_BASE_NAME)\\n# Replace 'your_function_here' with the actual function that will acquire the image input\\nimg = your_function_here()\\ntransformed_img = model_transforms(img)\\nembedding = model(transformed_img)\\n\"", "answer_id": "XPbrk4vLxWpP7XUhms8fhr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 340, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('d4data/biomedical-ner-all')\\n<<>>: Transformers\\n<<>>: 1. Import the required classes from the transformers package, including AutoTokenizer and AutoModelForTokenClassification.\\n2. Initialize the tokenizer and model with the pre-trained 'd4data/biomedical-ner-all' model, which is designed for Named Entity Recognition (NER) tasks in the biomedical domain.\\n3. Create a pipeline for Named Entity Recognition (NER) using the initialized tokenizer and model.\\n4. Pass the entity name string to the pipeline, which will process and extract features from the biomedical entity names.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('d4data/biomedical-ner-all')\\nmodel = AutoModelForTokenClassification.from_pretrained('d4data/biomedical-ner-all')\\nner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)\\nentity_name = \\\"atherosclerosis\\\"\\nresult = ner_pipeline(entity_name)\\n\"", "answer_id": "aoJjZjC9bX3dk8RjA6brHv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 341, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the sentence-transformers library and import the SentenceTransformer class.\\n2. Initialize the SentenceTransformer by providing the model name 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'.\\n3. Create a list of sentences or book reviews that you want to analyze for similarity.\\n4. Use the encode() method to generate embeddings for each sentence or book review.\\n5. Compare the embeddings using a similarity metric (e.g., cosine similarity) to determine how similar two examples are from multiple books.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nreviews = ['I love this book!', 'This book is fantastic!', 'I am not a fan of this book.']\\nmodel = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')\\nembeddings = model.encode(reviews)\\nsimilarity_matrix = cosine_similarity(embeddings)\\nprint(similarity_matrix)\\n\"", "answer_id": "8Xm9gyP5ZeHzNmYdTairSQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 342, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('facebook/opt-66b', torch_dtype=torch.float16)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Start by importing the necessary libraries, which include AutoModelForCausalLM and AutoTokenizer from the transformers package.\\n2. Load the pre-trained model 'facebook/opt-66b' using the from_pretrained method of the AutoModelForCausalLM class.\\n3. Use the tokenizer to convert the textual prompt into input_ids that can be fed to the model.\\n4. Generate a text response using the model's generate method and the input_ids.\\n5. Decode the generated output into a human-readable string using the tokenizer's batch_decode method.\\n<<>>: from transformers import AutoModelForCausalLM, AutoTokenizer\\nimport torch\\nmodel = AutoModelForCausalLM.from_pretrained('facebook/opt-66b', torch_dtype=torch.float16).cuda()\\ntokenizer = AutoTokenizer.from_pretrained('facebook/opt-66b', use_fast=False)\\nprompt = \\\"Welcome to the company, we are glad to have you on board!\\\"\\ninput_ids = tokenizer(prompt, return_tensors='pt').input_ids.cuda()\\ngenerated_ids = model.generate(input_ids)\\nresponse = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\\n\"", "answer_id": "JHhkUriFhrtT57QU6iYj8h", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 343, "text": "<<>>: Multimodal Feature Extraction\\n<<>>: AutoModel.from_pretrained('DeepPavlov/rubert-base-cased')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'AutoModel' and 'AutoTokenizer' from transformers.\\n2. Use the 'from_pretrained' method of the AutoModel class to load the pre-trained model 'DeepPavlov/rubert-base-cased'. This model has been specifically designed for working with Russian text and can extract useful features needed for downstream processing.\\n3. Similarly, load the tokenizer for the model using 'AutoTokenizer.from_pretrained()'.\\n4. Tokenize the Russian text using the loaded tokenizer.\\n5. Pass the tokenized text to the model for feature extraction.\\n<<>>: from transformers import AutoModel, AutoTokenizer\\nmodel = AutoModel.from_pretrained('DeepPavlov/rubert-base-cased')\\ntokenizer = AutoTokenizer.from_pretrained('DeepPavlov/rubert-base-cased')\\nencoded_text = tokenizer(russian_text, return_tensors='pt')\\nfeatures = model(**encoded_text).last_hidden_state\\n\"", "answer_id": "fr58oXVYjNkvQUuTQdVNmc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 344, "text": "<<>>: Natural Language Processing Code Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('lanwuwei/BERTOverflow_stackoverflow_github')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are AutoTokenizer and AutoModelForTokenClassification from the transformers package.\\n2. Use the AutoTokenizer.from_pretrained method to create a tokenizer with the specified model, such as 'lanwuwei/BERTOverflow_stackoverflow_github'.\\n3. Use the AutoModelForTokenClassification.from_pretrained method to load the model, which in this case is 'lanwuwei/BERTOverflow_stackoverflow_github'.\\n4. The model will be used for token classification, specifically for identifying code segments and comments within a given dataset.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification\\ntokenizer = AutoTokenizer.from_pretrained('lanwuwei/BERTOverflow_stackoverflow_github')\\nmodel = AutoModelForTokenClassification.from_pretrained('lanwuwei/BERTOverflow_stackoverflow_github')\\n\"", "answer_id": "Ez8CMjyRnkAUBNxDwwaFLT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 345, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('dreamlike-art/dreamlike-photoreal-2.0', torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the StableDiffusionPipeline class from the diffusers package.\\n2. Load the pre-trained model 'dreamlike-art/dreamlike-photoreal-2.0' with the specified torch_dtype of torch.float16 for reduced memory usage.\\n3. Set the text prompt to describe the desired image: \\\"Vintage sports car racing through a desert landscape during sunset, high resolution, colorful sky, dreamlike quality, clean and sharp edges.\\\"\\n4. Use the pipeline to generate the image based on the text prompt.\\n5. Save the generated image to a file for use in the marketing campaign.\\n<<>>: from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = 'dreamlike-art/dreamlike-photoreal-2.0'\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nprompt = \\\"Vintage sports car racing through a desert landscape during sunset, high resolution, colorful sky, dreamlike quality, clean and sharp edges\\\"\\nimage = pipe(prompt).images[0]\\nimage.save('./vintage_sports_car_desert.jpg')\\n\"", "answer_id": "Veb5bMKKLeonAaHJcc8PhY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 346, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base', scheduler=EulerDiscreteScheduler.from_pretrained('stabilityai/stable-diffusion-2-base', subfolder=scheduler), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary libraries, including StableDiffusionPipeline and EulerDiscreteScheduler from the diffusers package.\\n2. Load the pretrained model 'stabilityai/stable-diffusion-2-base' using the from_pretrained method of the StableDiffusionPipeline class.\\n3. Load the pretrained scheduler using the from_pretrained method of the EulerDiscreteScheduler class.\\n4. Move the pipeline to the GPU if available using the .to() method.\\n5. Pass the text description of the scene to the pipeline to generate an image.\\n6. Save the generated image to a file.\\n<<>>: from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\\nmodel_id = 'stabilityai/stable-diffusion-2-base'\\nscheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder='scheduler')\\npipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nprompt = \\\"A magical forest with fairies and a talking tree\\\"\\nimage = pipe(prompt).images[0]\\nimage.save('storybook_image.png')\"", "answer_id": "8JMz2nFHAVzrSguwgehLbm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 347, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: pipeline('text-to-image', model='Lykon/DreamShaper')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a text-to-image pipeline using the 'Lykon/DreamShaper' model.\\n3. The model is designed to generate images based on input text, and it can be used to create artistic images based on user input.\\n4. Use the created pipeline to generate an image description from the input text.\\n<<>>: from transformers import pipeline\\ntext_to_image = pipeline('text-to-image', model='Lykon/DreamShaper')\\nimage_description = text_to_image(\\\"Input text goes here.\\\")\"", "answer_id": "NibWTtGouVCQfAMbm8rToF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 348, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('prompthero/openjourney', torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries, including StableDiffusionPipeline from the diffusers package and torch for tensor manipulation.\\n2. Load the pre-trained text-to-image model 'prompthero/openjourney' using the from_pretrained method of the StableDiffusionPipeline class, specifying the torch_dtype parameter as torch.float16 for better performance.\\n3. Provide a textual description of the desired image as input to the model. This could be a prompt or a description of the features you want the generated image to have.\\n4. Optionally, you can specify a negative_prompt to guide the model away from undesired aspects in the generated image.\\n5. Call the pipeline method with the input text and any additional parameters, such as num_inference_steps and guidance_scale, to generate the image.\\n6. Save the generated image to a file for use on your e-commerce website.\\n<<>>: from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = 'prompthero/openjourney'\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nprompt = 'a mountain with a sunset background'\\nnegative_prompt = 'cheesy, low quality, blurry'\\nimage = pipe(prompt, negative_prompt=negative_prompt).images[0]\\nimage.save('./mountain_sunset.png')\\n\"", "answer_id": "CuP72fSbttzjEWxhsPjQhY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 349, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-to-text generation model.\\n3. Specify the model 'salesforce/blip2-opt-6.7b' to be loaded. This is a large, multimodal model trained for tasks such as image captioning and visual question answering.\\n4. The created model can be used to generate descriptive captions for the given product photographs by providing the image as input to the model.\\n<<>>: from transformers import pipeline\\ncaption_generator = pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\nimage_caption = caption_generator(image_data)\\n\"", "answer_id": "V5GwJP2NgBKNi2kCkvqc3t", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 350, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('microsoft/git-large-textvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelForSeq2SeqLM' from transformers.\\n2. Instantiate the tokenizer and the model using the 'AutoTokenizer.from_pretrained()' and 'AutoModelForSeq2SeqLM.from_pretrained()' methods with the provided model name 'microsoft/git-large-textvqa'.\\n3. The model can be used for visual question answering tasks, where it takes a image as input and answers questions based on the content of the image.\\n4. In your case, you can use the model to process images of landmarks and provide information about them based on the questions users ask.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('microsoft/git-large-textvqa')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('microsoft/git-large-textvqa')\\n\"", "answer_id": "nVVwX7egzzFY8FwTe9FNEt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 351, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: Pix2StructForConditionalGeneration.from_pretrained('google/deplot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary class Pix2StructForConditionalGeneration from the transformers library.\\n2. Load the pre-trained model 'google/deplot' using the from_pretrained method. This model has been trained for image-to-text tasks, which is useful for analyzing chart images and generating summaries.\\n3. Load the image data from a file or acquire it in real-time if the chart is displayed on a screen or printed on a page.\\n4. Apply the model to the image data and obtain the generated text output. The model can analyze the chart and generate a summary of the information it contains.\\n<<>>: from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor\\nimport torch\\nfrom PIL import Image\\nmodel = Pix2StructForConditionalGeneration.from_pretrained('google/deplot')\\nprocessor = Pix2StructProcessor.from_pretrained('google/deplot')\\nimage = Image.open('chart_image_path.jpg')\\ninputs = processor(image, return_tensors='pt')\\nsummary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=150, early_stopping=True)\\nsummary = processor.decode(summary_ids[0], skip_special_tokens=True)\\n\"", "answer_id": "3KFys8jmVjHNXZhg89Hvz5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 352, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: pipeline('text-to-video', model='camenduru/text2-video-zero')\\n<<>>: Hugging Face\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a text-to-video pipeline using the model 'camenduru/text2-video-zero', which is designed for generating videos from text input.\\n3. We provide the input text, \\\"Chef John's Culinary Adventures\\\", which will be used as the basis for the video.\\n4. The pipeline will generate a video based on the given input text that can be used in the cooking show intro.\\n<<>>: from transformers import pipeline\\ntext2video = pipeline('text-to-video', model='camenduru/text2-video-zero')\\nvideo = text2video(\\\"Chef John's Culinary Adventures\\\")\"", "answer_id": "dUG2xPLnHVNqz3LmqsCLbq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 353, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: TuneAVideoPipeline.from_pretrained('nitrosocke/mo-di-diffusion', unet=UNet3DConditionModel.from_pretrained('Tune-A-Video-library/mo-di-bear-guitar', subfolder='unet', torch_dtype=torch.float16), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and classes such as TuneAVideoPipeline, UNet3DConditionModel, and Torch.\\n2. Instantiate the pretrained UNet3DConditionModel and the TuneAVideoPipeline model with the 'nitrosocke/mo-di-diffusion' model and the provided directories.\\n3. Assign a text prompt describing the desired scene, such as \\\"a person walking along a beach\\\".\\n4. Generate the video using the provided text prompt and save the resulting video as a file.\\n<<>>: from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline\\nfrom tuneavideo.models.unet import UNet3DConditionModel\\nfrom tuneavideo.util import save_videos_grid\\nimport torch\\npretrained_model_path = 'nitrosocke/mo-di-diffusion'\\nunet_model_path = 'Tune-A-Video-library/mo-di-bear-guitar'\\nunet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder='unet', torch_dtype=torch.float16).to('cuda')\\npipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to('cuda')\\npipe.enable_xformers_memory_efficient_attention()\\nprompt = \\\"A person walking along a beach\\\"\\nvideo = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos\\nsave_videos_grid(video, f'./{prompt}.gif')\"", "answer_id": "XuAwTA8B5hbkeunKmZMuNs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 354, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: ViltForQuestionAnswering.from_pretrained('dandelin/vilt-b32-finetuned-vqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries, including ViltProcessor and ViltForQuestionAnswering from transformers, requests, and Image from PIL.\\n2. Load the pre-trained model 'dandelin/vilt-b32-finetuned-vqa' using the ViltForQuestionAnswering class.\\n3. Load the ViltProcessor, which helps with the image and text preprocessing.\\n4. Obtain the image URL and the question text.\\n5. Preprocess the image and question text using the ViltProcessor.\\n6. Pass the preprocessed inputs to the model to get the answer logits.\\n7. Find the index of the highest logit value and use the model's config to map the index to a label.\\n8. Print the predicted answer.\\n<<>>: from transformers import ViltProcessor, ViltForQuestionAnswering\\nimport requests\\nfrom PIL import Image\\nimage_url = 'https://example.com/image.jpg'\\nquestion_text = 'What color is the car?'\\nprocessor = ViltProcessor.from_pretrained('dandelin/vilt-b32-finetuned-vqa')\\nmodel = ViltForQuestionAnswering.from_pretrained('dandelin/vilt-b32-finetuned-vqa')\\nimage = Image.open(requests.get(image_url, stream=True).raw)\\ninputs = processor(image, question_text, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\nidx = logits.argmax(-1).item()\\nanswer = model.config.id2label[idx]\\nprint(\\\"Predicted answer:\\\", answer)\"", "answer_id": "WRUcBZKpRuvK2SRmbxt2LQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 355, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('document-question-answering', model='pardeepSF/layoutlm-vqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a document-question-answering model.\\n3. Specify the model 'pardeepSF/layoutlm-vqa' to be loaded. This model is designed for answering questions based on a document's content.\\n4. The created model can be used to analyze the content of an uploaded document and answer questions related to it.\\n<<>>: from transformers import pipeline\\ndoc_qa = pipeline('document-question-answering', model='pardeepSF/layoutlm-vqa')\\nquestion = \\\"What is the total amount due?\\\"\\ndoc_url = \\\"https://example.com/document.pdf\\\"\\nanswer = doc_qa(question=question, url=doc_url)\\n\"", "answer_id": "iyJvHzQYo7n2P3mWWZCbSm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 356, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary 'pipeline' function from the transformers library.\\n2. Use the pipeline function to create a question-answering model, specifying the 'tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa' model, which is designed for document question answering tasks.\\n3. The created model can be used to quickly and accurately answer questions about the contents of study guides and textbooks by providing the question and context (i.e., the textbook content) to the model.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\nquestion = \\\"What is photosynthesis?\\\"\\ncontext = \\\"Photosynthesis is the process by which green plants and some other organisms use sunlight to synthesize foods with the help of chlorophyll pigments. It involves the conversion of carbon dioxide and water into glucose and oxygen.\\\"\\nresult = qa_pipeline({'question': question, 'context': context})\\nanswer = result[\\\"answer\\\"]\\n\"", "answer_id": "44QyAMVLrNeKA3pBL5J2Ld", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 357, "text": "<<>>: Multimodal Graph Machine Learning\\n<<>>: AutoModel.from_pretrained('graphormer-base-pcqm4mv1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries - the AutoModel class from the transformers package.\\n2. Use the from_pretrained method of the AutoModel class to load the pretrained Graphormer model, with the model name 'graphormer-base-pcqm4mv1'. This model has been specifically trained on molecule datasets and is well-suited for predicting molecular properties.\\n3. The loaded model can be used for various tasks, such as graph classification, graph representation, and relation extraction on molecule datasets.\\n<<>>: from transformers import AutoModel\\ngraphormer_model = AutoModel.from_pretrained('graphormer-base-pcqm4mv1')\\n\"", "answer_id": "fKNWQ7hrqGnyyeEjP7oGiB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 358, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/roberta-base-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers library. This includes AutoModelForQuestionAnswering and AutoTokenizer.\\n2. We then use the from_pretrained method of the AutoModelForQuestionAnswering class to load the pre-trained model 'deepset/roberta-base-squad2'.\\n3. The model can be used to find the answer to a question based on a given context. In this case, the question is about a person's eligibility for a loan, and the context is a company policy restricting applicants to citizens of the United States who are 18 years old or above and earn a monthly salary of at least $4,000.\\n4. The model will return the answer based on the provided context, which is 'No, because the applicant must be a US citizen and earn a monthly salary of at least $4,000.'.\"", "answer_id": "aTAoMMMsu3JXwi9jf69mRt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 359, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-pc-component-detection')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the required Python packages: ultralyticsplus and ultralytics.\\n2. Import YOLO and render_result from ultralyticsplus library.\\n3. Instantiate the YOLO object detection model with the pre-trained model 'keremberke/yolov8m-pc-component-detection'.\\n4. Override the model parameters like 'conf', 'iou', 'agnostic_nms', and 'max_det' to fine-tune the detection performance.\\n5. Use the model's predict method to detect components in an input image.\\n6. Render the results to visualize the detected components in the image.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-pc-component-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path/to/image.jpg' # replace with the image URL or path\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "daW5kc4VfTmhjcGnaUV4AY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 360, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', model='fxmarty/resnet-tiny-beans')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model, which in this case is 'fxmarty/resnet-tiny-beans'. This model has been trained for image classification tasks, which is what we need for classifying images of houseplants.\\n3. The loaded model can be used to analyze an image and classify it into different categories, such as cactus, fern, or succulent.\\n4. To classify the image, provide the image path or URL as input to the model.\\n5. The model will then return the predicted class probabilities for the image.\\n<<>>: from transformers import pipeline\\nimport requests\\nfrom PIL import Image\\n# Load the pre-trained model\\nclassifier = pipeline('image-classification', model='fxmarty/resnet-tiny-beans')\\n# Load the image\\nurl = 'path/to/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\n# Classify the image\\nresult = classifier(image)\\n# Print the prediction\\nprint(result['labels'], result['scores'], result['probabilities'])\\n\"", "answer_id": "H5g4oPFDReeLDdPpyLJ6xs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 361, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', model='julien-c/hotdog-not-hotdog')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Then, create an image classification pipeline using the provided model name 'julien-c/hotdog-not-hotdog'. This model is trained to classify images as hotdog or not hotdog.\\n3. Next, call the image_classification_pipeline function with the path to your image. It will return a dictionary containing the classification results.\\n4. Finally, you can see if the model classifies your image as hotdog or not.\\n<<>>: from transformers import pipeline\\nimage_classification_pipeline = pipeline('image-classification', model='julien-c/hotdog-not-hotdog')\\nresult = image_classification_pipeline('path/to/your/image.jpg')\\n\"", "answer_id": "gC2mkqegGsn72cEJZNzrtK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 362, "text": "<<>>: Computer Vision Object Detection\\n<<>>: pipeline('object-detection', model='microsoft/table-transformer-structure-recognition')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to load the pre-trained model 'microsoft/table-transformer-structure-recognition'. This model is designed specifically for detecting rows, columns, and cells in table images.\\n3. Provide the table image as input to the model, which will analyze it and detect the rows and columns as well as the cells contained within the table.\\n4. The output can be processed to extract the structured data from the scanned table image.\\n<<>>: from transformers import pipeline\\ntable_detector = pipeline('object-detection', model='microsoft/table-transformer-structure-recognition')\\ntable_image = 'path/to/table_image.jpg'\\n# replace 'path/to/table_image.jpg' with the path to your table image\\nresult = table_detector(table_image)\\n\"", "answer_id": "JoYJzAtjUgpgqhR7YfiJFy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 363, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: clip.load('timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries: clip from huggingface_hub, PIL.Image, and requests.\\n2. Load the model 'timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k' using the clip.load() function which is designed for zero-shot image classification tasks.\\n3. Load the image using a PIL.Image object or an image URL, and crop it to the required size and dimensions.\\n4. Pass the image and the list of class names representing food items (vegetarian, meat, seafood, etc.) to the loaded model.\\n5. The model will predict if the image contains any meat and whether it's from a vegetarian or non-vegetarian source.\\n<<>>: import requests\\nfrom PIL import Image\\nfrom huggingface_hub import clip\\nmodel = clip.load('timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k')\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with path to your image\\nclass_names = ['vegetarian', 'meat', 'seafood']\\nresults = model(image, class_names)\\n\"", "answer_id": "fXtdEkAdiutfsGZD78cnTL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 364, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='laion/CLIP-ViT-B-32-laion2B-s34B-b79K')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a zero-shot image classification model using the 'laion/CLIP-ViT-B-32-laion2B-s34B-b79K' model.\\n3. This model can be used to classify images of various objects in the kitchen, such as fruits, vegetables, dishes, and utensils.\\n4. To classify an image, provide the image file path and a list of class names (categories) that you want to recognize in the image.\\n5. The model will then return a list of probabilities for each class, indicating its confidence in the classification.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('zero-shot-image-classification', model='laion/CLIP-ViT-B-32-laion2B-s34B-b79K')\\nclass_names = ['apple', 'banana', 'tomato', 'potato', 'spoon', 'fork']\\nclassification_result = image_classifier('path/to/kitchen_image.jpg', class_names=class_names)\\n\"", "answer_id": "fD66uv5tQbjHjvjdB8rDgg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 365, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-small-coco-instance')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers and PIL packages. This includes Mask2FormerForUniversalSegmentation for the image segmentation model.\\n2. We then use the from_pretrained method of the Mask2FormerForUniversalSegmentation class to load the pre-trained model 'facebook/mask2former-swin-small-coco-instance'. This model has been trained for image segmentation tasks, which is exactly what we need for segmenting clothes in photos.\\n3. We load the image data from a file or URL, depending on the source.\\n4. This model can then be used to segment clothes in the given images.\\n<<>>: from transformers import Mask2FormerForUniversalSegmentation\\nfrom PIL import Image\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with path to your image\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-small-coco-instance')\\ninputs = processor(images=image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\nresult = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_instance_map = result['segmentation']\"", "answer_id": "8ytyfbUWc6nPeXwcijJBaA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 366, "text": "\\n<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-normal')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and classes, including the ControlNetModel and StableDiffusionControlNetPipeline.\\n2. Load the pre-trained model 'lllyasviel/sd-controlnet-normal' using the ControlNetModel.from_pretrained method.\\n3. Load the image to be processed, either from a file or acquired in real-time from a camera.\\n4. Normalize the image data and convert it to the format expected by the model.\\n5. Create a pipeline using the StableDiffusionControlNetPipeline.from_pretrained method, which will combine the diffusion process with the normal map extraction.\\n6. Run the pipeline on the image, which will generate a normal map representation of the object's surface.\\n7. Save the resulting normal map image to a file.\\n<<>>: \\nfrom diffusers import ControlNetModel, StableDiffusionControlNetPipeline\\nfrom PIL import Image\\nimport torch\\nfrom transformers import CLIPTextModel\\nfrom diffusers.utils import load_image\\nimage = load_image('input_object_image.png') # Replace with the path to your object image\\nimage = image.convert('RGB')\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-normal', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\nprocessor = CLIPTextModel.from_pretrained('runwayml/stable-diffusion-v1-5', num_hidden_layers=11, torch_dtype=torch.float16)\\npipe.enable_model_cpu_offload()\\nnormal_map_image = pipe(image, num_inference_steps=20).images[0]\\nnormal_map_image.save('output_normal_map.png')\\n\"", "answer_id": "YWEWcpHWWGi97iwYBfFZzt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 367, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_mlsd')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary packages and classes, including ControlNetModel and StableDiffusionControlNetPipeline.\\n2. Load the pre-trained ControlNet model 'lllyasviel/control_v11p_sd15_mlsd' using the from_pretrained method.\\n3. Create a pipeline to combine the ControlNet model with the Stable Diffusion method, which is based on the OpenAI GPT-J-13B model.\\n4. Modify the pipeline's scheduler by setting the UniPCMultistepScheduler's enable_model_cpu_offload to True to reduce CPU usage.\\n5. Generate the final image by providing the desired text prompt, an initial image, the number of inference steps, and a random number generator.\\n6. Save the generated image with the added building and river.\\n<<>>: from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nfrom controlnet_aux import MLSDdetector\\nfrom diffusers.utils import load_image\\nimport torch\\ncheckpoint = 'lllyasviel/control_v11p_sd15_mlsd'\\ncontrolnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\nprompt = \\\"A beautiful landscape with a building and a river\\\"\\nimage = load_image('input_image_path')\\ncontrol_image = MLSDdetector(image, text_prompt=prompt)\\ncontrol_image = control_image.cpu()\\noutput_image = pipe(prompt, num_inference_steps=30, image=control_image).images[0]\\noutput_image.save('images/image_out.png')\\n\"", "answer_id": "JYMbx9CpLpUQVrWJTwU5Cb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 368, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_softedge')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and classes, such as ControlNetModel, StableDiffusionControlNetPipeline, and UniPCMultistepScheduler from the diffusers and transformers packages.\\n2. Load the pre-trained ControlNet model 'lllyasviel/control_v11p_sd15_softedge' for diffusion-based text-to-image generation.\\n3. Create a pipeline object with the pre-trained ControlNet model and set the scheduler and other parameters.\\n4. Use this pipeline object to generate the desired image of a luxury living room with a fireplace, specifying the text prompt and other parameters.\\n5. Save the generated image to a file for use in the project.\\n<<>>: from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nfrom diffusers.utils import load_image\\nimport torch\\nimage_url = \\\"https://your_image_url\\\" # Replace with the URL or path of the reference image\\ncontrol_image = load_image(image_url).convert('RGB')\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_softedge', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\nprompt = \\\"luxury living room with a fireplace\\\"\\ngenerator = torch.manual_seed(33)\\nimage = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]\\nimage.save('images/living_room_fireplace.png')\\n\"", "answer_id": "EC7hXJgucCLuJ4Y8vSvPoD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 369, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: from_pretrained_keras('google/maxim-s3-deblurring-gopro')\\n<<>>: Keras\\n<<>>: 1. Import the necessary libraries and classes, including the MAXIM API, Image from PIL and various other packages.\\n2. Load the pretrained model 'google/maxim-s3-deblurring-gopro' using the from_pretrained_keras method.\\n3. Load the blurred image using the Image.open method from the PIL library.\\n4. Process the image and convert it to a suitable format for the MAXIM model.\\n5. Use the MAXIM model to deblur the image, and save the output to a new image file, such as 'deblurred_image.png'.\\n<<>>: from MAXIM_API import from_pretrained_keras\\nfrom PIL import Image\\nimport numpy as np\\nimport tensorflow as tf\\nimport os\\nimport requests\\nurl = 'https://path_to_blurred_image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nimage = np.array(image)\\nimage = tf.convert_to_tensor(image)\\nimage = tf.image.resize(image, (128, 128))\\nmodel = from_pretrained_keras('google/maxim-s3-deblurring-gopro')\\npredictions = model.predict(tf.expand_dims(image, 0))\"", "answer_id": "7xSHgCRP3jGjP2dtxQVr7b", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 370, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', vae=AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-ema'))\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, which include AutoencoderKL from the diffusers.models module and StableDiffusionPipeline from the diffusers module.\\n2. Use the from_pretrained method to load the 'CompVis/stable-diffusion-v1-4' model for text-to-image generation, and load the 'stabilityai/sd-vae-ft-ema' model as the Variational Autoencoder (VAE) by calling AutoencoderKL.from_pretrained.\\n3. Initialize the pipeline by passing the loaded model and VAE as arguments.\\n4. Use the pipeline to generate an image based on the given input text, and save the resulting image to a file titled 'a_head_full_of_roses.png'.\\n<<>>: from diffusers.models import AutoencoderKL\\nfrom diffusers import StableDiffusionPipeline\\nmodel = 'CompVis/stable-diffusion-v1-4'\\nvae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-ema')\\npipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)\\ninput_text = \\\"A head full of roses\\\"\\ngenerated_image = pipe(input_text).images[0]\\ngenerated_image.save('a_head_full_of_roses.png')\"", "answer_id": "GchQXFwzum2FV8q9cko7w4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 371, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-ema-church-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the 'diffusers' package, which contains the necessary classes for working with Denoising Diffusion Probabilistic Models (DDPM).\\n2. Import the DDPMPipeline class from the diffusers package.\\n3. Load the pre-trained 'google/ddpm-ema-church-256' model using the DDPMPipeline.from_pretrained() method. This model is trained to generate high-quality images of churches.\\n4. Generate a new image by calling the loaded model. The resulting image will be a high-quality rendering of a church, created using unconditional image generation.\\n5. Save the generated image to a file.\\n<<>>: !pip install diffusers\\nfrom diffusers import DDPMPipeline\\nmodel_id = 'google/ddpm-ema-church-256'\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\\n\"", "answer_id": "GemKZaYbioLWt53qJdiNh7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 372, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DiffusionPipeline.from_pretrained('google/ncsnpp-celebahq-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Start by installing the 'diffusers' package, which contains the necessary classes for the task.\\n2. Import the DiffusionPipeline class from the diffusers package.\\n3. Use the from_pretrained method to instantiate a pre-trained model, specifying 'google/ncsnpp-celebahq-256' as the model to load. This model is trained on the CelebA-HQ dataset and is capable of generating high-quality human faces.\\n4. Call the instantiated model to generate a new portrait image.\\n5. Save the generated image to a file (e.g., 'sde_ve_generated_image.png') for use as a company team portrait.\\n<<>>: !pip install diffusers\\nfrom diffusers import DiffusionPipeline\\nmodel_id = 'google/ncsnpp-celebahq-256'\\nsde_ve = DiffusionPipeline.from_pretrained(model_id)\\nimage = sde_ve()[0]\\nimage.save('sde_ve_generated_image.png')\\n\"", "answer_id": "6gkhDzHvHBgiRaTyEmb4XU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 373, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-cat-256')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, we import the required class from the diffusers package. This includes DDPMPipeline, which is used for the unconditional image generation model.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'google/ddpm-cat-256'. This model has been specifically trained to generate images of cats, which is what we need for designing cat-themed video game characters.\\n3. This model can be used to generate an image, which can serve as the basis for a cartoon cat character design. The generated image is then saved to the file 'ddpm_generated_image.png'.\\n<<>>: from diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-cat-256')\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\\n\"", "answer_id": "oUmxWFtaUqGsxBvvpbq7HZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 374, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries:VideoMAEImageProcessor and VideoMAEForVideoClassification from transformers, numpy, and torch.\\n2. Load the pre-trained video classification model 'videomae-base-finetuned-ssv2' using the from_pretrained function of VideoMAEForVideoClassification.\\n3. Create an instance of the VideoMAEImageProcessor by calling its from_pretrained method with the same model name.\\n4. If the video input is a list of frames, convert it into a numpy array with shape (16, 3, 224, 224).\\n5. Process the video frames using the VideoMAEImageProcessor instance with return_tensors='pt' to get the pixel values.\\n6. Pass the pixel values to the model by calling its forward method, which will output logits.\\n7. Obtain the predicted class index by taking the argmax of the logits, then lookup the corresponding label using the id2label attribute of the model.\\n8. The predicted class label is a natural language description of what's happening in the video.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nnum_frames = 16\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-finetuned-ssv2')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-ssv2')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\"", "answer_id": "VwT9tQmuUTCqSYELfy4gxA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 375, "text": "<<>>: Computer Vision Video Classification\\n<<>>: AutoModelForVideoClassification.from_pretrained('dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, including AutoModelForVideoClassification for the video classification model.\\n2. Load the pre-trained model 'dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset' using the from_pretrained method of AutoModelForVideoClassification. This model is specifically fine-tuned for video classification tasks and has been trained on a dataset consisting of RealLifeViolenceSituations.\\n3. Process the CCTV video feeds by extracting frames and preprocess them according to the model requirements.\\n4. Use the loaded model to analyze the video content for signs of violence.\\n<<>>: from transformers import AutoModelForVideoClassification\\nmodel = AutoModelForVideoClassification.from_pretrained('dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset')\\n# Preprocess video frames\\n# ...\\n# Then, use the model for video classification\\n# ...\\n###Code (for reference only) from the example_code provided in the API description:\\nfrom transformers import AutoModelForVideoClassification, AutoTokenizer\\nmodel = AutoModelForVideoClassification.from_pretrained('dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset')\\ntokenizer = AutoTokenizer.from_pretrained('dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset')\\ntokens = tokenizer(video_path, return_tensors=\\\"pt\\\")\\noutputs = model(**tokens)\\n\"", "answer_id": "EiakwM5Rt27HiEd3AU92yG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 376, "text": "<<>>: Computer Vision Video Classification\\n<<>>: AutoModelForVideoClassification.from_pretrained('sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the transformers package. This includes AutoModelForVideoClassification for the video classification model.\\n2. We then use the from_pretrained method of the AutoModelForVideoClassification class to load the pre-trained model 'sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset'. This model has been fine-tuned for video classification tasks, which is exactly what we need for our sports highlight generator.\\n3. We can then use this model to analyze video clips and classify the sports activities happening in them.\\n<<>>: from transformers import AutoModelForVideoClassification\\nvideo_classifier = AutoModelForVideoClassification.from_pretrained('sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset')\\nsports_categories = ['soccer', 'basketball', 'tennis', 'swimming', 'gymnastics']\\n# Add your own categories if needed\\nresult = video_classifier(video_clip)\\nvideo_category = result[0]['label']\\n\"", "answer_id": "2GfPirngb38YqzMNomSZDu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 377, "text": "<<>>: Computer Vision Video Classification\\n<<>>: TimesformerForVideoClassification.from_pretrained('facebook/timesformer-hr-finetuned-k400')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes and libraries, such as AutoImageProcessor, TimesformerForVideoClassification, numpy, and torch.\\n2. Load the pre-trained Timesformer model 'facebook/timesformer-hr-finetuned-k400', which is capable of classifying videos into one of the 400 possible Kinetics-400 labels.\\n3. Use the AutoImageProcessor class to preprocess the video frames (convert them to tensors) and then pass them to the Timesformer model.\\n4. The model will output logits for each class, and we can obtain the predicted class index by selecting the class with the highest logit score.\\n5. Finally, map the predicted class index to its corresponding label using the model's config.id2label dictionary.\\n6. The result is the genre of the movie based on its actions.\\n<<>>: from transformers import AutoImageProcessor, TimesformerForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 448, 448))\\nprocessor = AutoImageProcessor.from_pretrained('facebook/timesformer-hr-finetuned-k400')\\nmodel = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-hr-finetuned-k400')\\ninputs = processor(images=video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "3TvGpEumA4yasLxqEpfi63", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 378, "text": "<<>>: Computer Vision Image Classification\\n<<>>: timm.create_model('convnextv2_huge.fcmae_ft_in1k', pretrained=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary libraries: Image from PIL, timm for the image classification model, and urlopen from urllib.request for downloading the image.\\n2. We then create an instance of the Image class from the URL provided to load the image.\\n3. We create the pre-trained ConvNeXt-V2 image classification model using timm.create_model and setting pretrained to True.\\n4. We apply the model to the input image and obtain the classification output that indicates if the image contains a bike or a car.\\n<<>>: from urllib.request import urlopen\\nfrom PIL import Image\\nimport timm\\nimg = Image.open(urlopen('https://your_image_url_here'))\\nmodel = timm.create_model('convnextv2_huge.fcmae_ft_in1k', pretrained=True)\\nmodel = model.eval()\\ndata_config = timm.data.resolve_model_data_config(model)\\ntransforms = timm.data.create_transform(**data_config, is_training=False)\\noutput = model(transforms(img).unsqueeze(0))\\nbike_or_car_prediction = output.argmax(dim=1).item()\\n\"", "answer_id": "mxkchrRWU4GPJwPzRT9AFA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 379, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='finiteautomata/bertweet-base-sentiment-analysis')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a sentiment analysis model, specifying the model 'finiteautomata/bertweet-base-sentiment-analysis'. This model has been trained on tweets and can predict the sentiment of text in the form of a binary value (positive or negative).\\n3. We then use the model to analyze the sentiment of tweets mentioning specific products to determine which products are receiving positive customer feedback.\\n<<>>: from transformers import pipeline\\nsentiment_analyzer = pipeline('sentiment-analysis', model='finiteautomata/bertweet-base-sentiment-analysis')\\nproduct_tweets = ['I love the new iPhone X!', 'The battery life on the Galaxy S9 is great.', 'The update broke my Pixel 2!']\\nsentiments = sentiment_analyzer(product_tweets)\"", "answer_id": "EkBfaqg9r5dEoZSNQCUT9y", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 380, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='roberta-base-openai-detector')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'roberta-base-openai-detector'.\\n3. The loaded model will be used for text classification, a task where the model makes predictions about the category of the text.\\n4. The model will detect if the original text was generated by a GPT-2 model.\\n5. You can use this API to filter content moderation tasks by detecting if the text was generated by a GPT-2 model or not.\\n<<>>: from transformers import pipeline\\ndetector = pipeline('text-classification', model='roberta-base-openai-detector')\\ntext = 'Original text generated by GPT-2 or not?'\\nresult = detector(text)\"", "answer_id": "b6f5psXjr8r2cULq4xBbbF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 381, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='zhayunduo/roberta-base-stocktwits-finetuned')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text classification model, specifying the model 'zhayunduo/roberta-base-stocktwits-finetuned'. This model is specifically designed for analyzing sentiments in stock-related comments.\\n3. The created classifier can be used to analyze the sentiment of the provided comments related to stocks.\\n4. The output will be the sentiment classification for each stock comment.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('text-classification', model='zhayunduo/roberta-base-stocktwits-finetuned')\\ncomments = ['just buy', 'just sell it', 'entity rocket to the sky!', 'go down', 'even though it is going up, I still think it will not keep this trend in the near future']\\nresults = classifier(comments)\\nprint(results)\"", "answer_id": "NToMaNURAaVVGMPbs2ZvuU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 382, "text": "Here's a simple, short plant care instruction for your friend: Water your potted plant when the top layer of soil is dry, and use a balanced fertilizer once a month during the growing season.\"", "answer_id": "HYg3QQYPVHVUwtJnK45yhQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 383, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('d4data/biomedical-ner-all')\\n<<>>: Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes AutoModelForTokenClassification for the named entity recognition model and AutoTokenizer for tokenizing the text input.\\n2. We then use the from_pretrained method of the AutoModelForTokenClassification class to load the pre-trained model 'd4data/biomedical-ner-all'. This model has been specifically trained for biomedical Named Entity Recognition tasks, making it ideal for extracting biomedical entities from case reports.\\n3. We also load the corresponding tokenizer with AutoTokenizer.from_pretrained().\\n4. Case reports can be tokenized and passed through the NER model to extract biomedical entities.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('d4data/biomedical-ner-all')\\ntokenizer = AutoTokenizer.from_pretrained('d4data/biomedical-ner-all')\\ncase_report = \\\"Case report text goes here.\\\"\\ninputs = tokenizer(case_report, return_tensors='pt')\\noutputs = model(**inputs)\\nentities = tokenizer.batch_decode(outputs, as_target=True)\\n\"", "answer_id": "V4QGBNm2Lk7fxyy4kJcqgK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 384, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Davlan/bert-base-multilingual-cased-ner-hrl')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, which are `AutoTokenizer` and `AutoModelForTokenClassification`.\\n2. Load the pre-trained multilingual model from the Hugging Face model hub, using the model 'Davlan/bert-base-multilingual-cased-ner-hrl'. This model is capable of recognizing entities such as people, organizations, and locations in multiple languages.\\n3. Create a tokenizer using the same model, which is responsible for converting text into tokens (inputs) that can be fed into the model.\\n4. Use the loaded model and tokenizer to extract entities from the given text in different languages.\\n5. Iterate through the tokens and filter the ones with high entity confidence scores corresponding to people, organizations, and locations.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('Davlan/bert-base-multilingual-cased-ner-hrl')\\nmodel = AutoModelForTokenClassification.from_pretrained('Davlan/bert-base-multilingual-cased-ner-hrl')\\nner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy='simple')\\nexample_text = \\\"Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute.\\\"\\nner_results = ner_pipeline(example_text)\\nprint(ner_results)\"", "answer_id": "WMJcbQhMNaDCi4vrLQUvSr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 385, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-company_all-903429548', use_auth_token=True)\\n<<>>: Hugging Face Transformers\\n<<>>:1. We import the required classes from the transformers library: AutoModelForTokenClassification and AutoTokenizer.\\n2. We load the pre-trained model 'ismail-lucifer011/autotrain-company_all-903429548' using the from_pretrained method, which is trained on the 'ismail-lucifer011/datasets/company_all' dataset for entity extraction tasks, specifically for company name extraction.\\n3. We tokenize the input text using the AutoTokenizer instance.\\n4. We pass the tokenized input to the model to identify and extract company names from the text.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-company_all-903429548', use_auth_token=True)\\ntokenizer = AutoTokenizer.from_pretrained('ismail-lucifer011/autotrain-company_all-903429548', use_auth_token=True)\\ninputs = tokenizer('The company XYZ is one of the top 3 companies in the world, with over 500,000 employees.', return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "dFA4cnLhuWFCdFWnLDBNx4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 386, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/ner-english-ontonotes-large')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries from the flair package, which includes Sentence and SequenceTagger.\\n2. Load the pre-trained 'flair/ner-english-ontonotes-large' model using the SequenceTagger.load() method. This model is designed for Named Entity Recognition (NER) tasks.\\n3. Create a Sentence object from the input paragraph using the Sentence() constructor, and use the tagger.predict() method to predict the NER tags for the sentence.\\n4. Iterate over the entities in the sentence using the get_spans() method, and print the entity information such as the type and position in the text.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/ner-english-ontonotes-large')\\nparagraph = \\\"Chatbot NLP Paragraph Here...\\\"\\nsentence = Sentence(paragraph)\\ntagger.predict(sentence)\\nfor entity in sentence.get_spans('ner'):\\n print(entity)\\n\"", "answer_id": "Ahsq6v5SvAvJK8FDqNStrW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 387, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-sql-execution')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers and pandas packages.\\n2. Load the pre-trained model 'microsoft/tapex-large-sql-execution' using the BartForConditionalGeneration class.\\n3. Create a pandas DataFrame to represent your data and specify the query.\\n4. Use the loaded model to generate an output based on the table and query provided to it.\\n5. Decode the output to get the answer to the query.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-large-sql-execution')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-sql-execution')\\ndata = {\\n \\\"year\\\": [1896, 1900, 1904, 2004, 2008, 2012],\\n \\\"city\\\": [\\\"Athens\\\", \\\"Paris\\\", \\\"St. Louis\\\", \\\"Athens\\\", \\\"Beijing\\\", \\\"London\\\"]\\n}\\ntable = pd.DataFrame.from_dict(data)\\nquery = \\\"Select the year when Beijing hosted the Olympic games\\\"\\nencoding = tokenizer(table=table, query=query, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nanswer = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\n\"", "answer_id": "bHvL5VQq62hK69hEVcMEeT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 388, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: pipeline('table-question-answering', model='dsba-lab/koreapas-finetuned-korwikitq')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the model, which in this case is 'dsba-lab/koreapas-finetuned-korwikitq'.\\n3. The loaded model will be used for table question answering, which is a task where the model answers questions based on a table input.\\n4. We can provide the table data in Korean and ask questions in Korean as well.\\n<<>>: from transformers import pipeline\\ntable_qa = pipeline('table-question-answering', model='dsba-lab/koreapas-finetuned-korwikitq')\\n# Replace 'your_table_data_here' with your table data in Korean\\nquestion = 'your_question_here' # Replace with your question in Korean\\nanswer = table_qa({'table': 'your_table_data_here', 'query': question})\\n\"", "answer_id": "UgP9Fo6NqnBWcTYHoX46Jz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 389, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\n<<>>: PyTorch Transformers\\n<<>>:1. Import the necessary libraries, which are 'TapexTokenizer', 'BartForConditionalGeneration' from transformers, and 'pandas' for working with data.\\n2. Load the pre-trained model 'microsoft/tapex-large-finetuned-wikisql' using the 'BartForConditionalGeneration.from_pretrained()' method and its corresponding tokenizer.\\n3. Prepare the data in the form of a pandas DataFrame representing your large dataset of financial transactions.\\n4. Define a SQL-like query, such as \\\"SELECT COUNT(*) FROM Transactions WHERE Date >= '2021/01/01' AND Date <= '2021/12/31'\\\" to get the desired information.\\n5. Tokenize the data and then generate the answer using the pre-trained model and tokenizer.\\n6. Decode the output to obtain the required information in a human-readable format.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\ndata = {'TransactionDate': ['2021/01/01', '2021/01/02', '2021/01/03'], 'Amount': [1000, 1500, 2000]}\\ndf = pd.DataFrame(data)\\nquery = \\\"SELECT COUNT(*) FROM Transactions WHERE Date >= '2021/01/01' AND Date <= '2021/12/31'\\\"\\nencoding = tokenizer(table=df, query=query, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nprint(tokenizer.batch_decode(outputs, skip_special_tokens=True))\\n\"", "answer_id": "LBkCvv47Ny3HwqR3aQTnW8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 390, "text": "\\nModel conversion is important because it gives freedom to the user and lets people easily switch between frameworks. Using the option to convert models between FARM and transformers, you can use whichever framework suits your needs best at any given time.\"", "answer_id": "XETMEyaQ2UeSR5ifSyAgi7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 391, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: BlipForQuestionAnswering.from_pretrained('Salesforce/blip-vqa-capfilt-large')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the required libraries: Image from PIL, requests for handling URL requests, and BlipProcessor and BlipForQuestionAnswering from transformers.\\n2. Import the necessary classes and functions from the transformers library, such as BlipProcessor and BlipForQuestionAnswering, and Image from PIL for handling image data.\\n3. Load the pre-trained BlipForQuestionAnswering model and the pre-trained BlipProcessor using the from_pretrained() method.\\n4. Obtain the image data from an URL or load an image from a local file.\\n5. Provide a description or question as input.\\n6. Process the image, question, and context using the pre-trained BlipProcessor.\\n7. Use the pre-trained BlipForQuestionAnswering model to generate answers based on the processed input.\\n8. Decode and display the generated answer.\\n<<>>:import requests\\nfrom PIL import Image\\nfrom transformers import BlipProcessor, BlipForQuestionAnswering\\nprocessor = BlipProcessor.from_pretrained('Salesforce/blip-vqa-capfilt-large')\\nmodel = BlipForQuestionAnswering.from_pretrained('Salesforce/blip-vqa-capfilt-large')\\nimg_url = 'https://example.com/image.jpg'\\nraw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\\nquestion = 'What color is the car in the picture?'\\ninputs = processor(raw_image, question, return_tensors='pt')\\nout = model.generate(**inputs)\\nanswer = processor.decode(out[0], skip_special_tokens=True)\\nprint(answer)\"", "answer_id": "iGbjHsc4z4KNvxb3d4tUbb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 392, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='sultan/BioM-ELECTRA-Large-SQuAD2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Initialize the question-answering pipeline by specifying the model as 'sultan/BioM-ELECTRA-Large-SQuAD2'. This model is fine-tuned for the SQuAD2.0 dataset and is specialized for biomedical question answering tasks, which is ideal for the COVID-19 research project.\\n3. To use the pipeline, simply provide the context (a research paper, news article, or any other relevant text) and a question related to the COVID-19 pandemic or related research.\\n4. The model will return an answer based on the provided context.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='sultan/BioM-ELECTRA-Large-SQuAD2')\\ncontext = 'A research paper or news article related to COVID-19'\\nquestion = 'Your question about COVID-19 or related research'\\nresult = qa_pipeline({'context': context, 'question': question})\\n\"", "answer_id": "h3TCSj4qECnBv35sMPY6pw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 393, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='deepset/roberta-large-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the 'pipeline' function from the transformers library provided by Hugging Face.\\n2. Use the 'pipeline' function to create a question-answering model, specifying the model 'deepset/roberta-large-squad2' to be loaded.\\n3. Use the created model to answer the question of how long it takes for Mars to orbit the sun by providing the question and relevant context.\\n4. The model will return the answer to the question, which can be used in the book about the solar system.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='deepset/roberta-large-squad2')\\ncontext = \\\"Mars is the fourth planet from the Sun and the second smallest planet in the Solar System. It is often referred to as the \\\"Red Planet\\\" due to its reddish color.\\\"\\nquestion = \\\"How long does it take for Mars to orbit the sun?\\\"\\nresult = qa_pipeline({'question': question, 'context': context})\\nanswer = result['answer']\"", "answer_id": "a7XKwsedJpho7NTNg7woJd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 394, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a visual question answering model.\\n3. Specify the model 'microsoft/git-base-vqav2' to be loaded. This model is trained to answer questions based on the content of images, making it suitable for your quiz app design.\\n4. The created model can be used to analyze images and answer questions based on their content.\\n5. To use the model, provide the image data as input and ask the question you want the model to answer based on the image content.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\nresult = vqa(image='path/to/image.jpg', question='What is in the image?')\\n# replace 'path/to/image.jpg' with the path to your image file\"", "answer_id": "2hwjNCE6Wzwq6mFPEpRdxN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 395, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes from the transformers package, which includes 'AutoTokenizer' and 'AutoModelForQuestionAnswering'.\\n2. Load the pre-trained Longformer model using the 'from_pretrained' method for the 'valhalla/longformer-base-4096-finetuned-squadv1' model.\\n3. Prepare the input for the model by providing the context and the question. You will need an tokenizer to tokenize the question and context, and then perform padding, truncation, and return tensors as needed.\\n4. Feed the input to the model and obtain answers in the form of start and end tokens.\\n5. Extract the answer from the context after the model returns.\\n6. This process can be used to answer any question regarding software products, given the appropriate context and question.\\n<<>>: import torch\\nfrom transformers import AutoTokenizer, AutoModelForQuestionAnswering\\ntokenizer = AutoTokenizer.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\nmodel = AutoModelForQuestionAnswering.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\ncontext = \\\"This is the context where the answer will be extracted.\\\"\\nquestion = \\\"What is the answer?\\\"\\nencoding = tokenizer(question, context, return_tensors='pt')\\ninput_ids = encoding['input_ids']\\nattention_mask = encoding['attention_mask']\\nstart_scores, end_scores = model(input_ids, attention_mask=attention_mask)\\nall_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())\\nanswer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]\\nanswer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))\"", "answer_id": "VcF7RUppjkzz3oL6TrYrZ7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 396, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='michellejieli/NSFW_text_classification')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text classification model for filtering inappropriate content.\\n3. Specify the model 'michellejieli/NSFW_text_classification' to be loaded. This model is trained on NSFW_text_classification and can help identify and filter out inappropriate content in messages.\\n4. The created classifier can be used to analyze and filter messages for unwanted content before they are sent.\\n<<>>: from transformers import pipeline\\nnsfw_classifier = pipeline('sentiment-analysis', model='michellejieli/NSFW_text_classification')\\nmessage_content = \\\"This is a test message.\\\"\\nresult = nsfw_classifier(message_content)\\n\"", "answer_id": "G2sGWqCxtVHmXzAQg9uyts", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 397, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: CrossEncoder('cross-encoder/nli-deberta-v3-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the CrossEncoder class from the sentence_transformers package.\\n2. Create an instance of the CrossEncoder class with the 'cross-encoder/nli-deberta-v3-small' model. This model is trained to determine the logical relationship between two sentences, such as contradiction, entailment, or neutral.\\n3. Use the predict method with the two sentences you want to check for logical relationship. The method will return three scores corresponding to each relationship. The highest score indicates the most likely relationship between the sentences.\\n4. Check the scores to determine the logical relationship between the sentences.\\n<<>>: from sentence_transformers import CrossEncoder\\nmodel = CrossEncoder('cross-encoder/nli-deberta-v3-small')\\nsentence1 = 'A man is eating pizza.'\\nsentence2 = 'A person is consuming food.'\\nscores = model.predict([(sentence1, sentence2)])\\nrelationship = ['contradiction', 'entailment', 'neutral'][scores.argmax()]\\nprint(relationship)\\n\"", "answer_id": "BVu2HxLqzc6RBVPZ5kTaxc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 398, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('typeform/distilbert-base-uncased-mnli')\\n<<>>: Transformers\\n<<>>: 1. Import the required libraries and functions from the transformers package. This includes 'pipeline' and 'AutoModelForSequenceClassification'.\\n2. Use the 'pipeline' function to load the pre-trained model 'typeform/distilbert-base-uncased-mnli' which is suitable for zero-shot classification tasks.\\n3. The model will classify the given text into one of the categories ('technology', 'literature', and 'science') based on semantic similarity.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='typeform/distilbert-base-uncased-mnli')\\nsentence = \\\"The book I am reading is fascinating and full of interesting insights.\\\"\\ncandidate_labels = ['technology', 'literature', 'science']\\nresult = classifier(sentence, candidate_labels)\\nprint(result)\"", "answer_id": "99DwqGXe5zmR7Nj2mceoKZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 399, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='MoritzLaurer/DeBERTa-v3-xsmall-nli-fever-trieste')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to load the MoritzLaurer/DeBERTa-v3-xsmall-nli-fever-trieste model, which is specific for zero-shot classification tasks and natural language inference.\\n3. Provide the model with two Russian sentences - the first as the premise and the second as the hypothesis - as input.\\n4. The model will then determine whether the second sentence logically contradicts the information provided by the first sentence.\\n<<>>: from transformers import pipeline\\nzc_pipeline = pipeline('zero-shot-classification', model='MoritzLaurer/DeBERTa-v3-xsmall-nli-fever-trieste')\\npremise = 'Russian sentence 1'\\nhypothesis = 'Russian sentence 2'\\nresult = zc_pipeline(hypothesis, hypothesis, labels=[-1], scores_threshold=0.95)\\n\"", "answer_id": "YE6QaCodCozC5SJUZmXmpj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 400, "text": "<<>>: Natural Language Processing Translation\\n<<>>: MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-es-en')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'MarianMTModel' and 'MarianTokenizer' from transformers.\\n2. Use the 'MarianTokenizer.from_pretrained()' function to load the tokenizer for the required model, which is 'Helsinki-NLP/opus-mt-es-en'.\\n3. Use the 'MarianMTModel.from_pretrained()' function to load the Spanish to English translation model.\\n4. Once the model and tokenizer are loaded, input sentences in Spanish can be tokenized and translated to English using the tokenizer and model together.\\n5. The resulting English translations can be obtained from the model's 'generate()' method.\\n<<>>: from transformers import MarianMTModel, MarianTokenizer\\nmodel = MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-es-en')\\ntokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-es-en')\\ntranslation = tokenizer.translate('Hola, esta es una prueba')\\n# replace 'Hola, esta es una prueba' with your Spanish sentence\\n\"", "answer_id": "K3gCQ5ZJxprWtEpCrUaxyq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 401, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('summarization', model='lidiya/bart-large-xsum-samsum')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'lidiya/bart-large-xsum-samsum' model for abstractive summarization.\\n3. The loaded model will be used for generating a summary of the given text, which resembles the style of SparkNotes.\\n4. The model will automatically summarize the input text, which can be your book, and return it in a concise and clear way.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='lidiya/bart-large-xsum-samsum')\\nbook_content = \\\"The book's content here...\\\"\\nsummary = summarizer(book_content, max_length=100, min_length=0)\\nprint(summary[0]['summary_text'])\"", "answer_id": "9KFuB9D9Cfem4WJdhqTwTK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 402, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: BarthezModel.from_pretrained('moussaKam/barthez-orangesum-abstract')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries from the transformers package provided by Hugging Face.\\n2. Load the Barthez model, which is specifically trained for abstract generation in French, using the from_pretrained method with the model name 'moussaKam/barthez-orangesum-abstract'.\\n3. Use the loaded model to generate a summary of the given French article.\\n<<>>: from transformers import BarthezTokenizer, BarthezModel\\ntokenizer = BarthezTokenizer.from_pretrained('moussaKam/barthez-orangesum-abstract')\\nmodel = BarthezModel.from_pretrained('moussaKam/barthez-orangesum-abstract')\\ninputs = tokenizer(\\\"summarize: \\\" + article_text, return_tensors=\\\"pt\\\")\\noutputs = model.generate(**inputs)\\nsummary = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "Akr92SuEVrJe258vm93wXg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 403, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('ToddGoldfarb/Cadet-Tiny', low_cpu_mem_usage=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelForSeq2SeqLM' from transformers.\\n2. Instantiate the tokenizer and the model using the 'from_pretrained' method, and load the 'ToddGoldfarb/Cadet-Tiny' model.\\n3. The loaded model will be used for generating responses in a chatbot-like interface.\\n4. Whenever a user asks a question, the tokenizer will be used to process the input and generate the appropriate input_ids and tokens for the model.\\n5. The model will then generate a response based on the input, and the tokenizer will be used to decode the output and produce the response in a human-readable format.\\n6. The created chatbot can be integrated into the school website to answer queries related to admissions, classes, teachers, and extracurriculars.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nmodel_name = 'ToddGoldfarb/Cadet-Tiny'\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name, low_cpu_mem_usage=True)\\nuser_input = \\\"Hello, I'm considering enrolling my child at your school. Can you tell me about your admissions process?\\\"\\ninput_ids = tokenizer(user_input, return_tensors='pt').input_ids\\noutput = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)\\nresponse = tokenizer.decode(output[0], skip_special_tokens=True)\\n\"", "answer_id": "JnciVkvCbG2FNTPhPPLMSH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 404, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5Model.from_pretrained('t5-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'T5Tokenizer' and 'T5Model' from transformers.\\n2. Load the pre-trained T5-Small model using the from_pretrained method.\\n3. Tokenize the input text (the long article) using the T5Tokenizer.\\n4. Generate the summary using the T5 model's generate function, decoding the resulting tokens into human-readable text.\\n5. Print the generated summary.\\n<<>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('t5-small')\\nmodel = T5Model.from_pretrained('t5-small')\\narticle = 'Your long article text here...'\\ninput_ids = tokenizer('summarize: ' + article, return_tensors='pt').input_ids\\ngenerated_tokens = model.generate(input_ids)\\nsummary = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)\\nprint(summary)\"", "answer_id": "VqyNfMVCKeTuSHbxY9pPHG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 405, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='Zixtrauce/BaekBot')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a conversational model.\\n3. Specify the model 'Zixtrauce/BaekBot', which is based on the GPT-2 architecture and trained for text generation, to be loaded.\\n4. The created chatbot can be used to generate human-like responses to frequently asked questions (FAQs) and assist customers with your products.\\n5. Provide a message as input to the chatbot, and it will generate a natural-sounding conversation response based on the input.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='Zixtrauce/BaekBot')\\ninput_message = \\\"What is the difference between renewable and non-renewable energy sources?\\\"\\nresponse = chatbot(input_message)[\\\"generated_text\\\"]\"", "answer_id": "bzxWhVf2R6MU5FYbkBdqxD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 406, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then use the pipeline function to create a text-generation model, using the 'Zixtrauce/BDBot4Epoch' pre-trained model.\\n3. We provide the instruction as input to the model along with the knowledge, which are concatenated with a ' ' separator.\\n4. The model generates a response based on the provided instruction and knowledge.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\ninstruction = \\\"What is the best way to choose a video game?\\\"\\nknowledge = \\\"Some factors to consider when choosing a video game are personal preferences, genre, graphics, gameplay, storyline, platform, and reviews from other players or gaming websites.\\\"\\nconversation = instruction + ' ' + knowledge\\nresponse = text_generator(conversation)\\nprint(response[0]['generated_text'])\"", "answer_id": "edyYHTfWTKCWHLLxo59dae", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 407, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5ForConditionalGeneration.from_pretrained('google/flan-t5-xxl')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes T5Tokenizer and T5ForConditionalGeneration.\\n2. Load the pre-trained model 'google/flan-t5-xxl' using the from_pretrained method of both T5Tokenizer and T5ForConditionalGeneration classes.\\n3. Prepare the input French sentence by specifying the source and target languages in parentheses: \\\"Je t\\u2019aime\\\" (translate French to English).\\n4. Tokenize the input sentence using the T5Tokenizer instance, and provide the tokenized input to the model.\\n5. Generate the translated output using the T5ForConditionalGeneration instance, and decode the output tokens to obtain the translated sentence in English.\\n<<>>: from transformers import T5Tokenizer, T5ForConditionalGeneration\\ntokenizer = T5Tokenizer.from_pretrained('google/flan-t5-xxl')\\nmodel = T5ForConditionalGeneration.from_pretrained('google/flan-t5-xxl')\\ninput_text = \\\"translate French to English: Je t\\u2019aime\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutputs = model.generate(input_ids)\\ntranslated_sentence = tokenizer.decode(outputs[0])\\n\"", "answer_id": "g2WWdggWh7t8fCcwWmXEKv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 408, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: LEDForConditionalGeneration.from_pretrained('MingZhong/DialogLED-base-16384')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'LEDForConditionalGeneration' and 'PADTForConditionalGeneration' from transformers.\\n2. Load the pre-trained DialogLED model using the 'LEDForConditionalGeneration.from_pretrained()' function with the provided model name 'MingZhong/DialogLED-base-16384'.\\n3. The loaded model is designed for long dialogue understanding and summarization. It supports multiple languages, such as English, Chinese, Japanese, and German.\\n4. Prepare the input text, which is a lengthy dialogue you want to summarize.\\n5. Call the 'generate()' method on the loaded model with the input text to produce a summary.\\n6. Decode the generated summary using the 'decode()' method of the LED model.\\n<<>>: from transformers import LEDForConditionalGeneration, PADTForConditionalGeneration\\nmodel = LEDForConditionalGeneration.from_pretrained('MingZhong/DialogLED-base-16384')\\ninput_text = \\\"Long dialogue you want to summarize here...\\\"\\nsummary = model.generate(input_text)\\nsummary_text = model.decode(summary[0])\"", "answer_id": "RoewBRQbiYrz7J5Lgjkf9e", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 409, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5ForConditionalGeneration.from_pretrained('google/byt5-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, including T5ForConditionalGeneration for the text-to-text generation model and AutoTokenizer for tokenizing the input and output texts.\\n2. Load the pre-trained model 'google/byt5-small' and the tokenizer using the from_pretrained methods.\\n3. Tokenize the input text (the English article) using the tokenizer, and set the return_tensors parameter to 'pt' (for PyTorch).\\n4. Generate the translated French text using the model's generate method, passing the input_ids and setting the max_length and num_return_sequences parameters for controlling the output length and the number of translations.\\n5. Decode the generated output using the tokenizer's decode method and skip special tokens.\\n<<>>: from transformers import T5ForConditionalGeneration, AutoTokenizer\\nmodel = T5ForConditionalGeneration.from_pretrained('google/byt5-small')\\ntokenizer = AutoTokenizer.from_pretrained('google/byt5-small')\\ninput_text = \\\"translate English to French: The quick brown fox jumps over the lazy dog\\\"\\ninput_ids = tokenizer('translate English to French:', return_tensors='pt').input_ids\\noutputs = model.generate(input_ids, max_length=32, num_return_sequences=1)\\ntranslated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "JvFvwqAiauxk6XFggpqSjE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 410, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelForSeq2SeqLM' from the transformers library.\\n2. Load the pretrained model 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3' using the 'AutoModelForSeq2SeqLM.from_pretrained()' function.\\n3. Use the tokenizer to tokenize the text input (the document) and convert it into tensors.\\n4. Feed the input tensors to the model, which will generate user queries based on the document text.\\n5. Decode the generated queries using the tokenizer's 'batch_decode' method.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ndocument = \\\"The document text goes here...\\\"\\ninputs = tokenizer(document, return_tensors='pt')\\noutputs = model.generate(inputs['input_ids'])\\ngenerated_queries = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\n\"", "answer_id": "EuohacQEW3DgNKHu7hrPdQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 411, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilroberta-base')\\n<<>>: Transformers\\n<<>>: 1. We first import the pipeline function from the transformers package. This will be used to create a fill-mask pipeline, which can complete the missing word in a given sentence.\\n2. We create the fill-mask pipeline using the 'distilroberta-base' model, which is a smaller, faster, and lighter version of the RoBERTa-base model, trained to understand the structure of the English language and complete masked sentences.\\n3. The pipeline can then be used to complete the masked sentence, generating a customer satisfaction slogan based on the input: \\\"Customer satisfaction is our top .\"\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilroberta-base')\\nresult = unmasker(\\\"Customer satisfaction is our top .\\\")\\nslogan = result[0]['token_str']\"", "answer_id": "a4eaiKDpWgk739zuBDKDc8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 412, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='camembert-base', tokenizer='camembert-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to create a fill-mask pipeline with the 'camembert-base' model and tokenizer.\\n3. The fill-mask pipeline is used to fill in the blanks (masked tokens) in given sentences.\\n4. In order to fill in the blank, the model will analyze the context of the sentence and provide the most appropriate word to complete the sentence.\\n<<>>: from transformers import pipeline\\ncamembert_fill_mask = pipeline('fill-mask', model='camembert-base', tokenizer='camembert-base')\\nresult = camembert_fill_mask('Le camembert est :')\"", "answer_id": "n4MSPVyCffCLFmxkMCo4sC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 413, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/paraphrase-mpnet-base-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the sentence-transformers library and import the SentenceTransformer class.\\n2. Initialize the SentenceTransformer model with the 'sentence-transformers/paraphrase-mpnet-base-v2' model.\\n3. Encode the description and document titles/abstracts/passages using the model's encode() method. This will generate embedding vectors that represent the documents in a high-dimensional space.\\n4. Compute the cosine similarity between the encoded description and the embeddings of each document. The higher the similarity score, the more similar the document.\\n5. Rank documents based on their similarity scores and return the top matching documents.\\n<<>>: from sentence_transformers import SentenceTransformer\\nimport numpy as np\\nfrom sklearn.metrics.pairwise import cosine_similarity\\ndescription = \\\"Description of the desired document content.\\\"\\nmodel = SentenceTransformer('sentence-transformers/paraphrase-mpnet-base-v2')\\ndoc_embeddings = model.encode(np.array(document_titles) + np.array(document_passages))\\nsimilarity_scores = cosine_similarity(doc_embeddings, description.reshape(1, -1))\\ntop_matches = np.argsort(-similarity_scores[0])[::-1][:2]\\n\"", "answer_id": "hgYpzsePYN4TEHEq8mFAa6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 415, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the sentence-transformers library using pip.\\n2. Import the SentenceTransformer class from the sentence_transformers package.\\n3. Initialize the SentenceTransformer model with 'sentence-transformers/all-MiniLM-L12-v2' as the pre-trained model.\\n4. Provide a list of sentences or song lyrics you want to compare.\\n5. Use the encode() method of the SentenceTransformer model to obtain embeddings for each sentence.\\n6. Compare the embeddings for similarity either using cosine similarity or any other similarity metric.\\n7. Based on the similarity measure, identify and return the similar sentences or song lyrics.\\n<<>>: from sentence_transformers import SentenceTransformer\\nsentences = [\\\"Love is blind and music is the language of the blind.\\\", \\\"Music is the language of the blind and love is the universal sound.\\\"]\\nmodel = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\nembeddings = model.encode(sentences)\\n\"", "answer_id": "JFpsGfRbLysTmxCFuxns9k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 416, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='prithivida/parrot_fluency_model')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the 'prithivida/parrot_fluency_model', a model designed for paraphrase-based utterance augmentation.\\n3. The loaded model will help you generate similar questions that users have submitted or may want the matching algorithm to ask.\\n4. Provide the model with a text input, which will be the question you want to suggest, and the model will generate similar questions.\\n<<>>: from transformers import pipeline\\nparrot = pipeline('text-classification', model='prithivida/parrot_fluency_model')\\nuser_question = \\\"What's your favorite color?\\\"\\nparrot_response = parrot(user_question)\\ngenerated_questions = parrot_response\\n\"", "answer_id": "RMERFfV2n2NxCneSU9vmRn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 417, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: pipeline('text2text-generation', model='Pi3141/DialoGPT-medium-PALPATINE2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' and 'AutoTokenizer' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model and tokenizer. The model to load is 'Pi3141/DialoGPT-medium-PALPATINE2', which is designed for conversational applications.\\n3. The loaded model will be used for generating a human-like conversational response to a given input message, which may contain sensitive information.\\n4. The tokenizer is used to convert the input message into tokens that the model can understand.\\n5. The model generates a response based on the input message, which can be read aloud to the user.\\n<<>>: from transformers import pipeline, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained('Pi3141/DialoGPT-medium-PALPATINE2')\\nmessage = \\\"I urgently need to know the password for my account.\\\"\\ninput_prompt = f\\\"{tokenizer.encode(message, return_tensors='pt')}: Read aloud {tokenizer.decode(input_prompt, skip_special_tokens=True)}\\\"\\ngenerated_response = pipeline('text2text-generation', model='Pi3141/DialoGPT-medium-PALPATINE2')(input_prompt)[0]\"", "answer_id": "YGzdymQzczrtfkE9ERUPGF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 418, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-zh-cv7_css10', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries and functions, including 'load_model_ensemble_and_task_from_hf_hub' from 'fairseq.checkpoint_utils', 'TTSHubInterface' from 'fairseq.models.text_to_speech', and 'IPython.display' module.\\n2. Load the pre-trained Chinese TTS model 'facebook/tts_transformer-zh-cv7_css10' using the 'load_model_ensemble_and_task_from_hf_hub' function.\\n3. Prepare the Chinese text to be converted into speech by creating a tts_text object and passing it through the get_model_input method.\\n4. Generate the speech waveform and the corresponding sample rate using the 'get_prediction' method for the prepared tts_text.\\n5. Play the generated audio waveform using IPython.display.Audio.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-zh-cv7_css10', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = \\\"\\u4f60\\u597d\\uff0c\\u6b22\\u8fce\\u6765\\u5230\\u6570\\u5b57\\u4e16\\u754c\\u3002\\\"\\ntts_text = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, tts_text)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "nvQ2L3RxwVKznHTjD6PNQL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 419, "text": "<<>>: Audio Text-to-Speech\\n<<>>: Tacotron2.from_hparams(source='speechbrain/tts-tacotron2-ljspeech')\\n<<>>: SpeechBrain\\n<<>>:1. Import the necessary classes and modules from the SpeechBrain library.\\n2. Load the pre-trained Tacotron2 model and the HiFi-TTS vocoder using their respective from_hparams methods.\\n3. Encode the text using the Tacotron2 model to produce a spectrogram. The encoded mel spectrogram will be used as input to the HiFi-TTS vocoder.\\n4. Generate the waveform using the HiFi-TTS vocoder's generate_speech method, and save the generated audio as a .wav file.\\n<<>>: from speechbrain.pretrained import Tacotron2, HiFiTTS\\nimport torchaudio\\ntacotron2 = Tacotron2.from_hparams(source='speechbrain/tts-tacotron2-ljspeech')\\nhifi_tts = HiFiTTS.from_hparams(source='speechbrain/tts-hifitters-ljspeech')\\ntext = \\\"Hello, this is a test run.\\\"\\nmel_output, mel_length, alignment = tacotron2.encode_text(text)\\nwaveforms = hifi_tts.generate_speech(mel_output, alignment=alignment)\\ntorchaudio.save('example_TTS.wav', waveforms.squeeze(1), 22050)\"", "answer_id": "j79XzufoPq8cNqWEjrmmjb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 420, "text": "<<>>: Audio Text-to-Speech\\n<<>>: pipeline('text-to-speech', model='SYSPIN/Telugu_Male_TTS')\\n<<>>: ESPnet\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'SYSPIN/Telugu_Male_TTS'. This model is designed to synthesize Telugu text into speech.\\n3. The loaded model can then be used to generate speech from the provided Telugu text, such as mantras or prayers. This would allow you to teach kids how to pronounce conventional holy prayers in Telugu.\\n<<>>: from transformers import pipeline\\ntext_to_speech = pipeline('text-to-speech', model='SYSPIN/Telugu_Male_TTS')\\nmantra = \\\"\\u0c24\\u0c46\\u0c32\\u0c41\\u0c17\\u0c41\\u0c17\\u0c41 \\u0c2a\\u0c3e\\u0c20\\u0c02\\\"\\nspeech = text_to_speech(mantra)\"", "answer_id": "GWJtubgCY8yG9Np3PaDYwp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 421, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10')\\n<<>>: Fairseq\\n<<>>: 1. Import the required libraries and functions from fairseq, huggingface_hub, and torchaudio.\\n2. Load the pre-trained multilingual text-to-speech model 'facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10' using the load_model_ensemble_and_task_from_hf_hub function.\\n3. This model supports English, French, Spanish, and Italian languages. To convert text to speech in one of these languages, pass the text to be spoken in that language to the model.\\n4. The model will generate a speech waveform that can be played back using the play_wav function.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_fr_css10')\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = \\\"Bonjour, ceci est une exp\\u00e9rience multim\\u00e9dienne de texte au format LM-TTS.\\\"\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\\n\"", "answer_id": "HBgBdRyF7wFfBnJNuEkee3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 422, "text": "[Japanese] \\u3053\\u3093\\u306b\\u3061\\u306f\\u3001\\u79c1\\u305f\\u3061\\u306f\\u3042\\u306a\\u305f\\u306e\\u52a9\\u3051\\u304c\\u5fc5\\u8981\\u3067\\u3059\\u3002\"", "answer_id": "Xqcpy4yxSjKbPWyyrDWyWN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 423, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: PunctuationModel()\\n<<>>: Transformers\\n<<>>: 1. To add punctuation to transcripts, we can use the 'oliverguhr/fullstop-punctuation-multilang-large' model available through the Hugging Face Transformers library.\\n2. First, install the 'deepmultilingualpunctuation' package, as it is required to use the model.\\n3. Import the PunctuationModel class from the deepmultilingualpunctuation package.\\n4. Create an instance of the PunctuationModel class and use it to add punctuation to the transcript text.\\n5. The model should be trained to understand the 12 languages that the model is capable of punctuating: German, Spanish, French, Italian, Latin, Dutch, Swedish, Danish, Norwegian, Portuguese, Brazilian Portuguese, and English.\\n<<>>: from deepmultilingualpunctuation import PunctuationModel\\nmodel = PunctuationModel()\\ntranscript = \\\"Hello, I'm a model podcast.\\\"\\nresult = model.restore_punctuation(transcript)\\nprint(result)\\n\"", "answer_id": "G4ifoBgAeQKKx8FAMMdnwy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 424, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: HubertForCTC.from_pretrained('facebook/hubert-large-ls960-ft')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries: Wav2Vec2Processor from transformers, HubertForCTC from transformers, and load_dataset from datasets.\\n2. Load the pre-trained Wav2Vec2Processor and HubertForCTC model using the from_pretrained method with the 'facebook/hubert-large-ls960-ft' model name.\\n3. Load an audio dataset and extract the audio files and sampling rate from each audio entry.\\n4. Process the audio files using the Wav2Vec2Processor to create input values for the HubertForCTC model.\\n5. Feed the input values into the HubertForCTC model and obtain the logits.\\n6. Identify the most likely text transcription by taking the argmax of the logits and decode the prediction using the processor.\\n7. Analyze the text transcription from the audio file for further analysis.\\n<<>>:import torch\\nfrom transformers import Wav2Vec2Processor, HubertForCTC\\nfrom datasets import load_dataset\\nprocessor = Wav2Vec2Processor.from_pretrained('facebook/hubert-large-ls960-ft')\\nmodel = HubertForCTC.from_pretrained('facebook/hubert-large-ls960-ft')\\nds = load_dataset('your_audio_dataset', 'sampling_rate', split='validation')\\nsampling_rate = ds.argsample()['sampling_rate']\\naudio_file = ds[0]['file']\\ninput_values = processor(audio_file, sampling_rate=sampling_rate, return_tensors='pt').input_values\\nlogits = model(input_values).logits\\npredicted_ids = torch.argmax(logits, dim=-1)\\ntranscription = processor.decode(predicted_ids[0])\"", "answer_id": "W3ziwBG47he8MtUQdsv4WH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 426, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required BaseModel class from the transformers package.\\n2. Load the pretrained model 'JorisCos/DCUNet_Libri1Mix_enhsingle_16k' using the from_pretrained method of the BaseModel class. This model is designed for speech enhancement tasks.\\n3. Apply the model to an audio file containing speech that needs to be enhanced.\\n4. The model will process the input audio and return an enhanced version of the speech, which can be saved as a new output file.\\n<<>>: from transformers import BaseModel\\nmodel = BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\nenhanced_speech = model(input_audio_file_path)\\n\"", "answer_id": "c3yToqTkFgmY9a5vBwwjR3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 427, "text": "1. Import the Asteroid library.\\n2. Load the pre-trained ConvTasNet_Libri2Mix_sepclean_8k model for speech separation using the Asteroid 'load_model' function.\\n3. Create a function called 'separate_file' that will separate the speakers in an audio file using the loaded model.\\n4. Call the 'separate_file' function with the path to the audio file you want to separate as an argument.\\n5. Save the separated audio sources as separate output files using the 'torchaudio.save' function.\\n<<>>: from asteroid import ConvTasNet_Libri2Mix_sepclean_8k\\nimport torchaudio\\nmodel = ConvTasNet_Libri2Mix_sepclean_8k()\\nseparate_file = lambda filepath: model.separate(torchaudio.load(filepath))\\nseparated_sources = separate_file('input_audio.wav')\\n# replace 'input_audio.wav' with path to your audio file\\ntorchaudio.save('source1hat.wav', separated_sources[0].detach().cpu(), 8000)\\ntorchaudio.save('source2hat.wav', separated_sources[1].detach().cpu(), 8000)\"", "answer_id": "2bcN2Udkyo6APk3DLL9q42", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 428, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-to-audio', model='facebook/textless_sm_cs_en')\\n<<>>: Fairseq\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio-to-audio translation model.\\n3. Specify the model 'facebook/textless_sm_cs_en' to be loaded. This model is specifically designed for speech-to-speech translation between Spanish and English, which is what we need for translating the guide's language in real-time.\\n4. The created model can be used to perform real-time translation of the guide's speech into English, making the tourist experience more enjoyable and accessible.\\n<<>>: from transformers import pipeline\\nsignal_to_audio = pipeline('audio-to-audio', model='facebook/textless_sm_cs_en')\\ntranslated_audio = signal_to_audio(input_audio)\\n\"", "answer_id": "HX3uFt3j3xbzL4uCKiUrSk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 429, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_s2ut_en-hk')\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries and functions, including load_model_ensemble_and_task_from_hf_hub from fairseq.checkpoint_utils and the model 'facebook/xm_transformer_s2ut_en-hk' from Hugging Face Hub.\\n2. Load the speech-to-speech translation model from Hugging Face Hub using the model name 'facebook/xm_transformer_s2ut_en-hk'. This model is designed specifically for translating English speech to Hokkien speech.\\n3. Use the loaded model and task to generate an audio description for an English audio file that will be translated into Hokkien and played back as spoken audio.\\n4. Run the task with the input English audio file and get the translated Hokkien audio file as output.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nfrom huggingface_hub import snapshot_download\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_s2ut_en-hk', arg_overrides={'config_yaml': 'config.yaml', 'task': 'speech_to_text'})\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\ngenerator = task.build_generator([model], cfg)\\nenglish_audio = 'english_audio.wav'\\nsnapshot_download('facebook/xm_transformer_s2ut_en-hk@release', root='fairseq')\\nhokkien_audio = S2THubInterface.get_model_input(task, english_audio)\\ntranslated_audio = S2THubInterface.get_prediction(task, model, generator, hokkien_audio)\\n\"", "answer_id": "9hmZgUQaBJLjzuyCYkvMKA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 430, "text": "1. Import the required libraries such as transformers, datasets, torch, torchaudio, and os.\\n2. Load the pre-trained model 'facebook/xm_transformer_s2ut_hk-en' using the transformers.load() function.\\n3. Load the configuration for the model using the torch.optim.load() function.\\n4. Set up a generator to handle data augmentation for the model using torchaudio.transforms.\\n5. Define the source speech audio and the sampling rate.\\n6. Translate the source speech to English using the model.generate() method.\\n7. Translate the translated text back to Hokkien using the HiFiGAN vocoder.\\n8. Write the translated Hokkien text to a file.\\n9. You can then use the translated Hokkien text for further processing or display.\\n### Code:\\nfrom transformers import xm_transformer_s2ut_hk_en\\nfrom datasets import load_dataset\\nimport torch\\nimport torchaudio\\nimport torch.optim as optim\\nfrom huggingface_hub import snapshot_download\\nmodel = xm_transformer_s2ut_hk_en.from_pretrained(\\n 'facebook/xm_transformer_s2ut_hk-en'\\n)\\nconfig = optim.unet_config(model_config)\\ndef translate_speech(source_audio):\\n vocoder = HiFiGANVocoder.from_pretrained(\\n 'facebook/xm_transformer_v2_gpt2_hk-en',\\n src_sampling_rate=source_audio[\\\"sampling_rate\\\"]\\n )\\n text_translation = vocoder.decode(vocoder.encode(source_audio[\\\"array\\\"], source_audio[\\\"sampling_rate\\\"], config, False))\\n return text_translation\\nsource_audio = {\\n \\\"array\\\": audio_data,\\n \\\"sampling_rate\\\": 16000\\n}\\ntranslated_text = translate_speech(source_audio)\\n\"", "answer_id": "hQd8XBw8DGc5ctUqizMi4w", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 431, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-es-css10')\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary functions and modules from the fairseq and IPython packages. This includes load_model_ensemble_and_task_from_hf_hub for loading the text-to-speech model and TTSHubInterface for generating audio from text.\\n2. Load the 'facebook/tts_transformer-es-css10' pre-trained model and its configuration using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Update the configuration with the data configuration using TTSHubInterface.update_cfg_with_data_cfg method.\\n4. Instantiate a generator using the task.build_generator method with the loaded model and configuration.\\n5. Update the model with the vocoder 'vpinto/tts-transformer-mhubert-vocoder' using TTSHubInterface.update_model_with_hparams method.\\n6. Prepare the Spanish text input (customer feedback) for sentiment analysis.\\n7. Get the sample rate from the configuration and convert the text to an audio array using TTSHubInterface.get_model_input method.\\n8. Use TTSHubInterface.get_prediction to generate the speech audio and IPython.display.Audio to play the audio.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-es-css10', arg_overrides={'vocoder': 'vpinto/tts-transformer-mhubert-vocoder'})\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = \\\"Hola, esta es una prueba.\\\"\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "nHPP5UsXDTHyuDP6Es3X37", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 432, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='padmalcom/wav2vec2-large-emotion-detection-german')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio classification model for emotion detection in German speech.\\n3. Specify the model 'padmalcom/wav2vec2-large-emotion-detection-german' which has been trained on the emo-DB 2.1 dataset and can classify eight different emotions: angry, calm, disgust, fearful, happy, neutral, sad, and surprised.\\n4. The created classifier can be used to classify emotions in German speech input, which can be useful for language learning or other applications.\\n<<>>: from transformers import pipeline\\nemotion_classifier = pipeline('audio-classification', model='padmalcom/wav2vec2-large-emotion-detection-german')\\nemotion_result = emotion_classifier(german_speech_file_path)\\n\"", "answer_id": "TvtH6KUWPiij6uMaKRMYKw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 433, "text": "<<>>: Audio Audio Classification\\n<<>>: EncoderClassifier.from_hparams(source='speechbrain/spkrec-xvect-voxceleb', savedir='pretrained_models/spkrec-xvect-voxceleb')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the speechbrain package. This includes the EncoderClassifier class for speaker verification.\\n2. We then use the from_hparams method of the EncoderClassifier class to load the pre-trained model 'speechbrain/spkrec-xvect-voxceleb'. This model has been trained for speaker verification tasks, which is exactly what we need for improving security in a voice assistant service.\\n3. With the model loaded, we can use it to perform speaker verification. This involves providing a speech audio file and asking the model to classify the speaker identity from the audio signal.\\n4. The model will return a set of speaker embeddings, and we can use these embeddings to verify if the speaker's identity matches the expected identity for increased security.\\n<<>>: from speechbrain.pretrained import EncoderClassifier\\nimport torchaudio\\nmodel = EncoderClassifier.from_hparams(source='speechbrain/spkrec-xvect-voxceleb', savedir='pretrained_models/spkrec-xvect-voxceleb')\\nsignal, fs = torchaudio.load('path_to_audio_file.wav')\\nembeddings = model.encode_batch(signal)\\n\"", "answer_id": "6kbNrMtwmr9wfhC7u4jA7k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 434, "text": "<<>>: Audio Audio Classification\\n<<>>: AutoModelForAudioXVector.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We need to import the required libraries from the transformers package. This includes AutoProcessor, AutoModelForAudioXVector, and the torch library.\\n2. We then use the from_pretrained method of the AutoModelForAudioXVector class to load the pre-trained model 'anton-l/wav2vec2-base-superb-sv'. This model is trained for speech verification and can identify speakers based on their voice.\\n3. The call center agent's voice can be recorded and then processed using the loaded model to generate the speaker embeddings. These embeddings can then be used to identify the person on the other end of the line during calls.\\n4. By comparing the recorded voice of the caller with the speaker embeddings, we can find a match and display the identity of the person on the other end of the line.\\n<<>>: from transformers import AutoProcessor, AutoModelForAudioXVector\\nimport torch\\nprocessor = AutoProcessor.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\nmodel = AutoModelForAudioXVector.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\ninput_values = processor(caller_audio, return_tensors='pt', padding='longest').input_values\\nlogits = model(input_values).logits\\npredicted_label = torch.argmax(logits, dim=-1)\\nspeaker_embeddings = model.config.id2label[predicted_label]\\n\"", "answer_id": "H48s77vyx2GQSNgTkCAVqv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 435, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: pyannote.audio\\n<<>>: 1. Import the necessary libraries, which are 'Pipeline' from pyannote.audio.\\n2. Use the 'Pipeline' function to load the pretrained speaker diarization model from pyannote.audio with the provided model name and access token.\\n3. Process the audio file using the loaded pipeline. The pipeline will return information about the speakers and their respective time intervals.\\n4. Iterate through the speaker and time intervals to extract the speaker labels and timestamps, and output the results.\\n<<>>: from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\ndiarization = pipeline('audio.wav')\\n# replace 'audio.wav' with the path to your audio file\\nfor speaker, interval in diarization:\\n print(f\\\"Speaker: {speaker}\\\\nInterval: {interval}\\\")\"", "answer_id": "oMkvR5miJemsjUUGLBfrKb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 436, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='Bingsu/temp_decision_BERT_v3_base_w_mean_label_samuel_lipvala_2023')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Create a zero-shot classification model using the pipeline function and specify the model 'Bingsu/temp_decision_BERT_v3_base_w_mean_label_samuel_lipvala_2023'.\\n3. This model can be used to classify wine quality based on their textual descriptions.\\n4. You can provide the wine quality descriptions as input to the classifier along with the candidate labels \\\"red\\\" and \\\"white\\\" to categorize the wines.\\n5. The classifier will return the quality scores for each wine, which can be used to recommend them based on their quality.\\n<<>>: from transformers import pipeline\\nwine_classifier = pipeline('zero-shot-classification', model='Bingsu/temp_decision_BERT_v3_base_w_mean_label_samuel_lipvala_2023')\\nwine_descriptions = [\\\"Intense aromas of red fruit, soft tannins, and a long finish.\\\", \\\"Fresh floral notes, succulent acidity, and a crisp finish.\\\"]\\ncandidate_labels = ['red', 'white']\\nquality_scores = wine_classifier(wine_descriptions, candidate_labels=candidate_labels)\\n\"", "answer_id": "28ujpXTsyscYqXLywRodDq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 437, "text": "<<>>: Tabular Tabular Classification\\n<<>>: AutoModel.from_pretrained('abhishek/autotrain-adult-census-xgboost')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, such as json, joblib, and pandas.\\n2. Load the pre-trained XGBoost model 'abhishek/autotrain-adult-census-xgboost' using joblib.\\n3. Read the customer features (e.g., age, income, occupation) from a CSV file using pandas.\\n4. Predict whether a customer will make a purchase based on their browsing behavior using the trained model.\\n5. The model can classify customers based on their features and predict their purchase likelihood with a high degree of accuracy.\\n###code:\\n```python\\nimport json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('customer_data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n```", "answer_id": "ZdAN34eNcoCgJ5RpReYNdC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 438, "text": "<<>>: Tabular Classification\\n<<>>: TF Decision Trees\\n<<>>: Keras\\n<<>>: 1. Import the necessary libraries such as TensorFlow, Keras, and any required datasets for training.\\n2. Create a model using the TensorFlow library, which can be trained for binary classification using any available dataset.\\n3. The model should use TensorFlow decision trees to make predictions based on the input features.\\n4. To classify a person's income, provide the required demographic information as input features to the model, and it will predict the income category with a certain level of accuracy.\\n<<>>: 1. Import the necessary libraries and modules.\\n2. Create the model using TensorFlow decision trees, and train it on a dataset appropriate for binary classification.\\n3. Use the trained model to classify a person's income based on their demographic information, provided as input features.\\n<<>>: import tensorflow as tf\\nfrom tensorflow.keras import layers\\nmodel = tf.estimator.DecisionTreesClassifier(input_features)\\n# Train the model on a dataset for binary classification\\nincome_category = model.predict(demographic_info)\\n\"", "answer_id": "TViVPHpTNbN3oXaHG9ez2t", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 439, "text": "<<>>: Tabular Tabular Classification\\n<<>>: load('path_to_folder/sklearn_model.joblib')\\n<<>>: Scikit-learn\\n<<>>:1. We will import the necessary functions and packages, including joblib for loading the pre-trained model and pandas for handling data.\\n2. We will load the pre-trained Scikit-learn classification model 'julien-c/skops-digits' using joblib.\\n3. The model has been trained to predict whether a given set of input features represents a successful or unsuccessful vacation based on the destination, accommodation, and travel style.\\n4. We can use this model to make predictions for a given set of client features (data) and provide suggestions to clients who may need additional support.\\n5. The model outputs will help the travel agency recommend suitable vacation packages for clients.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('path_to_folder/sklearn_model.joblib')\\n# replace 'client_features.csv' with the path of the input data file\\ndata = pd.read_csv('client_features.csv')\\npredictions = model.predict(data)\\n\"", "answer_id": "CTwV4rbPcPKUtACJZLiGYn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 440, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We first import the necessary libraries, which include json, joblib, and pandas.\\n2. We then load the pre-trained 'model.joblib' binary classification model using joblib.load(). This model has been trained to classify carbon emissions based on input features.\\n3. We load the feature configuration file 'config.json' that lists the required features for classification.\\n4. We read the input data (data.csv) using pandas and select the required features from it.\\n5. We then process and clean the input data according to the model's requirements and store it in a pandas DataFrame.\\n6. Finally, we use the 'predict()' method of the loaded model to classify the carbon emissions for each building in the input data.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "MLFw9PrWxWqyTfebQ3SiLz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 441, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import the required libraries, such as json, joblib, and pandas.\\n2. Load the pre-trained model using joblib.load() method.\\n3. Load the config file to get the list of features used in the model.\\n4. Read the tabular data (data.csv) using pandas and select the required features from the dataset.\\n5. Rename the columns of data according to the feature names used in the model.\\n6. Use the loaded model to predict the CO2 emissions categorization for each record in the dataset based on the material consumption.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "fsv8jGcRSui5WLi8uSYu7G", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 442, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We first import the necessary libraries, including joblib for loading the machine learning model and pandas for handling the data.\\n2. We then load the pre-trained model 'model.joblib', which has been trained to predict carbon emissions based on input features.\\n3. Next, we load the given data files ('data.csv' and 'target.csv'), which contain feature information for various facilities.\\n4. We extract the required features from each data file and combine them into a single dataset.\\n5. Finally, we use the model to predict the carbon emissions for each facility and output the results.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\nfeatures = ['feature1', 'feature2', 'feature3']\\ndata = data[features]\\npredictions = model.predict(data)\\n\"", "answer_id": "8oFPxw3ZPMEbusXnW6dQoh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 443, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We import the necessary libraries, including joblib for loading the model and pandas for handling the dataset.\\n2. We then load the pre-trained regression model 'model.joblib' which is trained to predict carbon emissions based on historical data.\\n3. We load the provided dataset (data.csv) with features such as population, area, emissions_base, etc.\\n4. We preprocess the dataset by setting the target column as 'y' and all other features as 'features'.\\n5. Finally, we use the loaded model to predict the carbon emissions for each row in the dataset.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "epaPmpkXM2tVjnRr8hoJSX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 444, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the required libraries, which are 'joblib', 'pandas', and 'json'.\\n2. Load the pre-trained model using joblib.load('model.joblib').\\n3. Read the CSV file containing the historic data using pandas.load_csv('data.csv').\\n4. Process the data to match the features required by the model. This can be done by selecting the appropriate columns from the original data.\\n5. Use the model to predict CO2 emissions for the given data.\\n<<>>: import joblib\\nimport pandas as pd\\nimport json\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\"", "answer_id": "Ds7yVvWSxHKLMAz5hxGfkS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 445, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the required libraries, including joblib and pandas.\\n2. Load the pre-trained model using the joblib.load function.\\n3. Load the feature configuration file that lists the required features for the prediction.\\n4. Read the historical electricity consumption data from a CSV file using pandas. This data should match the required features from the configuration file.\\n5. Ensure that the columns in the data match the required feature order by applying the correct label to each column.\\n6. Use the loaded model to predict electricity consumption for the given data.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('historical_data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "YekSctSxv6QcRyiP5unQnK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 446, "text": "<<>>: Reinforcement Learning\\n<<>>: mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n<<>>: ML-Agents\\n<<>>: 1. Install the required libraries: unity-ml-agents, deep-reinforcement-learning, and ML-Agents-SoccerTwos.\\n2. Use the ML-Agents tool to download the pre-trained poca agent for SoccerTwos, specifying the chosen model as 'Raiden-1001/poca-Soccerv7.1'.\\n3. Create a configuration file (YAML format) for the training environment, specifying parameters such as the environment, learning rate, and more.\\n4. Use the downloaded agent and the trained configuration file to create a training session using the mlagents-learn command. The --resume flag is used to load the pre-trained agent and continue training from the point it was saved.\\n5. The training session will run until the specified number of episodes have been completed, at which point the trained model will be saved back to the Hugging Face model hub.\\n<<>>: # Download the pre-trained agent\\n!mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n# Train the model\\n!mlagents-learn --resume\\n# After the training, save the model\\n!mlagents-save-to-hf --run-id= --repo-id='Raiden-1001/poca-Soccerv7.1'\\n\"", "answer_id": "DMmo5QG5bgrrfdKzmKgpcA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 447, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='HumanCompatibleAI/ppo-seals-CartPole-v1',filename='{MODEL FILENAME}.zip',)\\n<<>>: Stable-Baselines3\\n<<>>:1. First, we need to import the necessary libraries from the rl_zoo3 and stable_baselines3 packages. This includes load_from_hub for loading pre-trained models.\\n2. We then load the pre-trained PPO (Proximal Policy Optimization) agent using the load_from_hub function, specifying the repo_id 'HumanCompatibleAI/ppo-seals-CartPole-v1' and the filename of the trained model.\\n3. The PPO agent can be used to play the CartPole-v1 environment. We can evaluate its performance by running the environment for a specified number of timesteps and calculating the mean and standard deviation of the agent's rewards.\\n4. The mean reward and its standard deviation can be used to evaluate the agent's consistency in the CartPole-v1 environment.\\n<<>>: from rl_zoo3 import load_from_hub\\nfrom stable_baselines3 import PPO\\nmodel_filename = 'your_model_filename.zip'\\nmodel = load_from_hub(repo_id='HumanCompatibleAI/ppo-seals-CartPole-v1', filename=model_filename)\\nmean_reward, std_reward = evaluate_policy(model, n_timesteps=200)\\n\"", "answer_id": "LTPusUkFPgKaLPdig99gvt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 448, "text": "<<>>: Reinforcement Learning\\n<<>>: load_from_hub(repo_id='sb3/dqn-CartPole-v1', filename='{MODEL FILENAME}.zip')\\n<<>>: Stable-Baselines3\\n<<>>:1. We first install the required libraries, including rl_zoo3, stable-baselines3, and stable-baselines3-contrib.\\n2. We import the load_from_hub function from the rl_zoo3 package.\\n3. We use the load_from_hub function to download and load the pre-trained model 'sb3/dqn-CartPole-v1'. This model has been trained using the DQN algorithm on the CartPole-v1 environment, which is suitable for our production robots.\\n4. We can then use this model to create and train our virtual environment for the production robots.\\n<<>>: from rl_zoo3 import load_from_hub\\nfrom stable_baselines3 import DQN\\nfrom stable_baselines3.common.env_util import make_vec_env\\nmodel_filename = \\\"path/to/model/file.zip\\\"\\n# replace 'path/to/model/file.zip' with the path to your model file\\ncheckpoint = load_from_hub(repo_id=\\\"sb3/dqn-CartPole-v1\\\", filename=model_filename)\\nmodel = DQN.load(checkpoint)\\nenv = make_vec_env(\\\"CartPole-v1\\\", n_envs=1)\\n\"", "answer_id": "nNcf5SvEyBjQtc7LFs7Dbs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 449, "text": "<<>>: Natural Language Processing Feature Extraction\\n<<>>: BartModel.from_pretrained('gogamza/kobart-base-v2')\\n<<>>: PyTorch Transformers\\n<<>>: 1. Import the necessary libraries, including PreTrainedTokenizerFast and BartModel, from the transformers package.\\n2. Load the tokenizer and model using the from_pretrained method, with the model identifier 'gogamza/kobart-base-v2' and the tokenizer 'gogamza/kobart-base-v2'.\\n3. Tokenize the input Korean news article using the loaded tokenizer.\\n4. Extract the features from the tokenized input using the loaded model.\\n5. The resulting features can be used as input for your content recommendation service.\\n<<>>: from transformers import PreTrainedTokenizerFast, BartModel\\ntokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2')\\nmodel = BartModel.from_pretrained('gogamza/kobart-base-v2')\\ninput_text = \\\"\\ud55c\\uad6d\\uc5b4 \\ud14d\\uc2a4\\ud2b8 \\ub300\\ud558\\uc744.\\\" # Replace with the Korean news article text\\ninputs = tokenizer(input_text, return_tensors='pt')\\noutputs = model(**inputs)\\nfeatures = outputs.last_hidden_state\\n\"", "answer_id": "hSnF22mdQsThh5HMrGzKKD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 450, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: pipeline('image-to-image', model='GreeneryScenery/SheepsControlV5')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, such as transformers.\\n2. Use the pipeline function from transformers to load the pre-trained model 'GreeneryScenery/SheepsControlV5', which is designed for image-to-image tasks, including generating hashtags.\\n3. Provide the image URL and a desired text prompt, such as \\\"Getting ready for the #NaturePhotoContest.\\\"\\n4. The model will generate a hashtag based on the provided image and text prompt.\\n5. This can be useful for automatically generating hashtags for social media posts to improve performance.\\n<<>>: from transformers import pipeline\\nhashtag_generator = pipeline('image-to-image', model='GreeneryScenery/SheepsControlV5')\\nimage_url = 'https://example.com/image.jpg'\\ntext_prompt = 'Getting ready for the #NaturePhotoContest.'\\nhashtag = hashtag_generator(image_url, text_prompt)[0]['generated_hashtag']\\n\"", "answer_id": "Eb58u5YNZED937wQFsRkws", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 451, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Load the pre-trained model 'sentence-transformers/paraphrase-MiniLM-L6-v2' by creating an instance of the SentenceTransformer class.\\n3. Encode the given TV show descriptions into a 384-dimensional dense vector space using the model's .encode() method.\\n4. Calculate the cosine similarity between the embeddings of the TV show descriptions to find the most similar shows.\\n5. Return the most similar TV show descriptions based on the calculated similarity scores.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\ntv_show_descriptions = ['description1', 'description2', 'description3']\\n# replace with your TV show descriptions\\nmodel = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')\\nembeddings = model.encode(tv_show_descriptions)\\nsimilarity_matrix = cosine_similarity(embeddings)\\ntv_shows_similarity = {v: round(w, 2) for v, w in zip(tv_show_descriptions, similarity_matrix[0])}\\nmost_similar_shows = sorted(tv_shows_similarity, key=lambda x: x[1], reverse=True)[:3]\\n\"", "answer_id": "DSa6t8YRVxrsSZQEfqeD56", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 452, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='Salesforce/blip-vqa-capfilt-large')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a Visual Question Answering (VQA) pipeline using the 'Salesforce/blip-vqa-capfilt-large' model. This model is trained to answer questions based on images, and it can understand both text and visual data.\\n3. Use the VQA pipeline to process an image along with a question related to that image.\\n4. Provide the path to the image of your pet dogs, and ask a question related to that image.\\n5. The model will return an answer based on the visual content of the image and the question provided.\\n<<>>: from transformers import pipeline\\nvqa_pipeline = pipeline('visual-question-answering', model='Salesforce/blip-vqa-capfilt-large')\\nimage_path = 'path/to/your/dog_image.jpg'\\nquestion = 'How many dogs are in the picture?'\\nanswer = vqa_pipeline({'image': image_path, 'question': question})\\nprint(answer)\"", "answer_id": "esFKeg9KAszHSeUzeJXgcf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 453, "text": "1. Import the required libraries, which include PIL, pydal.io, and transformers.\\n2. Load the image using the PIL Image.open() function by providing the path to the image.\\n3. Convert the image into a format that the model can understand using the FeatureExtractor from the Hugging Face Transformers library.\\n4. Pass the extracted features to the Pix2Struct model.\\n5. The model will generate text based on the visual properties extracted from the image.\\n6. Finally, decode the generated text using the tokenizer.\\n###Code:\\n```python\\nfrom PIL import Image\\nfrom pydal.io import ImageIO\\nfrom transformers import FeatureExtractor, AutoModelForCausalLM, AutoTokenizer\\nimage_path = ''\\nimage = Image.open(image_path)\\nfeature_extractor = FeatureExtractor.from_pretrained('google/pix2struct-chartqa-base')\\nmodel = AutoModelForCausalLM.from_pretrained('google/pix2struct-chartqa-base')\\ntokenizer = AutoTokenizer.from_pretrained('google/pix2struct-chartqa-base')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n# Post-process the output to generate text based on the image\\n```\"", "answer_id": "eQd6Ea4s8sAR4LFote5QTG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 454, "text": "\\n1. We start by identifying the problem: users are unable to read street signs in a foreign language.\\n2. Next, we consider possible solutions that could address the issue, such as: \\n - Translate the text from the foreign language into the user's native language using a dictionary or translation app.\\n - Use a smartphone app that recognizes foreign language text and converts it into the user's native language (e.g., Google Translate)\\n - Use a camera to capture an image of the street sign and use OCR (optical character recognition) to extract the text in the user's native language\\n3. We then evaluate the potential solutions and choose the one that is most effective and efficient for the given situation.\\n4. Finally, we design the product that implements the selected solution, such as a wearable device with a camera and OCR capabilities that automatically translates street signs in foreign languages.\"", "answer_id": "bwG5vuJ2yQehaHQKusCD2d", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 455, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'microsoft/git-base-vqav2'. This model is capable of answering questions about images using the Visual Question Answering (VQA) technique.\\n3. Provide the image file path and a text question related to the image.\\n4. The model processes the image and the question and returns an appropriate answer.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\nimage_path = 'path/to/image.jpg'\\nquestion = 'What is in the image?'\\nanswer = vqa(image=image_path, question=question)\\n\"", "answer_id": "VZ3msCrUDgL6s3uivt75qX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 456, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes AutoTokenizer and AutoModelForQuestionAnswering from the transformers library.\\n2. Load the tokenizer and model using 'valhalla/longformer-base-4096-finetuned-squadv1' as the model name.\\n3. The loaded model is a Longformer question-answering model, which is capable of extracting answers from input documents when given a set of questions.\\n4. To use this model, tokenize the input question and document using the tokenizer, then feed the result into the model for prediction.\\n5. Finally, extract the start and end positions of the answer from the model's output, and convert these positions into a readable format.\\n<<>>: from transformers import AutoTokenizer, AutoModelForQuestionAnswering\\ntokenizer = AutoTokenizer.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\nmodel = AutoModelForQuestionAnswering.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\ninputs = tokenizer(question, context, return_tensors='pt')\\noutputs = model(**inputs)\\nstart = torch.argmax(outputs.start_logits)\\nend = torch.argmax(outputs.end_logits)\\nanswer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][start:end+1]))\\n\"", "answer_id": "nb3eMWD3n8imxRrivpQSEz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 457, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('document-question-answering', model='jinhybr/OCR-DocVQA-Donut')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the pipeline function from the transformers package provided by Hugging Face.\\n2. We then use the pipeline function to create a document-question-answering model. This model is capable of extracting information from textual documents and answering questions based on the content within the document.\\n3. We specify the model 'jinhybr/OCR-DocVQA-Donut' to be loaded. This model combines a vision encoder (Swin Transformer) and a text decoder (BART) to perform the document question answering task.\\n4. The created question-answering model can be used to extract answers to specific questions from the scanned document.\\n<<>>: from transformers import pipeline\\ndoc_vqa = pipeline('document-question-answering', model='jinhybr/OCR-DocVQA-Donut')\\nanswer = doc_vqa(question='What is the total amount due?', context=scanned_document_content)\\n\"", "answer_id": "h62eT3zLv3BpBmLtRcwcCs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 458, "text": "<<>>: Multimodal Graph Machine Learning\\n<<>>: AutoModel.from_pretrained('clefourrier/graphormer-base-pcqm4mv2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes AutoModel for loading the pre-trained Graphormer model.\\n2. We then use the from_pretrained method of the AutoModel class to load the pre-trained 'clefourrier/graphormer-base-pcqm4mv2' model. This Graphormer model has been trained on the PCQM4M-LSCv2 dataset and is designed for graph classification tasks, making it suitable for classifying molecular structures.\\n3. We preprocess the molecular dataset by converting it into a graph and creating the necessary attribute vectors.\\n4. This Graphormer model can then be used to classify the molecular structures in the dataset, which is essential for developing the new drug.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('clefourrier/graphormer-base-pcqm4mv2')\\n# Preprocess the molecular dataset and convert it into a graph\\n# ...\\n# Use the model for molecular classification\\n# ...\"", "answer_id": "BZoiLsWtMkw8jPGSWe89Y7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 459, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModelForImageClassification.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-054332')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, which include 'AutoModelForImageClassification' from transformers.\\n2. Load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221116-054332' using the AutoModelForImageClassification.from_pretrained() function.\\n3. Use the loaded model to estimate the depth of objects in an input image.\\n4. The input image should be processed to remove color information and converted into a suitable format for the model.\\n5. Feed the processed image into the model to obtain the depth estimation, which represents the distance between the objects in the image.\\n6. The depth estimation can be used by an autonomous vehicle to navigate the parking lot.\\n<<>>: from transformers import AutoModelForImageClassification\\nimport torch\\nfrom torchvision import transforms\\nmodel = AutoModelForImageClassification.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221116-054332')\\npreprocess = transforms.Compose([\\n transforms.ToTensor(), \\n transforms.Resize((224, 224)), \\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\\ninput_image = preprocess(image_path).unsqueeze(0)\\ndepth_estimation = model(input_image)\\n\"", "answer_id": "Aeuo4B33Zo9CvJcBjCWfhM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 460, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries: DPTForDepthEstimation and DPTFeatureExtractor from the transformers package, Image from PIL, and torch for tensor manipulation.\\n2. Load the pre-trained depth estimation model 'Intel/dpt-hybrid-midas' using the DPTForDepthEstimation.from_pretrained() method. This model is trained to estimate depth from single monocular images.\\n3. Load the feature extractor using DPTFeatureExtractor.from_pretrained() method.\\n4. Create a model input by passing an image through the feature extractor and obtaining the tensors.\\n5. Call the model with the inputs to get the depth estimation output in the form of a depth tensor.\\n6. Now, you can use this depth estimation tensor for tasks like object detection, collision avoidance, and path planning in autonomous vehicles.\\n<<>>: from transformers import DPTForDepthEstimation, DPTFeatureExtractor\\nfrom PIL import Image\\nimport torch\\nmodel = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas', low_cpu_mem_usage=True)\\nfeature_extractor = DPTFeatureExtractor.from_pretrained('Intel/dpt-hybrid-midas')\\nimage = Image.open(your_image_path)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\n\"", "answer_id": "H76MyrvHuNAwxC7Wt47ufW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 461, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-depth')\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary packages, including the ControlNetModel class from the diffusers package and Image class from the PIL package.\\n2. Use the from_pretrained method to load the pre-trained model 'lllyasviel/sd-controlnet-depth'. This model is specifically trained for depth estimation tasks.\\n3. Load the image data from a file or acquire it in real-time from a construction site camera.\\n4. Use the depth_estimator pipeline to estimate the depth map of the image.\\n5. The depth map can be useful for analyzing construction site conditions and optimizing workflows.\\n<<>>: from diffusers import ControlNetModel\\nfrom PIL import Image\\nimport numpy as np\\nfrom transformers import pipeline\\ndepth_estimator = pipeline('depth-estimation')\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with path to your image\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-depth')\\ninputs = depth_estimator(image)['predicted_depth']\\noutputs = controlnet(inputs)\\ndepth_map = outputs[0]\\n\"", "answer_id": "VUnKBeMToiRBcTL7sggU3E", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 462, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', model='saltacc/anime-ai-detect')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an image classification model.\\n3. Specify the model 'saltacc/anime-ai-detect' to be loaded. This model is designed to detect if an image is anime-style art created by humans or AI-generated.\\n4. The created classifier can be used to classify user-submitted images into anime-art-human or AI-generated categories to ensure the user-submitted image is an authentic anime artwork.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('image-classification', model='saltacc/anime-ai-detect')\\nresult = classifier(image)\\n\"", "answer_id": "iYVTAjGT9weFAxBZ2gCd8p", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 463, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft')\\n<<>>: Hugging Face\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a zero-shot image classification model.\\n3. Specify the model 'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft' to be loaded. This model is trained for zero-shot image classification tasks, which means it can classify images into categories it has not been explicitly trained on.\\n4. Provide the image and a list of class names to the model for classification. The model will then return the most likely class label for the given image.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('zero-shot-image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft')\\nclass_names = ['class1', 'class2', 'class3'] # list of class names to predict from\\nresult = image_classifier(image, class_names)\"", "answer_id": "j2GATRtfLfucBvC48DMe2d", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 464, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vits8')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We need to import the required classes from the transformers and PIL packages. This includes ViTFeatureExtractor for the image processing part and ViTModel for the image classification model.\\n2. We then use the from_pretrained method of the ViTModel class to load the pre-trained model 'facebook/dino-vits8'. This model has been trained for image classification tasks, which is exactly what we need for categorizing products based on their images.\\n3. We load the image data from a file or URL, which will be used to classify the product.\\n4. We create an instance of the ViTFeatureExtractor class to preprocess the image before passing it to the model.\\n5. The model is then used to classify the image and output the relevant category.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimage = Image.open('product_image_path.jpg')\\n# replace 'product_image_path.jpg' with path to your image\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits8')\\nmodel = ViTModel.from_pretrained('facebook/dino-vits8')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\\n\"", "answer_id": "MqD4EoPzjEmnigygRXLebp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 465, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-hard-hat-detection')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes from the ultralyticsplus package.\\n2. Create an instance of the YOLO object detection model by specifying the model 'keremberke/yolov8m-hard-hat-detection'. This model is trained to detect hard hats in images, which is useful for ensuring safety on a construction site.\\n3. Set the model overrides to adjust the confidence threshold, Intersection over Union (IoU) threshold, agnostic Non-Maximum Suppression (NMS), and maximum number of detections.\\n4. Use the model's predict() method to perform object detection on a given image.\\n5. Extract the bounding boxes and labels of the detected hard hats from the results.\\n6. Optionally, you can display the detection results on the input image for visualization.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-hard-hat-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path_to_image.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "ZBc294DbRfoACRdFgKEPXD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 466, "text": "<<>>: Computer Vision Object Detection\\n<<>>: DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers, torch, PIL, and requests packages. This includes DetrForObjectDetection for the object detection model, Image for processing image data, and requests for downloading the image from a URL.\\n2. We then use the from_pretrained method of the DetrForObjectDetection class to load the pre-trained model 'facebook/detr-resnet-101'. This model has been trained for object detection tasks, which is exactly what we need for detecting people in a surveillance camera feed.\\n3. We load the image data from a file, or it can be acquired in real-time from the surveillance camera feed.\\n4. This model can then be used to analyze an image and identify any unknown people entering the property.\\n<<>>: from transformers import DetrForObjectDetection\\nfrom PIL import Image\\nimport requests\\nimage = Image.open('surveillance_image.jpg')\\n# replace 'surveillance_image.jpg' with path to your image\\nmodel = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "bR8ptDhUxVWab6cdZiCkXn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 467, "text": "<<>>: Computer Vision Object Detection\\n<<>>: yolov5.load('keremberke/yolov5m-license-plate')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including 'yolov5' and 'render_result' from the yolov5 package.\\n2. Load the 'keremberke/yolov5m-license-plate' model using the yolov5.load() function. This model has been trained for license plate detection and recognition tasks.\\n3. Use the loaded model to analyze an image and detect license plate numbers.\\n4. Check whether the detected license plate numbers are authorized or not.\\n5. Render the detection results on the original image to visualize the results.\\n<<>>: import yolov5\\nmodel = yolov5.load('keremberke/yolov5m-license-plate')\\nmodel.conf = 0.25\\nmodel.iou = 0.45\\nmodel.agnostic = False\\nmodel.multi_label = False\\nmodel.max_det = 1000\\nimg = 'image_path.jpg' # replace with the path to the image containing the license plate\\nresults = model(img, size=640)\\npredictions = results.pred[0]\\nboxes = predictions[:, :4]\\nscores = predictions[:, 4]\\ncategories = predictions[:, 5]\\nrender = yolov5.render_result(model=model, image=img, result=results['pred'], size=640)\\nrender.show()\"", "answer_id": "nMt3KEcbDdUbHVdPPTCt3v", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 468, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-cityscapes-1024-1024')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include SegformerFeatureExtractor, SegformerForSemanticSegmentation, PIL, and requests.\\n2. Load the pre-trained Segformer model that is fine-tuned on the Cityscapes dataset using the from_pretrained method.\\n3. Load an image of an urban scene using the PIL library.\\n4. Extract features from the image using the SegformerFeatureExtractor.from_pretrained method.\\n5. Pass the extracted features to the pre-trained Segformer model.\\n6. The model will output logits for different semantic regions, which can be further processed to visualize or analyze the image.\\n<<>>: from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = SegformerFeatureExtractor.from_pretrained('nvidia/segformer-b0-finetuned-cityscapes-1024-1024')\\nmodel = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-cityscapes-1024-1024')\\nurl = 'https://example.com/path/to/urban_image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\n\"", "answer_id": "idmj8oxEnwqDi3VCQEy59n", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 469, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: DetrForSegmentation.from_pretrained('facebook/detr-resnet-101-dc5')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries: DetrFeatureExtractor, DetrForSegmentation from transformers, Image from PIL, and requests.\\n2. Load the image from a URL or local file using the Image.open() method from PIL and the requests library.\\n3. Create a feature extractor using DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-dc5').\\n4. Create a model instance using DetrForSegmentation.from_pretrained('facebook/detr-resnet-101-dc5').\\n5. Feature extract the input image using the feature extractor, and provide the extracted tensors as input to the model.\\n6. Process the model outputs to identify the clothing items present in the image.\\n<<>>: from transformers import DetrFeatureExtractor, DetrForSegmentation\\nfrom PIL import Image\\nimport requests\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-dc5')\\nmodel = DetrForSegmentation.from_pretrained('facebook/detr-resnet-101-dc5')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "G7E7uh2jkePxdRjDkd2C2s", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 470, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: OneFormerForUniversalSegmentation.from_pretrained('shi-labs/oneformer_ade20k_swin_tiny')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary classes from the transformers package, including OneFormerProcessor and OneFormerForUniversalSegmentation.\\n2. Load the pre-trained model 'shi-labs/oneformer_ade20k_swin_tiny' using the from_pretrained method. This model can perform semantic, instance, and panoptic segmentation tasks.\\n3. Create a processor using OneFormerProcessor.from_pretrained method, which will handle the image input and segmentation output.\\n4. Load the image to be segmented using the PIL library.\\n5. Use the processor and model to perform the desired segmentation task on the input image.\\n6. Process the output to obtain the specific segmentation masks required for your smart city project.\\n<<>>: from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation\\nfrom PIL import Image\\nimage = Image.open('input_image.jpg')\\n# replace 'input_image.jpg' with the path to your image\\nprocessor = OneFormerProcessor.from_pretrained('shi-labs/oneformer_ade20k_swin_tiny')\\nmodel = OneFormerForUniversalSegmentation.from_pretrained('shi-labs/oneformer_ade20k_swin_tiny')\\nsemantic_inputs = processor(images=image, task_inputs=['semantic'], return_tensors='pt')\\nsemantic_outputs = model(**semantic_inputs)\\npredicted_semantic_map = processor.post_process_semantic_segmentation(semantic_outputs, target_sizes=[image.size[::-1]])[0]\"", "answer_id": "K6HXnR3fmKJ8BcLMjPG7Rt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 471, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: YOLO('keremberke/yolov8m-pothole-segmentation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the required packages ultralyticsplus and ultralytics.\\n2. Import the YOLO class from the ultralyticsplus library.\\n3. Instantiate the YOLO model using the 'keremberke/yolov8m-pothole-segmentation' pre-trained model.\\n4. Set the model's overrides such as conf, iou, agnostic_nms, and max_det to adjust the detection thresholds.\\n5. Provide an image URL or a local image file to process.\\n6. Use the model.predict() method to get the detected pothole bounding boxes and masks.\\n7. Visualize the results using the render_result() function from the ultralyticsplus library.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-pothole-segmentation')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(results[0].masks)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "izUoFTbqPNid8SoBWsJh2E", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 472, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-openpose')\\n<<>>: Hugging Face\\n<<>>:1. We import the necessary classes from the diffusers, PIL, and controlnet_aux packages.\\n2. We create an instance of the OpenposeDetector and apply it to the image. This detector helps us identify the human pose in the given image.\\n3. We create a StableDiffusionControlNetPipeline using the pretrained 'runwayml/stable-diffusion-v1-5' model and configure it with the controlnet 'lllyasviel/sd-controlnet-openpose'.\\n4. We set the scheduler to UniPCMultistepScheduler with the appropriate scheduler configuration.\\n5. We enable memory-efficient attention and CPU offloading for the pipeline.\\n6. We pass the image with the identified human pose to the pipeline, which generates the estimated human pose in the form of a control image.\\n7. We create an instance of the StableDiffusionPipeline using the pretrained 'runwayml/stable-diffusion-v1-5' model and configure it with the controlnet 'lllyasviel/sd-controlnet-openpose'.\\n8. We set the scheduler to UniPCMultistepScheduler with the appropriate scheduler configuration.\\n9. We enable memory-efficient attention and CPU offloading for the pipeline.\\n10. We pass the text description, the initial image, and the control image to the pipeline, which generates the output image with the estimated human pose.\\n<<>>: from PIL import Image\\nfrom diffusers import StableDiffusionControlNetPipeline, StableDiffusionPipeline, ControlNetModel\\nfrom controlnet_aux import OpenposeDetector\\nfrom diffusers.utils import load_image\\nopenpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\\nimage = load_image('input_image_path.png')\\nimage = openpose(image)\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-openpose')\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nprompt = 'your prompt here'\\nimage = openpose(image)\\nimage = pipe(prompt, image, num_inference_steps=20).images[0]\\nimage.save('images/estimated_human_pose.png')\\n\"", "answer_id": "HZy9permLpnTjPBcuCm9B7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 473, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-mlsd')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, including the ControlNetModel class from the diffusers package.\\n2. Load the pre-trained model 'lllyasviel/sd-controlnet-mlsd' using the from_pretrained method of the ControlNetModel class. This model is designed for detecting straight lines and controlling diffusion models in image diffusion.\\n3. Use the loaded model to process your input image, making adjustments based on the desired lines and diffusion effects.\\n4. Save the edited image to your preferred output file.\\n<<>>: from diffusers import ControlNetModel\\nfrom PIL import Image\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-mlsd')\\nimage = Image.open('your_image.jpg')\\n# replace 'your_image.jpg' with your input image path\\nedited_image = controlnet(image)\\nedited_image.save('edited_image.png')\\n\"", "answer_id": "gi77ZQJBkYvcYj3kYxvSWD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 474, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: pipeline('image-to-image', model='GreeneryScenery/SheepsControlV5')\\n<<>>: Hugging Face\\n<<>>: 1. Import the 'pipeline' function from the transformers library provided by Hugging Face.\\n2. Use the 'pipeline' function to create an 'image-to-image' model, which is capable of generating diverse variations of an input image.\\n3. Specify the model 'GreeneryScenery/SheepsControlV5' to be loaded. This is a model that has been trained on a large dataset of images and can generate various styles of output images based on the input image provided to it.\\n4. The created model can be used to generate different styles or versions of the photographer's original image, giving him/her more artistic control over the final result.\\n<<>>: from transformers import pipeline\\nstylizer = pipeline('image-to-image', model='GreeneryScenery/SheepsControlV5')\\ninput_image = 'path/to/image/file'\\n# Replace 'path/to/image/file' with the actual image file path\\noutput_images = stylizer(input_image)\\n\"", "answer_id": "6oobFWEqN8ivvsHdnECx75", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 475, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-car-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the diffusers library, which is required for using the DDPMPipeline.\\n2. Import the DDPMPipeline class from the diffusers library.\\n3. Use the from_pretrained method to load the pre-trained model 'google/ddpm-car-256', which generates high-quality images of cars.\\n4. Generate a new image of a car using the loaded model and save the image to a file named 'ddpm_generated_image.png'.\\n5. This image can be used as a replacement for the used car images on the client's website.\\n<<>>: !pip install diffusers\\nfrom diffusers import DDPMPipeline\\nmodel_id = 'google/ddpm-car-256'\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\\n\"", "answer_id": "DFUJPKtcQYK8vyUqmLMauT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 476, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-cifar10-32')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the DDPMPipeline class from the diffusers package.\\n2. Load the pre-trained model 'google/ddpm-cifar10-32', which is designed for generating high-quality images of size 256x256 pixels.\\n3. Use the loaded DDPMPipeline object to generate an image.\\n4. Save the generated image to your desired location, such as a file named 'ddpm_generated_image.png'.\\n<<>>: from diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-cifar10-32')\\ngenerated_image = ddpm().images[0]\\ngenerated_image.save('ddpm_generated_image.png')\\n\"", "answer_id": "Jg3QtwGqtnLNkVLXydD7MS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 477, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DiffusionPipeline.from_pretrained('google/ncsnpp-celebahq-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the diffusers package, which includes the DiffusionPipeline for the unconditional image generation model.\\n2. Use the from_pretrained method of the DiffusionPipeline class to load the pre-trained model 'google/ncsnpp-celebahq-256'. This model has been trained for generating high-resolution images of human faces.\\n3. Generate a high-resolution image of a human face by calling the pipeline with the desired number of inference steps.\\n4. Save the generated image to a file, which can then be used as a product recommendation on your website.\\n<<>>: from diffusers import DiffusionPipeline\\nmodel_id = 'google/ncsnpp-celebahq-256'\\npipeline = DiffusionPipeline.from_pretrained(model_id)\\ngenerated_image = pipeline(num_inference_steps=200)\\ngenerated_image[0].save('recommended_product.png', 'PNG')\\n\"", "answer_id": "5ggcyBWjqPkKFWCzhiniqL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 478, "text": "<<>>: Computer Vision Video Classification\\n<<>>: TimesformerForVideoClassification.from_pretrained('fcakyon/timesformer-hr-finetuned-k400')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'TimesformerForVideoClassification' from transformers, 'numpy' and 'torch'.\\n2. Load the 'fcakyon/timesformer-hr-finetuned-k400' model using the 'TimesformerForVideoClassification.from_pretrained()' method.\\n3. The loaded model is a video classification model based on the Transformer architecture, designed for identifying actions or activities in a video clip.\\n4. To use the model, preprocess the video and convert it into a suitable format for the model.\\n5. Finally, use the 'model(tvars)' function to classify the video and obtain the predicted action.\\n<<>>: from transformers import TimesformerForVideoClassification, AutoImageProcessor\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(96, 3, 224, 224))\\nprocessor = AutoImageProcessor.from_pretrained('fcakyon/timesformer-hr-finetuned-k400')\\nmodel = TimesformerForVideoClassification.from_pretrained('fcakyon/timesformer-hr-finetuned-k400')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "hxvqdm6epje7b9Xx6QM5a5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 479, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, including VideoMAEFeatureExtractor and VideoMAEForVideoClassification.\\n2. Load the pre-trained model 'MCG-NJU/videomae-small-finetuned-ssv2' using the from_pretrained method of the VideoMAEForVideoClassification class.\\n3. Create a feature extractor using the VideoMAEFeatureExtractor.from_pretrained method. This extractor will preprocess the input video for the classification model.\\n4. Use the feature extractor to process the video and return tensors that can be passed to the classification model to obtain the output.\\n5. The output logits can be used to determine the class label of the video, which can be useful for classifying advertisement videos.\\n<<>>: from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nfeature_extractor = VideoMAEFeatureExtractor.from_pretrained('MCG-NJU/videomae-small-finetuned-ssv2')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-ssv2')\\ninputs = feature_extractor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\"", "answer_id": "AHYmpsnemxAUmHBBzGFNiJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 480, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers package, including VideoMAEImageProcessor and VideoMAEForVideoClassification.\\n2. We load a pre-trained VideoMAE model by using the from_pretrained method and specifying the model 'MCG-NJU/videomae-base-finetuned-kinetics'.\\n3. The model is capable of processing and classifying videos, and it has been fine-tuned on the Kinetics-400 dataset, which contains a wide range of sporting events.\\n4. Using this pre-trained model, the sports league can analyze game videos and extract information on game highlights.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "LutdXS7Jg2h3kt2VKU8BJm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 481, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-kinetics')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes, including VideoMAEImageProcessor and VideoMAEForVideoClassification from the transformers package, numpy for array manipulations, and torch for tensor operations.\\n2. Load the pretrained model using the VideoMAEForVideoClassification.from_pretrained() function, specifying the 'MCG-NJU/videomae-small-finetuned-kinetics' model.\\n3. Create a VideoMAEImageProcessor to preprocess video frames before they are fed into the model.\\n4. Process the input video and retrieve frame tensors, which will be fed into the model for classification.\\n5. Perform the video classification using the model, and obtain the predicted class index as the output.\\n6. Obtain the human-readable labels corresponding to the predicted class index using the model.config.id2label dictionary.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-small-finetuned-kinetics')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-kinetics')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "Xady3h6i7kPxZywLa63dhY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 482, "text": "<<>>: Computer Vision Video Classification\\n<<>>: XClipModel.from_pretrained('microsoft/xclip-base-patch16-zero-shot')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library provided by Hugging Face, including the XClipModel.\\n2. Use the from_pretrained method of the XClipModel class to load the pre-trained model 'microsoft/xclip-base-patch16-zero-shot'. This model has been trained for video classification tasks and can be used to categorize and classify videos.\\n3. Load the video data from a file or stream, then preprocess it as required by the model.\\n4. Use the XClipModel to analyze the video and predict a category. This can then be used to categorize and classify videos for your organization.\\n<<>>: from transformers import XClipModel\\nmodel = XClipModel.from_pretrained('microsoft/xclip-base-patch16-zero-shot')\\n# Load video data and preprocess as required\\nvideo_data = # Load your video data and preprocess it\\nprediction = model(video_data)\\nvideo_category = prediction.argmax(axis=1).item()\\n\"", "answer_id": "GFTu2xRtR47EfiYrktiCmU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 483, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes from the transformers package. This includes the VideoMAEImageProcessor and VideoMAEForPreTraining classes.\\n2. Load the pre-trained model 'MCG-NJU/videomae-base-short-ssv2' using the from_pretrained method of the VideoMAEForPreTraining class. This model has been trained for video classification tasks, which is what we need for analyzing surveillance videos.\\n3. Load the video data from a file or real-time stream.\\n4. Process the video frames using the VideoMAEImageProcessor, which will prepare them for use with the pre-trained model.\\n5. Use the pre-trained model to analyze the video frames and classify the activities taking place in the backyard.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining\\nimport numpy as np\\nimport torch\\nnum_frames = 16\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\nmodel = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\npixel_values = processor(video, return_tensors='pt').pixel_values\\nnum_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2\\nseq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame\\nbool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()\\noutputs = model(pixel_values, bool_masked_pos=bool_masked_pos)\\nloss = outputs.loss\\n\"", "answer_id": "VgyFsBERAtdAu7JNcBSmRT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 484, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: clip.load('timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k')\\n<<>>: Hugging Face\\n<<>>: 1. We import the necessary libraries, including the clip package provided by Hugging Face.\\n2. We then load the pre-trained model 'timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k' using the clip.load() function. This model is designed for zero-shot image classification tasks.\\n3. With the loaded model, we can now classify the image content by feeding the image and a list of class names to the model. The model will return the probability scores for each class.\\n4. We can then use these scores to determine whether the image contains a cat, a dog, or another object.\\n<<>>: from PIL import Image\\nfrom transformers import CLIPProcessor, CLIPFeatureExtractor\\nfrom clip.models import CLIPModel\\nmodel = CLIPModel.from_pretrained('timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k')\\nprocessor = CLIPProcessor.from_pretrained('timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k')\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with the path to your image\\nclass_names = ['cat', 'dog']\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs, class_names=class_names)\\nlogits_per_image = outputs.logits_per_image\\nprobs = logits_per_image.softmax(dim=1)\\n\"", "answer_id": "Fn4Byxw95md9fSMTUR4vZs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 485, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='hf-tiny-model-private/tiny-random-CLIPSegModel')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a zero-shot classification model.\\n3. Specify the model 'hf-tiny-model-private/tiny-random-CLIPSegModel' to be loaded. This model is capable of zero-shot classification, meaning it can classify examples into categories it has not been explicitly trained on.\\n4. Pass the image file and the possible class names to the created classifier. It will return the most probable plant type given in the provided class names.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='hf-tiny-model-private/tiny-random-CLIPSegModel')\\nresult = classifier(image='path/to/image/file', class_names='portulaca, purslane')\\n\"", "answer_id": "bZuosBbXct26RuDueFL9Mg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 486, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an image classification model.\\n3. Specify the model 'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k' to be loaded. This model is capable of zero-shot image classification, which means it can classify images into categories it has not been explicitly trained on.\\n4. The created classifier can be used to classify images into the desired categories: landscape, cityscape, beach, forest, and animals.\\n5. Provide the path to the image file and the class names as input to the classifier to organize the images.\\n<<>>: from transformers import pipeline\\nscene_classifier = pipeline('image-classification', model='laion/CLIP-ViT-bigG-14-laion2B-39B-b160k')\\nscene_categories = ['landscape', 'cityscape', 'beach', 'forest', 'animals']\\nclassification_results = scene_classifier('path/to/image.jpg', scene_categories)\\n\"", "answer_id": "BbnZU5s4D594gCfVkGZRos", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 487, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='laion/CLIP-ViT-g-14-laion2B-s34B-b88K')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary 'pipeline' function from the transformers library.\\n2. Create a zero-shot image classification pipeline using the 'laion/CLIP-ViT-g-14-laion2B-s34B-b88K' model, which is designed for classifying a variety of objects in images, similar to the objects in our electronic device inventory.\\n3. Pass the image path and class names to the pipeline to classify the images and organize products in your store.\\n4. The model will classify the product images and help you manage your inventory more efficiently.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('zero-shot-image-classification', model='laion/CLIP-ViT-g-14-laion2B-s34B-b88K')\\nclass_names = ['electronic device', 'cable', 'charger', 'headset', 'speaker']\\nclassified_image = image_classifier(image_path, class_names=class_names)\\n\"", "answer_id": "BgqReu26q6KA9o5FCGzcuw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 488, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\\n<<>>: Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create a sentiment analysis model, which is capable of determining the sentiment of the input text (positive or negative).\\n3. We specify the model 'finiteautomata/beto-sentiment-analysis' to be loaded. This is a pre-trained BERT-based model specifically designed for sentiment analysis in Spanish.\\n4. The created sentiment analysis model can be used to analyze the sentiment of the Spanish customer feedback.\\n<<>>: from transformers import pipeline\\nsentiment_analysis = pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\\nresult = sentiment_analysis(feedback_text)\\n\"", "answer_id": "HpkfCyPjxucd83jMP4BsAp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 489, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: RobertaForSequenceClassification.from_pretrained('zhayunduo/roberta-base-stocktwits-finetuned')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library: RobertaForSequenceClassification for sentiment analysis, and RobertaTokenizer for tokenizing the text.\\n2. Load the pretrained model 'zhayunduo/roberta-base-stocktwits-finetuned', which is specifically fine-tuned for sentiment analysis on stock forum data like StockTwits.\\n3. Use the RobertaTokenizer to tokenize the input text (stock forum sentiment analysis data).\\n4. Use the RobertaForSequenceClassification model to predict the sentiment of the tokenized text.\\n5. The prediction will return a sentiment label, which can be used to analyze and understand the market sentiment for a specific stock.\\n<<>>: from transformers import RobertaForSequenceClassification, RobertaTokenizer\\nimport torch\\ntokenizer = RobertaTokenizer.from_pretrained('zhayunduo/roberta-base-stocktwits-finetuned')\\nmodel = RobertaForSequenceClassification.from_pretrained('zhayunduo/roberta-base-stocktwits-finetuned')\\ntext = 'BUY' if stock_is_positive else 'SELL'\\ninputs = tokenizer(text, return_tensors='pt')\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\npredicted_label = logits.argmax(-1).item()\\nsentiment = model.config.id2label[predicted_label]\\n\"", "answer_id": "fi3M2jW9EeCXgHLHbBrMXr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 490, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='martin-ha/toxic-comment-model')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create a text classification model specialized in detecting toxic comments. This is achieved by loading the pre-trained model 'martin-ha/toxic-comment-model', which has been fine-tuned on a dataset of toxic comment data.\\n3. The created classifier can be used to assess user-generated comments and identify whether they are toxic or not.\\n4. This model can be integrated into the forum moderation process, helping to reduce the amount of toxic content on the platform.\\n<<>>: from transformers import pipeline\\ntoxic_comment_classifier = pipeline('text-classification', model='martin-ha/toxic-comment-model')\\ncomment_toxicity_result = toxic_comment_classifier(user_generated_comment)\\n\"", "answer_id": "RTbsmtFSUxsjvV5mkybVVF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 491, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/ner-english-ontonotes-fast')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'Sentence' from flair.data and 'SequenceTagger' from flair.models.\\n2. Use the 'SequenceTagger.load' function to load the 'flair/ner-english-ontonotes-fast' model. This model is designed for Named Entity Recognition (NER) tasks.\\n3. Create a 'Sentence' object using the text from the article, and then use the 'predict' method of the loaded tagger to predict the named entities.\\n4. Iterate over the detected entities, and for each entity, store the text, the entity type, and the position in the sentence.\\n5. This information can be used to analyze and process the news articles.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/ner-english-ontonotes-fast')\\narticle_text = 'Article text here...'\\nsentence = Sentence(article_text)\\ntagger.predict(sentence)\\nentities = sentence.get_spans('ner')\\n# Process the entities and analyze the news article\\n\"", "answer_id": "K7BF6hAdDwQTSB6ebDpEnt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 492, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-name_all-904029577', use_auth_token=True)\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library: AutoModelForTokenClassification for the model, and AutoTokenizer for tokenizing the input text.\\n2. Load the pre-trained model 'ismail-lucifer011/autotrain-name_all-904029577' using the from_pretrained method. This model has been trained for entity extraction tasks.\\n3. Load the tokenizer using the from_pretrained method with the same model name.\\n4. Tokenize the input text and pass the tokenized input through the model to extract entities.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-name_all-904029577', use_auth_token=True)\\ntokenizer = AutoTokenizer.from_pretrained('ismail-lucifer011/autotrain-name_all-904029577', use_auth_token=True)\\ninputs = tokenizer('Hello, my name is John Doe', return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "mEUwZRTb6ZUd7Ja9bdshtY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 493, "text": "[{'text': 'Empire State Building', 'entity': 'LOC'}, {'text': 'June 7th', 'entity': 'DATE'}, {'text': 'Jane Smith', 'entity': 'PER'}, {'text': '35 dollars', 'entity': 'MONEY'}]\\n\"", "answer_id": "crUhxaJ8vCMDc8xzjUR7wd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 494, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-name_all-904029577', use_auth_token=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, such as AutoModelForTokenClassification and AutoTokenizer.\\n2. Load the token classification model 'ismail-lucifer011/autotrain-name_all-904029577' using the from_pretrained() method. This model is trained to identify names and locations in text.\\n3. Load the tokenizer for this model with the same method, using the '--tokenizer' option.\\n4. Tokenize the online chat room text using the loaded tokenizer.\\n5. Pass the tokenized text to the loaded model to extract names and locations.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-name_all-904029577', use_auth_token=True)\\ntokenizer = AutoTokenizer.from_pretrained('ismail-lucifer011/autotrain-name_all-904029577', use_auth_token=True)\\ninputs = tokenizer(\\\"Online chat room text here\\\", return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "VZ9LZeBhRV9vq5DmTAyEKQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 495, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/ner-german')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We need to import the required classes from the flair package, which includes Sentence and SequenceTagger.\\n2. We then load the pre-trained model 'flair/ner-german' using the SequenceTagger.load() method. This model is capable of identifying entities like persons, locations, organizations, and other names in German text.\\n3. We create a Sentence object from the input text using the Sentence class.\\n4. We then use the predict() method of the tagger to predict the entities in the sentence.\\n5. We can print the detected entities by iterating over the sentence's spans and accessing the entities associated with each span.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/ner-german')\\nsentence = Sentence('Angela Merkel besuchte Berlin.')\\ntagger.predict(sentence)\\nprint(sentence)\\nfor entity in sentence.get_spans('ner'):\\n print(entity)\"", "answer_id": "2SVzRYbcdY7gPXpFSAvcRL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 496, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Davlan/bert-base-multilingual-cased-ner-hrl')\\n<<>>: Transformers\\n<<>>:1. Import the required classes from the transformers library: AutoTokenizer and AutoModelForTokenClassification.\\n2. Load the pre-trained multilingual model for named entity recognition by calling the from_pretrained method with the model name 'Davlan/bert-base-multilingual-cased-ner-hrl'.\\n3. Create a tokenizer using the AutoTokenizer.from_pretrained method with the same model name.\\n4. Tokenize the input text in the desired languages using the tokenizer's .encode() method, and then use the model to extract named entities.\\n5. The model is trained on a large corpus of text and can identify 18 different named entities across 9 languages, including person names, location names, organization names, and other miscellaneous names.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('Davlan/bert-base-multilingual-cased-ner-hrl')\\nmodel = AutoModelForTokenClassification.from_pretrained('Davlan/bert-base-multilingual-cased-ner-hrl')\\nnlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy='simple')\\nner_results = nlp(text)\\n\"", "answer_id": "MakJKVzGffHrMHvRHqcTai", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 497, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='monologg/koelectra-small-v2-distilled-korquad-384')\\n<<>>: Transformers\\n<<>>: 1. To incorporate question answering capability in your app, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a question-answering pipeline using the 'monologg/koelectra-small-v2-distilled-korquad-384' model. This Korean Question Answering model is trained on the KorQuAD dataset and can answer questions in Korean.\\n3. To use the pipeline, call it with a question and a context in which the answer can be found. The model will return the answer to the given question.\\n<<>>: from transformers import pipeline\\nnlp = pipeline('question-answering', model='monologg/koelectra-small-v2-distilled-korquad-384')\\nquestion = '\\uc9c8\\ubb38' # Replace with a Korean question\\ncontext = '\\ubb38\\ub9e5\\uc758\\ub294\\uc788\\ub294\\uc794' # Replace with a Korean context containing the answer\\nanswer = nlp(question=question, context=context)\\n\"", "answer_id": "DVPAv5Nfg2PxuVcabp3TQf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 498, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelWithLMHead' from transformers.\\n2. Use the 'from_pretrained' method for both the tokenizer and the model to load the pretrained model 'mrm8488/t5-base-finetuned-question-generation-ap' and its associated tokenizer.\\n3. Create a function called 'get_question' that takes an article summary and a question template as input. The function will tokenize the input and generate a question.\\n4. To create a quiz project, extract a relevant article summary, and use the 'get_question' function with a suitable question template, providing the extracted summary as input. The function will return a question with multiple options to choose from.\\n5. Add the generated question to your quiz project to test your users' knowledge.\\n<<>>: from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')\\nmodel = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')\\ndef get_question(summary, question_template):\\n input_text = f\\\"{question_template} {summary}\\\"\\n input_ids = tokenizer.encode(input_text, return_tensors='pt')\\n output = model.generate(input_ids, max_length=64, num_return_sequences=1)\\n question = tokenizer.decode(output[0], skip_special_tokens=True)\\n return question\\n\"", "answer_id": "XvruzprZvkbCm93tyezTx4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 499, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='finiteautomata/bertweet-base-sentiment-analysis')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. To determine the sentiment of the website text, we first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then use the pipeline function to create a text classification model specializing in sentiment analysis.\\n3. We load the 'finiteautomata/bertweet-base-sentiment-analysis' model, which is a BERTweet model trained on tweets and designed to identify positive, negative, and neutral sentiments.\\n4. The model will evaluate the website text and classify it as having either a positive or negative sentiment.\\n<<>>: from transformers import pipeline\\nsentiment_analysis = pipeline('text-classification', model='finiteautomata/bertweet-base-sentiment-analysis')\\nresult = sentiment_analysis('Technology is a wonderful thing that has the potential to improve our lives.')\\n\"", "answer_id": "D9XBXrcrSEoSJaiTd688Lx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 500, "text": "\\\"Welcome to our website! We are a specialized company in IT project management and custom software development. Our services include concept, development, maintenance, and 24/7 support. All rights reserved.\\\"\"", "answer_id": "mLFYkxHDoYyMhToVmNivUh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 501, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: pipeline('text2text-generation', model='google/t5-v1_1-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'google/t5-v1_1-base'.\\n3. The loaded model is capable of text-to-text generation and can be used to translate the documents from their original languages to English.\\n4. To perform the translation, provide the text in the source language as input to the model along with the task of translating the text to English.\\n<<>>: from transformers import pipeline\\nt5 = pipeline('text2text-generation', model='google/t5-v1_1-base')\\nsource_text = \\\"Text in French, Spanish, or Italian.\\\"\\ntranslation_task = \\\"translation_en_to_fr\\\" # Replace this with the appropriate task for the desired language (English to French, etc.)\\ntranslated_text = t5(translation_task, source_text)\\n\"", "answer_id": "jDsy49rQVmiTVHnKh5nCVi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 502, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: M2M100ForConditionalGeneration.from_pretrained('facebook/m2m100_1.2B')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face, including M2M100ForConditionalGeneration and M2M100Tokenizer.\\n2. Load the pre-trained M2M100 model and tokenizer using the from_pretrained method. The model is capable of translating text from one language to another without requiring specific training for each individual language.\\n3. Set the source language of the text in the tokenizer.\\n4. Tokenize the input text and generate the translated text using the model.\\n5. Decode the translated text using the tokenizer and skip the special tokens.\\n6. The result is the translated version of the input text in the target language.\\n<<>>: from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer\\nsrc_text = \\\"This is an example sentence to be translated.\\\"\\nmodel = M2M100ForConditionalGeneration.from_pretrained('facebook/m2m100_1.2B')\\ntokenizer = M2M100Tokenizer.from_pretrained('facebook/m2m100_1.2B')\\ntokenizer.src_lang = \\\"en\\\" # Replace with the source language code\\nencoded_input = tokenizer(src_text, return_tensors=\\\"pt\\\")\\noutput = model.generate(**encoded_input)\\ntranslated_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]\"", "answer_id": "UffqUXwjrW7EWdK5ohMQB3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 503, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, specifically 'AutoTokenizer' and 'AutoModelForSeq2SeqLM' from transformers.\\n2. Load the pre-trained model 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3' using the 'from_pretrained' method of the AutoModelForSeq2SeqLM class. This model is designed for text-to-text generation tasks.\\n3. Use the tokenizer to tokenize the input text (book summary) and create the input_ids and attention_mask tensors.\\n4. Use the model to generate a positive review of the book, decoding the output_ids into a readable format.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ninput_text = \\\"Book summary here...\\\"\\ninput_ids = tokenizer.encode(input_text, return_tensors='pt')\\nattention_mask = input_ids != tokenizer.pad_token_id\\noutput_ids = model.generate(input_ids, attention_mask=attention_mask)\\nprint(tokenizer.decode(output_ids[0], skip_special_tokens=True))\"", "answer_id": "h9sNyebwrynRRipYCqkFyb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 504, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_it', model='Helsinki-NLP/opus-mt-en-it')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a translation model that can convert English text to Italian.\\n3. Specify the model 'Helsinki-NLP/opus-mt-en-it' to be loaded. This model has been trained on a large corpus of text to perform English to Italian translation.\\n4. Use the created translator to translate your website content from English to Italian.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_it', model='Helsinki-NLP/opus-mt-en-it')\\ntranslated_website_content = translator(website_content_in_english)\\n\"", "answer_id": "LKZZTXHquNLnxXMDEcKvwq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 505, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: T5ForConditionalGeneration.from_pretrained('plguillou/t5-base-fr-sum-cnndm')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, which includes T5ForConditionalGeneration for the text summarization model and T5Tokenizer for tokenizing the input text.\\n2. Load the pre-trained model 'plguillou/t5-base-fr-sum-cnndm' using the from_pretrained method of the T5ForConditionalGeneration class. This model is specifically trained for text summarization tasks in French.\\n3. Tokenize the input text (in French) using the T5Tokenizer class and convert it into input_ids.\\n4. Use the model's generate method to create a summary for the input text.\\n5. Decode the generated tokens back into human-readable text using the tokenizer's decode method.\\n<<>>: from transformers import T5Tokenizer, T5ForConditionalGeneration\\ntokenizer = T5Tokenizer.from_pretrained('plguillou/t5-base-fr-sum-cnndm')\\nmodel = T5ForConditionalGeneration.from_pretrained('plguillou/t5-base-fr-sum-cnndm')\\ninput_text = \\\"summarize: \\\" + french_news_article\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\noutputs = model.generate(input_ids)\\nsummary = tokenizer.decode(outputs[0])\\n\"", "answer_id": "eg8uTmHwssXeBFVTrWAhSc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 506, "text": "<<>>: Natural Language Processing Translation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-fr-es')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library: AutoTokenizer and AutoModelForSeq2SeqLM.\\n2. Load the pre-trained French to Spanish translation model ('Helsinki-NLP/opus-mt-fr-es') using the from_pretrained method of the AutoModelForSeq2SeqLM class.\\n3. Tokenize the French input text using the AutoTokenizer.from_pretrained method with the same model name.\\n4. Then, use the tokenizer to convert the French text into model-specific tensors that can be fed into the model.\\n5. Finally, run the model to translate the French text into Spanish, and then convert the model output back into human-readable text using the tokenizer.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-fr-es')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-fr-es')\\nfrench_text = \\\"Bonjour, comment \\u00e7a va?\\\"\\ninputs = tokenizer.encode(french_text, return_tensors=\\\"pt\\\")\\noutputs = model.generate(inputs)\\nspanish_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "amfXsWb4wEwCr8nVGdrXSi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 507, "text": "1. We first import the necessary classes from the transformers package, including PEGASUSTokenizer and PEGASUSForConditionalGeneration.\\n2. We create an instance of the tokenizer and the model.\\n3. To use PEGASUS for summarization, we first need to tokenize the text input and convert it into a format that the model can understand.\\n4. We then pass the tokenized input to the model and generate the summary.\\n5. Finally, we decode the output tokens to obtain the summarized text.\\nHere is an example code snippet that summarizes a given article using the PEGASUS model: \\n```python\\nfrom transformers import PEGASUSTokenizer, PEGASUSForConditionalGeneration\\ntokenizer = PEGASUSTokenizer.from_pretrained('google/pegasus-cased')\\nmodel = PEGASUSForConditionalGeneration.from_pretrained('google/pegasus-cased')\\ninputs = tokenizer(article_text, return_tensors='pt')\\nsummary_ids = model.generate(inputs['input_ids'])\\nsummary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\\n```\"", "answer_id": "gTNAwPVZPe4JGUpBiPtDY2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 508, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-newsroom')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a summarization model, which is capable of generating accurate and concise summaries of text.\\n3. Load the 'google/pegasus-newsroom' model, which has been trained on large volumes of news articles and can summarize them efficiently.\\n4. Pass the long news article text to the summarization pipeline, and it will generate a short summary of the article.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-newsroom')\\nsummary = summarizer(long_news_article, min_length=50, max_length=100)[0]['summary_text']\\n\"", "answer_id": "5iRWURd6fMyvC3bKzDhfUr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 509, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='Zixtrauce/BaekBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary library, which is 'pipeline' from the transformers package.\\n2. Create a conversational pipeline using the 'conversational' task and the 'Zixtrauce/BaekBot' model.\\n3. The model is based on the GPT-2 architecture and is designed for generating human-like responses in a chat-like environment.\\n4. You can now use this pipeline to generate responses for questions related to your products.\\n<<>>: from transformers import pipeline\\nconversational_pipeline = pipeline('conversational', model='Zixtrauce/BaekBot')\\nresponse = conversational_pipeline([{'role': 'user', 'content': 'What is your product called?'}])\\n\"", "answer_id": "RtMhsuJBCCf2GKx6BEvsga", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 510, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-based', 'PygmalionAI/pygmalion-1.3b')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-based conversational model using the \\\"PygmalionAI/pygmalion-1.3b\\\" model.\\n3. This conversational model is based on the Joshua character from the book \\\"Ready Player One\\\" by Ernest Cline, and it can be used for generating chatbot-like responses.\\n4. To control the main character's behavior, provide an input message that the model will process and generate a response based on the input.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('text-based', 'PygmalionAI/pygmalion-1.3b')\\ninput_message = \\\"What is your favorite color?\\\"\\nresponse = chatbot(input_message)[\\\"generated_text\\\"]\\n\"", "answer_id": "bE4ynj9kdB78kvDsQnXHW8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 511, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('microsoft/DialoGPT-medium')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'AutoModelForCausalLM' and 'AutoTokenizer' from transformers.\\n2. Load the pre-trained DialoGPT model and tokenizer from Hugging Face, which are 'microsoft/DialoGPT-medium'.\\n3. The model is used for generating multi-turn dialogues, which is suitable for an AI assistant in the banking context.\\n4. When the customer talks to the AI assistant, the AI can respond with appropriate answers, handling various tasks as requested by the customer.\\n5. The AI assistant can maintain context within the conversation and provide a meaningful dialogue experience for the client.\\n<<>>: from transformers import AutoModelForCausalLM, AutoTokenizer\\nimport torch\\ntokenizer = AutoTokenizer.from_pretrained('microsoft/DialoGPT-medium')\\nmodel = AutoModelForCausalLM.from_pretrained('microsoft/DialoGPT-medium')\\n# Conversation example\\ncustomer_input = \\\"I want to open a savings account.\\\"\\nbot_input_ids = tokenizer.encode(customer_input + tokenizer.eos_token, return_tensors='pt')\\nbot_response = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)\\nresponse_text = tokenizer.decode(bot_response[:, bot_response.softmax(dim=-1)[:, bot_input_ids.numpy()].numpy()], skip_special_tokens=True)\\n\"", "answer_id": "Cgo5vsXS2iixjpchtGeD2A", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 512, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelForCausalLM.from_pretrained('ruDialoGpt3-medium-finetuned-telegram')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required classes AutoTokenizer and AutoModelForCausalLM from the transformers library.\\n2. Load the tokenizer and model for 'ruDialoGpt3-medium-finetuned-telegram', which is a pre-trained model for generating conversational responses in Russian.\\n3. Use the tokenizer to encode an input query from a customer in Russian, and then generate a response using the model.\\n4. Decode the generated token IDs back to text using the tokenizer to get the model's response in Russian.\\n<<>>: from transformers import AutoTokenizer, AutoModelForCausalLM\\ncheckpoint = 'Kirili4ik/ruDialoGpt3-medium-finetuned-telegram'\\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\\nmodel = AutoModelForCausalLM.from_pretrained(checkpoint)\\nmodel.eval()\\ninput_text = '\\u043f\\u0440\\u0438\\u0432\\u0435\\u0442, \\u043a\\u0430\\u043a \\u0434\\u0435\\u043b\\u0430?' # Russian-language customer query\\ninput_ids = tokenizer.encode(input_text, return_tensors='pt')\\noutput = model.generate(input_ids, max_length=100, num_return_sequences=1)\\noutput_text = tokenizer.decode(output[0], skip_special_tokens=True)\\n\"", "answer_id": "GbyUBoNr4g7xMCn6a6hsyJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 513, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('facebook/opt-13b')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We will import the necessary classes from the transformers library provided by Hugging Face. This includes AutoModelForCausalLM and AutoTokenizer.\\n2. We will use the from_pretrained method of the AutoTokenizer and AutoModelForCausalLM classes to load the pre-trained model 'facebook/opt-13b'. This model has been trained for text generation tasks, which is exactly what we need for generating a paragraph of tips on how to take care of houseplants.\\n3. We will use the tokenizer and the model to generate the desired text. \\n<<>>: from transformers import AutoModelForCausalLM, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained('facebook/opt-13b')\\nmodel = AutoModelForCausalLM.from_pretrained('facebook/opt-13b')\\nprompt = \\\"Tips on how to take care of houseplants: \\\"\\ninput_ids = tokenizer.encode(prompt, return_tensors='pt')\\noutput_ids = model.generate(input_ids)\\nparagraph = tokenizer.decode(output_ids[0], skip_special_tokens=True)\\n\"", "answer_id": "mzupW2L7ZR9n3ZGhWTQ4H7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 514, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='Salesforce/codegen-350M-multi')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Salesforce/codegen-350M-multi'.\\n3. The loaded model is a generative model that can be used to generate code based on given input.\\n4. Provide an appropriate input description for the model, such as \\\"Generate a Python function that prints 'Hello, World!'\\\" and pass it to the model.\\n5. The model will then generate the desired Python function as output.\\n<<>>: from transformers import pipeline\\ngenerator = pipeline('text-generation', model='Salesforce/codegen-350M-multi')\\ninput_description = \\\"Generate a Python function that prints 'Hello, World!'\\\"\\ngenerated_code = generator(input_description)\\nprint(generated_code[0]['generated_text'])\\n\"", "answer_id": "TJNWMSZqF8iFNaU8ME8CWJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 515, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='sshleifer/tiny-gpt2')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'sshleifer/tiny-gpt2'.\\n3. The loaded model will be used for text generation, which is a task where the model creates a text output based on the input provided.\\n4. You can provide a text prompt related to sports as input to the model, and it will generate a motivational quote that is relevant to sports.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='sshleifer/tiny-gpt2')\\nmotivational_quote = text_generator('Sports do not build character, they reveal it.', max_length=50, num_return_sequences=1)\\n\"", "answer_id": "aQMP5DAJeJswbfwxA4Txzj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 516, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='bigscience/test-bloomd-6b3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the 'pipeline' function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text generation model. This model is capable of generating creative text based on a given prompt.\\n3. Specify the model 'bigscience/test-bloomd-6b3' to be loaded. This is a powerful text generation model that can generate high-quality, diverse, and creative text based on a given starting phrase.\\n4. To generate a story, simply provide the starting phrase to the model and it will generate a continuation based on the given context.\\n<<>>: from transformers import pipeline\\nstory_generator = pipeline('text-generation', model='bigscience/test-bloomd-6b3')\\ngenerated_story = story_generator('Once upon a time in a small village')\\n\"", "answer_id": "L8mrHH3hxD3akkCVnGoSvQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 517, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='gpt2')\\n<<>>: Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text generation model.\\n3. Specify the model 'gpt2' to be loaded. This model is trained on a large corpus of text and can generate human-like text based on a given prompt.\\n4. The created generator can be used to generate creative sentences for marketing content. Simply provide a prompt related to your marketing message, and the model will generate a sentence or paragraph that aligns with the given context.\\n<<>>: from transformers import pipeline\\ngenerator = pipeline('text-generation', model='gpt2')\\ngenerated_sentence = generator(\\\"Create engaging content for our PR team to use in their marketing materials.\\\")\\n\"", "answer_id": "Ltn3HpSyQdv7WF99eUAtVW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 518, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'AutoTokenizer' and 'AutoModelForSeq2SeqLM' from transformers, and 'torch' for processing data.\\n2. Create a tokenizer and model using the pretrained 'Qiliang/bart-large-cnn-samsum-ChatGPT_v3' model. This model has been trained for text summarization tasks.\\n3. Process the code snippet and convert it into a tokenized input.\\n4. Use the model to generate a summary of the provided code snippet.\\n5. Decode the generated tokens back into human-readable text for presenting the summary to the user.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nimport torch\\ntokenizer = AutoTokenizer.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Qiliang/bart-large-cnn-samsum-ChatGPT_v3')\\ncode_snippet = \\\"your_code_snippet_here\\\"\\ninput_ids = tokenizer(code_snippet, return_tensors='pt').input_ids\\ngenerated_tokens = model.generate(input_ids, max_length=100)\\nsummary = tokenizer.decode(generated_tokens[0], skip_special_tokens=True)\\n\"", "answer_id": "emWh3px7KoePjDbqXa77St", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 519, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='bert-base-cased')\\n<<>>: Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create a fill-mask model, which is capable of filling in the blanks in sentences.\\n3. We specify the model 'bert-base-cased' to be loaded. This is a pre-trained transformer model that can predict missing words in a sentence based on the surrounding context.\\n4. The created unmasker can be used to fill in the blanks for example sentences, quizzes, or trivia questions.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='bert-base-cased')\\nfilled_sentence = unmasker(\\\"The capital of France is [MASK].\\\")\\n\"", "answer_id": "28K9BKcXP7UMM74tM2KUmw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 520, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='GroNLP/bert-base-dutch-cased')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Create an instance of the fill-mask pipeline using the 'GroNLP/bert-base-dutch-cased' model, which is a pre-trained BERT model specifically designed for the Dutch language.\\n3. Provide the instance with a Dutch sentence containing a masked token, represented by the token.\\n4. The model will then predict the most suitable word to fill the gap in the sentence, providing a list of possible completions ranked by relevance.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='GroNLP/bert-base-dutch-cased')\\nsentence = \\\"Wij lezen de [MASK] in het park.\\\"\\ncompletions = fill_mask(sentence)\\n\"", "answer_id": "gJBomyo5EDADhYE2zK6NL7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 521, "text": "<<>>: Natural Language Processing Translation\\n<<>>: MarianMTModel.from_pretrained('pytorch-models/opus-mt-pt-en')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'MarianMTModel' and 'MarianTokenizer' from transformers.\\n2. Load the pre-trained translation model and tokenizer for Portuguese (pt) to English (en) using the model name 'pytorch-models/opus-mt-pt-en'.\\n3. Use the tokenizer to tokenize the Portuguese song lyrics.\\n4. Use the loaded model to translate the tokenized lyrics into English.\\n5. Decode the translated text back into human-readable English.\\n<<>>: from transformers import MarianMTModel, MarianTokenizer\\nmodel_name = 'pytorch-models/opus-mt-pt-en'\\ntokenizer = MarianTokenizer.from_pretrained(model_name)\\nmodel = MarianMTModel.from_pretrained(model_name)\\nlyrics = \\\"Voc\\u00e9 em uma sala, sozinha, com um viol\\u00e3o...\\\"\\ninput_ids = tokenizer.encode(lyrics, return_tensors=\\\"pt\\\")\\ntranslated_tokens = model.generate(input_ids)\\ntranslated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)\\n\"", "answer_id": "QhnpDSECBaz2Bs2EwigSt4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 522, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'SentenceTransformer' and 'util' from the sentence_transformers package.\\n2. Load the pre-trained model 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1' using the SentenceTransformer class.\\n3. Encode the main text of the breaking news article and the rest of the articles in your database using the model's 'encode' method.\\n4. Calculate the cosine similarity scores between the main text of the breaking news article and each other article in your database.\\n5. Rank the articles based on their similarity scores in descending order.\\n6. Display the top few articles with their similarity scores and corresponding excerpts.\\n<<>>: from sentence_transformers import SentenceTransformer, util\\nmodel = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')\\narticles = [breaking_news_article, ...] # List of articles from your database\\nmain_text = 'The breaking news article text...'\\nquery_matrix = util.query_matrix(model, articles, main_text)\\nquery_matrix = query_matrix.T\\n# Find the top similarity scores and print the results\\nsimilarity_scores, _ = util.cosine_similarity(query_matrix, query_matrix)\\nranked_articles = sorted(zip(articles, similarity_scores), key=lambda x: x[1], reverse=True)\\ntop_articles = [article for article, score in ranked_articles[:3]]\\nprint('Top 3 similar articles:')\\nfor article, score in top_articles:\\n print(f'{article:<20} {score:.4f}')\"", "answer_id": "UVqy8ErHTAXfcsaoJa5ssM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 523, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Create an instance of the SentenceTransformer class with the model 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1'.\\n3. Encode the question and the sentences using the model's 'encode' method to obtain embeddings of the input.\\n4. Calculate the similarity scores between the question and the sentence embeddings using the cosine similarity function.\\n5. Sort the sentences by their similarity scores in descending order, and the most relevant sentence is the one with the highest score.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nimport numpy as np\\nquestion = \\\"What is the main purpose of photosynthesis?\\\"\\nsentences = [\\\"Photosynthesis is the process used by plants to convert light energy into chemical energy to fuel their growth.\\\", \\\"The Eiffel Tower is a famous landmark in Paris.\\\", \\\"Photosynthesis also produces oxygen as a byproduct, which is necessary for life on Earth.\\\"]\\nmodel = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')\\nquestion_emb = model.encode(question)\\nsentence_emb = model.encode(sentences)\\nnum_sentences = 2\\ncos_scores = cosine_similarity(question_emb, sentence_emb)\\n[scores] = sorted(zip(sentences, cos_scores), key=lambda x: x[1], reverse=True)\\n[sorted_idx] = [np.argsort(scores[i]) for i in range(num_sentences)]\\nsorted_idx = [i for i in sorted_idx[:num_sentences]]\"", "answer_id": "UZbz5RQjogH6itfbwHZtid", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 524, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the SentenceTransformer class from the sentence-transformers library.\\n2. Create an instance of the SentenceTransformer class with the model 'sentence-transformers/all-MiniLM-L12-v2'.\\n3. Use this model to encode the customer reviews into embeddings.\\n4. Apply clustering techniques like K-means or DBSCAN on the embedded review vectors to group similar reviews together based on their content.\\n5. Analyze the clustered customer reviews to identify and address common customer issues.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.cluster import KMeans\\ncustomer_reviews = [\\\"Great product, I love it!\\\", \\\"I am very satisfied with this purchase\\\", \\\"Not what I expected\\\"]\\nmodel = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\nembeddings = model.encode(customer_reviews)\\nkmeans = KMeans(n_clusters=3, random_state=0).fit(embeddings)\\ncluster_assignments = kmeans.labels_\\n\"", "answer_id": "3tE6M2vMfLDbXuUQfguqxk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 525, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the sentence-transformers package using pip.\\n2. Import the SentenceTransformer class from the sentence-transformers library.\\n3. Load the pre-trained model 'sentence-transformers/all-mpnet-base-v2' using the SentenceTransformer class. This model is designed for mapping sentences and paragraphs to a 768-dimensional dense vector space.\\n4. Encode the input sentences into embeddings using the model's encode() method.\\n5. Calculate the cosine similarity between the embeddings to obtain sentence similarity scores.\\n6. The higher the similarity score, the more semantically similar the sentences are.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nsentences = ['This is an example sentence', 'Each sentence is converted']\\nmodel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\nembeddings = model.encode(sentences)\\nsimilarity_scores = cosine_similarity(embeddings)\\nprint(similarity_scores)\\n\"", "answer_id": "AUSABmTx2oDeWJ7VKFHjse", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 526, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceModel('shibing624/text2vec-base-chinese')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the SentenceModel class from the text2vec library provided by Hugging Face.\\n2. Create an instance of the SentenceModel with the 'shibing624/text2vec-base-chinese' pre-trained model, which maps Chinese sentences and paragraphs to a 4096-dimensional dense vector space.\\n3. Use the encode method of the model to convert your source sentence and any potential suggestions into dense vector representations.\\n4. Calculate the cosine similarity between the dense vector representations of your source sentence and the suggested sentences to find the most similar one.\\n5. Print the suggested sentence with the highest similarity score.\\n<<>>: from text2vec import SentenceModel\\nmodel = SentenceModel('shibing624/text2vec-base-chinese')\\nsource_sentence = 'your source sentence in Chinese'\\nsuggested_sentences = ['suggestion 1', 'suggestion 2', 'suggestion 3']\\nembeddings = model.encode([source_sentence] + suggested_sentences)\\nhighest_similarity_index = embeddings.argsort()[::-1][:2]\\nsuggested_sentences[highest_similarity_index[0]]\\n\"", "answer_id": "Ue263rGwEJoNyArzzVpZ9d", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 527, "text": "\\n<<>>: Audio Text-to-Speech\\n<<>>: ./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n<<>>: ESPnet\\n<<>>: 1. First, you need to install the ESPnet package and set up the environment with the required dependencies.\\n2. Next, navigate to the ESPnet repository and download the pre-trained 'amadeus' TTS model.\\n3. Use the provided shell script ('run.sh') to download and configure the model for text-to-speech conversion.\\n4. Once the model is set up, you can input the text in your app to convert it into speech and play the audio using the provided example code.\\n<<>>: # In your app code, replace 'APP_TEXT_HERE' with your actual text\\n./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n# In your app, replace 'Your_Audio_Play_Method_Here' with your actual audio play method\\nAPP_TEXT_HERE\\n# Your_Audio_Play_Method_Here\"", "answer_id": "ekFQS8dbrQpDFKhPnvaB2h", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 528, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2ForCTC.from_pretrained('jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the Wav2Vec2ForCTC class from the transformers package and the load_audio function from the datasets package.\\n2. Instantiate the Wav2Vec2ForCTC model using the pretrained 'jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli' model which is designed for generating transcriptions with punctuation.\\n3. Load the audio file using the load_audio function and convert it into the required format, such as Spectrogram. \\n4. Use the Wav2Vec2ForCTC model to generate the transcription with punctuation by passing the processed audio in the format of (samples, frames) to the model.\\n5. Convert the generated transcription into a readable format, such as a plain text.\\n<<>>: from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\\nfrom datasets import load_audio\\nimport torch\\nmodel = Wav2Vec2ForCTC.from_pretrained('jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli')\\nprocessor = Wav2Vec2Processor.from_pretrained('jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli')\\naudio_file = \\\"path/to/your/audio_file.wav\\\"\\nsamples, frames = load_audio(audio_file)\\ninput_values = processor(samples, frames=frames, return_tensors=\\\"pt\\\", padding=True).input_values\\nlogits = model(input_values).logits\\npredicted_ids = torch.argmax(logits, dim=-1)\\ntranscription = processor.decode(predicted_ids[0])\\n\"", "answer_id": "469YGgnKgFycWRaiPs5nyx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 529, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes WhisperProcessor and WhisperForConditionalGeneration.\\n2. We then use the from_pretrained method of the WhisperForConditionalGeneration class to load the pre-trained model 'openai/whisper-base'. This model has been trained for automatic speech recognition tasks.\\n3. We create a processor out of the WhisperProcessor class using the same model name.\\n4. We load the audio file and preprocess it using the processor.\\n5. Finally, we use the generate method of the model to transcribe the spoken English words in the audio file.\\n6. The output will be a transcription of the spoken words in the audio file.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-base')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-base')\\naudio_file = 'audio_file_path.wav'\\n# replace 'audio_file_path.wav' with path to your audio file\\nsample = load_audio(audio_file)\\ninput_features = processor(sample['array'], sampling_rate=sample['sampling_rate'], return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\"", "answer_id": "hq5Kv9fAfMkkdGwCHZEh6K", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 530, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: WaveformEnhancement.from_hparams('speechbrain/mtl-mimic-voicebank', 'pretrained_models/mtl-mimic-voicebank')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the speechbrain package. This includes the WaveformEnhancement class for speech enhancement.\\n2. We then use the from_hparams method of the WaveformEnhancement class to load the pre-trained model 'speechbrain/mtl-mimic-voicebank'. This model has been trained for speech enhancement tasks, which is exactly what we need for cleaning and enhancing audio.\\n3. After loading the model, we can use it to enhance the speech audio by removing background noise and other unwanted elements.\\n<<>>: import torch\\nfrom speechbrain.pretrained import WaveformEnhancement\\nenhance_model = WaveformEnhancement.from_hparams(\\n source='speechbrain/mtl-mimic-voicebank',\\n savedir='pretrained_models/mtl-mimic-voicebank',\\n)\\n# Load the audio file\\naudio_file = 'path/to/audio_file.wav'\\nenhanced_audio = enhance_model.enhance_file(audio_file)\\n\"", "answer_id": "UmukBq9S9L55iMMy3bfaxV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 531, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: hf_hub_download(repo_id='JorisCos/ConvTasNet_Libri2Mix_sepclean_8k')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries: hf_hub_download from huggingface_hub and torchaudio.\\n2. Download the ConvTasNet_Libri2Mix_sepclean_8k model from Hugging Face Hub using the hf_hub_download function with the provided repository ID.\\n3. Load the downloaded model into your Python environment using torchaudio.load().\\n4. Read the audio file you want to separate the background music and vocal from using torchaudio.load().\\n5. Use the model to separate the background music and vocal by passing your audio file into the model's separate_file() method.\\n6. Save the separated audio sources using torchaudio.save() function.\\n<<>>: from huggingface_hub import hf_hub_download\\nimport torchaudio\\nmodel_name = 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\\nmodel_directory = hf_hub_download(repo_id=model_name)\\nmodel = torchaudio.load(model_directory + '/model.pt')\\naudio_file = model_directory + '/sep_clean_8k.wav'\\nseparated = model.separate_file(audio_file)\\ntorchaudio.save(separated[:, :, 0], audio_file + '_mixed_sep')\\ntorchaudio.save(separated[:, :, 1], audio_file + '_vocals_sep')\\n\"", "answer_id": "iFaSQ9E2hH9yqNcT8n8nyy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 532, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio source separation model.\\n3. Specify the model 'Awais/Audio_Source_Separation' to be loaded. This model has been trained on the Libri2Mix dataset for separating voices from background noise.\\n4. The created pipeline can be used to process audio input, separating the voices from the background noise.\\n5. This will improve the audio quality of the retrieved voices in the educational software.\\n<<>>: from transformers import pipeline\\naudio_separator = pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\nseparated_audio = audio_separator(audio_input)\\n\"", "answer_id": "VsJjzVs52Vu53Bvqyu7CBy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 533, "text": "<<>>: Audio Audio Classification\\n<<>>: AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelForAudioClassification' from transformers.\\n2. Load the pre-trained model 'MIT/ast-finetuned-speech-commands-v2', which is designed to classify commands spoken by the user.\\n3. Create a tokenizer to process the audio input.\\n4. Convert the audio input (e.g., a speech command) into tokens using the tokenizer.\\n5. Feed the tokenized input into the model to obtain the classification result.\\n6. Based on the classification result, the security system can either disarm or activate the alarm accordingly.\\n<<>>: from transformers import AutoTokenizer, AutoModelForAudioClassification\\ntokenizer = AutoTokenizer.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\nmodel = AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n# Replace 'audio_command.wav' with the actual audio file\\naudio_input = \\\"audio_command.wav\\\"\\ninput_tokens = tokenizer.encode(audio_input, return_tensors=\\\"pt\\\")\\nresult = model(input_tokens)\\ncommand_id = result.logits.argmax(-1).item()\\ncommand = tokenizer.decode(result.class_id_labels[command_id])\\n\"", "answer_id": "8he6GttBsc6hGdjxhdqerX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 534, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/wav2vec2-base-superb-er')\\n<<>>: PyTorch Transformers\\n<<>>: 1. Import the necessary libraries, which are 'load_dataset' from datasets, and 'pipeline' from transformers.\\n2. Load the dataset containing your audio recording using 'load_dataset' function.\\n3. Use the 'pipeline' function from transformers to create an audio classification model by specifying the model 'superb/wav2vec2-base-superb-er'.\\n4. The created classifier can be used to classify the emotions in the audio recording based on the speaker's emotional state.\\n5. The model is trained on the IEMOCAP dataset and can predict 6 Eklavya emotions: anger, disgust, fear, happiness, sadness, and neutral.\\n<<>>: from datasets import load_dataset\\nfrom transformers import pipeline\\ndataset = load_dataset('anton-l/superb_demo', 'er', split='session1')\\nclassifier = pipeline('audio-classification', model='superb/wav2vec2-base-superb-er')\\nemotion = classifier(dataset[0]['file'], top_k=1)\\nprint(emotion)\\n\"", "answer_id": "8hg36VufQ9bLTu94E8vuHC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 535, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: S2THubInterface()\\n<<>>: Fairseq\\n<<>>:1. First, we need to install the required libraries, which are fairseq, torchaudio, and huggingface_hub.\\n2. Next, we import the necessary classes from fairseq and torchaudio, such as S2THubInterface and load_audio.\\n3. We load the pre-trained model 'facebook/xm_transformer_s2ut_hk-en' for speech-to-text translation in Hokkien to English using the load_model_ensemble_and_task_from_hf_hub function.\\n4. We create a generator using the task.build_generator method.\\n5. We load the audio file using the load_audio function and specify the sample rate as 16 kHz.\\n6. We get the model input using the get_model_input method and specify the text using the task.sample_phrase variable.\\n7. We use the get_prediction method to obtain the translated text in English.\\n8. We can play the translated text using the IPython display library.\\n<<>>: import os\\nfrom pathlib import Path\\nimport torch\\nfrom fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nfrom fairseq.models.text_to_speech import CodeHiFiGANVocoder\\nfrom fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\\nfrom huggingface_hub import snapshot_download\\nimport IPython.display as ipd\\ncache_dir = os.getenv('HUGGINGFACE_HUB_CACHE')\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub(\\n 'facebook/xm_transformer_s2ut_hk-en',\\n arg_overrides={'config_yaml': 'config.yaml', 'task': 'speech_to_text'},\\n cache_dir=cache_dir,\\n)\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\ngenerator = task.build_generator([model], cfg)\\npath = \\\"https://huggingface.co/facebook/xm_transformer_s2ut_hk-en/resolve/main/audio/ten_ Usage: \\\"\\nsample_phrase = \\\"\\nmodel_input = get_model_input(task, sample_phrase)\\ntranslation = get_prediction(task, model, generator, model_input)\\nipd.Audio(translation[0], rate=16000)\"", "answer_id": "WMVghvAhQAgkhDAV9DuXsg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 536, "text": "<<>>: Audio Audio Classification\\n<<>>: Wav2Vec2ForSpeechClassification.from_pretrained('harshit345/xlsr-wav2vec-speech-emotion-recognition')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'Wav2Vec2ForSpeechClassification' from transformers.\\n2. Use the 'from_pretrained' method of the Wav2Vec2ForSpeechClassification class to load the pre-trained model 'harshit345/xlsr-wav2vec-speech-emotion-recognition'. This model has been trained for speech emotion recognition tasks and can detect seven different emotions: anger, disgust, fear, happiness, sadness, and neutral.\\n3. Use the loaded model to classify emotions in the given audio file. The model will return the detected emotion, the score of the prediction, and the index of the emotion in the audio.\\n4. This emotion recognition model can help users manage their emotions better by detecting and classifying the emotions in their audio files.\\n<<>>: from transformers import Wav2Vec2ForSpeechClassification\\nmodel = Wav2Vec2ForSpeechClassification.from_pretrained('harshit345/xlsr-wav2vec-speech-emotion-recognition')\\n# Load your audio file here and use the model to classify the emotion\\n\"", "answer_id": "j3BNQfHrxUfEdqRzbpDSY7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 537, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/hubert-large-superb-sid')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio classification model.\\n3. Specify the model 'superb/hubert-large-superb-sid' to be loaded. This model is trained for Speech Interface Detection and can identify the speaker in a given audio input.\\n4. The created classifier can be used to process customer voice recordings and recognize the voice of the customer to better understand and address their concerns.\\n<<>>: from transformers import pipeline\\nvoice_classifier = pipeline('audio-classification', model='superb/hubert-large-superb-sid')\\ncustomer_voice_id = voice_classifier(customer_audio_file_path)\\n\"", "answer_id": "Nm2VtCY7JnKeBeUygtbiSf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 538, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package provided by Hugging Face.\\n2. Use the pipeline function to create an audio classification model, specifying the model 'superb/wav2vec2-base-superb-ks'.\\n3. This model is trained to categorize spoken commands into specific keywords, which can trigger various actions when executed by the voice-controlled device.\\n4. The created classifier can be used to process incoming audio data, classify the spoken commands, and trigger the appropriate actions based on the identified keywords.\\n<<>>: from transformers import pipeline\\nkeyword_classifier = pipeline('audio-classification', model='superb/wav2vec2-base-superb-ks')\\nspeech_command = keyword_classifier(audio_file_path)\\n# replace 'audio_file_path' with the path to your audio file\\n\"", "answer_id": "K4x9Lz8uvH8LigJNx7q7ox", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 539, "text": "<<>>: Audio Audio Classification\\n<<>>: Wav2Vec2Model.from_pretrained('facebook/wav2vec2-large-xlsr-53')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include torch, torchaudio, transformers, and Wav2Vec2Model.\\n2. Load the pre-trained model 'facebook/wav2vec2-large-xlsr-53', which is designed for emotion recognition in Russian. This model will help understand customer satisfaction by analyzing emotions from their voices.\\n3. Process the audio input, either from a file or a live stream, and obtain the required input features, including padding, normalization, and dynamic size adjustment.\\n4. Pass the input features into the model to obtain the logits.\\n5. Softmax the logits to generate probabilities for each emotion and return the top emotions along with their corresponding scores.\\n<<>>: import torch\\nimport torchaudio\\nfrom transformers import Wav2Vec2Model\\nmodel = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-large-xlsr-53')\\nmodel = model.eval()\\ndef predict_emotions(audio_path, top_k=5):\\n # Load and preprocess audio file\\n audio_input = torchaudio.load(audio_path)\\n input_features = model(audio_input.unsqueeze(0)).input_features\\n top_emotions = torch.topk(softmax(input_features, dim=-1), k=top_k)\\n return [\\\"%.2f\\\", top_emotions[i][\\\"score\\\"] for i in range(top_k)]\\nresult = predict_emotions(\\\"path_to_audio_file.wav\\\", top_k=5)\\n\"", "answer_id": "QeUds5tLMscNA76fHqK3SA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 540, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: pipeline('voice-activity-detection', model='funasr/FSMN-VAD')\\n<<>>: Hugging Face\\n<<>>: 1. First, import the necessary class from the pyannote.audio package. This includes the VoiceActivityDetection pipeline.\\n2. Instantiate the VoiceActivityDetection pipeline using the 'voice-activity-detection' task and the pre-trained model 'funasr/FSMN-VAD'.\\n3. Use the instantiated pipeline to detect voice activity in the audio recordings.\\n4. Based on the voice activity detection results, transcribe the relevant segments of the audio.\\n<<>>: from pyannote.audio.pipelines import VoiceActivityDetection\\npipeline = VoiceActivityDetection(task='voice-activity-detection', model='funasr/FSMN-VAD')\\nvoice_activity_detection = pipeline('audio.wav')\\n\"", "answer_id": "5Kw8A4NhNByF25kmHyuX8t", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 541, "text": "<<>>: Tabular Tabular Classification\\n<<>>: load_model(cached_download(hf_hub_url('danupurnomo/dummy-titanic', 'titanic_model.h5')))\\n<<>>: Scikit-learn\\n<<>>: 1. Import the necessary libraries, which are 'huggingface_hub', 'joblib', 'pandas', and 'sklearn.model_selection'.\\n2. Use the 'hf_hub_url' and 'cached_download' functions provided by the 'huggingface_hub' package to load the pre-trained model from Hugging Face Model Hub.\\n3. The loaded model is a binary classifier that predicts whether a passenger survived or not based on certain demographics, such as age and gender.\\n4. Load the model using 'joblib.load()' and the provided model name.\\n5. Read the passenger data file (e.g., 'data.csv') using 'pandas.read_csv()' and preprocess it to match the format required by the model (e.g., selecting the necessary features).\\n6. Use the model to make predictions for the passenger data.\\n<<>>: from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\nREPO_ID = 'danupurnomo/dummy-titanic'\\nFILENAME = 'titanic_model.h5'\\nmodel = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))\\n# Load passenger data\\ndata = pd.read_csv('data.csv')\\n# Preprocess data as required by the model\\npredictions = model.predict(data)\\n\"", "answer_id": "WaK2uJYr7br9n2z2P5qCQe", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 542, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import the necessary libraries, including json, joblib, and pandas.\\n2. Load the pre-trained model using joblib.load('model.joblib') method, which can predict carbon emissions based on input features.\\n3. Load the configuration file (config.json) containing the feature names to use when making predictions.\\n4. Read the input dataset (data.csv) with the desired features and preprocess it according to the model's requirements.\\n5. Use the loaded model to predict carbon emissions in the input dataset.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "eSQKRERaUp9VfF4mZcx3Lg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 543, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, including joblib and pandas.\\n2. Load the pre-trained model using joblib.load() method. The model is trained to predict carbon emissions based on the given features.\\n3. Load the configuration file to get the features required for the prediction.\\n4. Read the input data in CSV format using pandas' read_csv() function and filter the data based on the required features.\\n5. Rename the columns of the data as specified in the configuration file.\\n6. Use the model.predict() method to obtain the carbon emissions predictions for the given data.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "kHYXhYbr4dFvBQ9EDa89Hc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 544, "text": "<<>>: Reinforcement Learning Robotics\\n<<>>: Stable-Baselines3.load_from_hub('araffin/stable-baselines3-mujoco-v1', 'stable-baselines3-mujoco-v1.zip')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the Stable-Baselines3 library, including the MuJoCo environment and the RL algorithm.\\n2. We then load the pre-trained model 'araffin/stable-baselines3-mujoco-v1' using the load_from_hub function. This model has been trained on the MuJoCo environment, which is suitable for two-wheeled self-balancing robot control tasks.\\n3. We can apply any RL algorithm from the Stable-Baselines3 library to stabilize the two-wheeled self-balancing robot. Some examples include SAC, PPO, and DQN.\\n4. We would use the trained model and the RL algorithm to develop a control policy for the robot based on the reinforcement learning principles.\\n5. Finally, we implement and test the control policy in a physical environment, confirming the effectiveness of the applied RL algorithm in stabilizing the two-wheeled self-balancing robot.\\n<<>>: from stable_baselines3 import PPO, SAC, DQN\\nfrom stable_baselines3.common.env_util import make_vec_env\\nfrom rl_zoo3.mujoco_env import load_from_hub\\nmodel = load_from_hub('araffin/stable-baselines3-mujoco-v1', 'stable-baselines3-mujoco-v1.zip')\\n# Choose an RL algorithm from PPO, SAC, or DQN\\n# Implement and test the control policy in the physical environment\"", "answer_id": "QRffyAd8vZT8zTM66kXHfP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 545, "text": "<<>>: Natural Language Processing Feature Extraction\\n<<>>: AutoModel.from_pretrained('YituTech/conv-bert-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes the AutoModel class for loading pre-trained models.\\n2. Load the 'YituTech/conv-bert-base' model using the from_pretrained method of the AutoModel class. This model has been designed for feature extraction tasks, making it suitable for processing large text datasets.\\n3. With the loaded model, you can now extract features from your large text dataset by passing your data through the model.\\n4. The features extracted can then be used for further data analysis, such as clustering or dimension reduction.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('YituTech/conv-bert-base')\\n# Process your large text dataset to obtain features\\nfeatures = model(dataset_processed)\\n\"", "answer_id": "ix98tsVvRsafrvty8baf6r", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 546, "text": "<<>>: Natural Language Processing Feature Extraction\\n<<>>: AutoModel.from_pretrained('dmis-lab/biobert-v1.1')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, which includes AutoTokenizer for tokenizing text and AutoModel for feature extraction.\\n2. Load the BioBERT model using 'dmis-lab/biobert-v1.1' as the model name.\\n3. Tokenize the input entity names and create a tensor using the AutoTokenizer.\\n4. Extract features from the tokenized tensor using the AutoModel.\\n5. The resulting tensor contains the extracted features for each entity name.\\n<<>>: from transformers import AutoTokenizer, AutoModel\\ntokenizer = AutoTokenizer.from_pretrained('dmis-lab/biobert-v1.1')\\nmodel = AutoModel.from_pretrained('dmis-lab/biobert-v1.1')\\ninputs = tokenizer(entity_names, return_tensors='pt')\\noutputs = model(**inputs)\\nfeatures = outputs.last_hidden_state\\n\"", "answer_id": "Wjp5VemBCg5wHguJAgavm8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 547, "text": "<<>>: Audio Automatic Beatification\\n<<>>: HubertForAudioClassification.from_pretrained('JorisCos/Hubert-large-ll60k')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary libraries from the transformers package. This includes HubertForAudioClassification for the beatification model and load_dataset for loading the dataset.\\n2. Use the from_pretrained method of the HubertForAudioClassification class to load the pre-trained model 'JorisCos/Hubert-large-ll60k'. This model has been trained for automatic beatification tasks, which is exactly what we need for detecting beats in a sample.\\n3. Load the dataset using the load_dataset function, which provides a convenient way to work with audio datasets.\\n4. Process a sample from the dataset and use the model to detect the beats within the audio clip.\\n<<>>: from transformers import HubertForAudioClassification\\nfrom datasets import load_dataset\\n# Load the Hubert model\\nmodel = HubertForAudioClassification.from_pretrained('JorisCos/Hubert-large-ll60k')\\n# Load the dataset\\nds = load_dataset('patrickfitzgerald/hubert-large-dataset')\\n# Process a sample from the dataset\\nsample = ds[0]['file']\\n# Detect beats in the audio clip\\noutputs = model(sample['array'])\"", "answer_id": "XhtPyKJKRajyRHNzBg3Cpu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 548, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('darkstorm2150/Protogen_v2.2_Official_Release')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary class from the diffusers package. This includes StableDiffusionPipeline for generating high-quality images from text descriptions.\\n2. Load the pre-trained model 'darkstorm2150/Protogen_v2.2_Official_Release' using the from_pretrained method of the StableDiffusionPipeline class. This model has been trained for text-to-image generation tasks.\\n3. Provide a text prompt describing the desired image, in this case, \\\"a serene lake at sunset\\\".\\n4. Use the loaded model to generate an image based on the provided prompt.\\n5. Save the generated image to a file.\\n<<>>: from diffusers import StableDiffusionPipeline\\nmodel_id = 'darkstorm2150/Protogen_v2.2_Official_Release'\\npipe = StableDiffusionPipeline.from_pretrained(model_id)\\nprompt = \\\"a serene lake at sunset\\\"\\nimage = pipe(prompt).images[0]\\nimage.save('serene_lake_at_sunset.jpg')\\n\"", "answer_id": "3DDim92yCB3CPbTRuefBSY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 549, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: pipeline('text-to-image', model='22h/vintedois-diffusion-v0-1')\\n<<>>: Hugging Face\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a text-to-image pipeline using the provided model '22h/vintedois-diffusion-v0-1', which is designed for generating images from text prompts.\\n3. We then feed the text prompt \\\"kangaroo eating pizza\\\" into the pipeline to generate an image that can be used as the store sign.\\n4. The generated image can be saved to a file or used directly in the sign design.\\n<<>>: from transformers import pipeline\\ntext_to_image = pipeline('text-to-image', model='22h/vintedois-diffusion-v0-1')\\ngenerated_image = text_to_image(\\\"kangaroo eating pizza\\\")[0][\\\"image\\\"]\"", "answer_id": "PLCMQw3hfCGVDtx2ZjBPMV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 550, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('darkstorm2150/Protogen_v2.2_Official_Release')\\n<<>>: Hugging Face\\n<<>>:1. First, import the required libraries such as StableDiffusionPipeline from the diffusers package and Image from the PIL package.\\n2. Load the pre-trained text-to-image generation model 'darkstorm2150/Protogen_v2.2_Official_Release' using the from_pretrained method of the StableDiffusionPipeline class.\\n3. Provide a prompt describing the desired image of people wearing the summer clothing with a beach background.\\n4. Generate the image using the model, and save the generated image to a file, such as 'summer_clothing_beach_promotion.png'.\\n<<>>: from diffusers import StableDiffusionPipeline\\nfrom PIL import Image\\nmodel_id = 'darkstorm2150/Protogen_v2.2_Official_Release'\\npipe = StableDiffusionPipeline.from_pretrained(model_id)\\nprompt = 'A promotional poster for a new line of summer clothing featuring people wearing the clothes with a beach background'\\nimage = pipe(prompt).images[0]\\nimage.save('summer_clothing_beach_promotion.png')\\n\"", "answer_id": "DX7E9EF8kSHS5c5bZuojPU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 551, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('ocr', model='kha-white/manga-ocr-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a Japanese optical character recognition (OCR) model.\\n3. Specify the model 'kha-white/manga-ocr-base' to be loaded. This model is specifically trained for Japanese text extraction from manga pages.\\n4. The created OCR model can be used to process manga images and extract the text within them.\\n<<>>: from transformers import pipeline\\nocr_pipeline = pipeline('ocr', model='kha-white/manga-ocr-base')\\nmanga_page_image_path = 'path/to/your/manga/page/image.jpg'\\ntext_output = ocr_pipeline(manga_page_image_path)\\n\"", "answer_id": "MfHaBVyxqfp3Vmfs4Dk57j", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 552, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: AutoModel.from_pretrained('naver-clova-ix/donut-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes and functions from the transformers library.\\n2. Use the 'from_pretrained' method to load the pre-trained 'naver-clova-ix/donut-base' model. This model is designed for visual question answering tasks, which can help you better understand the artwork based on your questions.\\n3. Prepare the image of the artwork and the question you want to ask.\\n4. Use the loaded model to answer the question based on the given image.\\n5. Process the answer to create better exhibition materials.\\n<<>>: from transformers import AutoModel, AutoTokenizer\\nmodel = AutoModel.from_pretrained('naver-clova-ix/donut-base')\\ntokenizer = AutoTokenizer.from_pretrained('naver-clova-ix/donut-base')\\n# Prepare image and question\\nimage_path = 'path_to_artwork.jpg'\\nquestion = 'What is the main subject of the artwork?'\\n# Tokenize and answer the question\\ninputs = tokenizer(question, images=image_path, return_tensors='pt')\\noutputs = model(**inputs)\\nanswer = tokenizer.decode(outputs.logits.argmax(-1)[0])\\n# Process the answer to create exhibition materials\\n# ...\\n\"", "answer_id": "RArRcSpBGWofmmRBDsuGF7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 553, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a visual question answering model using the pipeline function and specify the model 'microsoft/git-base-vqav2'. This model can answer questions about images.\\n3. Use the created model to answer questions about the given image. The model will analyze the image and provide an answer based on the question asked.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\nimage_path = 'path/to/image.jpg'\\n# Replace 'path/to/image.jpg' with the path to the image file\\nquestion = 'How many people are in this photo?'\\nanswer = vqa(image=image_path, question=question)\\n\"", "answer_id": "7khUPGRbpXeHbzFz4yXeFQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 554, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='deepset/minilm-uncased-squad2')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, including the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a question-answering pipeline using the 'deepset/minilm-uncased-squad2' model, which is specifically designed for extractive question answering tasks.\\n3. In the event of a door or object being tampered with in the home security system, the application can ask the model a question like \\\"Who entered the room?\\\" and use the returned answer to analyze the CCTV recordings.\\n<<>>: from transformers import pipeline\\nnlp = pipeline('question-answering', model='deepset/minilm-uncased-squad2')\\n# Use the nlp to get the answer to the question 'Who entered the room?' when door sensor is triggered\\ndoor_sensor_activated = True\\nanswer = nlp('Who entered the room?')\\n\"", "answer_id": "C2wsnaYgXRSHaKcoF6zNsD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 555, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='azwierzc/vilt-b32-finetuned-vqa-pl')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Create a visual question answering model by calling the pipeline function and specifying the 'visual-question-answering' task along with the pre-trained model 'azwierzc/vilt-b32-finetuned-vqa-pl'.\\n3. The created model can now be used to answer questions about images. Simply provide the image path and a question related to the image, and the model will return an answer based on the visual information in the image.\\n<<>>: from transformers import pipeline\\nvqa = pipeline('visual-question-answering', model='azwierzc/vilt-b32-finetuned-vqa-pl')\\nimage_path = 'path/to/image.jpg'\\nquestion = 'What is in the image?'\\nanswer = vqa(image_path, question)\\n\"", "answer_id": "PbSwNaHa63x3nvbTKRArpa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 556, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='frizwankhan/entity-linking-model-final')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a question-answering pipeline using the 'frizwankhan/entity-linking-model-final' model. This model has been fine-tuned on the DocVQA dataset for document question answering tasks.\\n3. With the created pipeline, we can pass an image URL or the local path of the invoice and a specific question to extract information from the document.\\n4. The model will analyze the image or file to identify and extract textual information, and return the requested information as output.\\n<<>>: from transformers import pipeline\\nimport PIL.Image\\nqa_pipeline = pipeline('question-answering', model='frizwankhan/entity-linking-model-final')\\nquestion = 'What is the total amount due?'\\nimage_url = 'https://example.com/invoice_image.png'\\n# Replace with your invoice image URL\\ninvoice_image = PIL.Image.open(requests.get(image_url, stream=True).raw)\\nanswer = qa_pipeline(image=invoice_image, question=question)\\n\"", "answer_id": "7jiu733jUAB2fBy9ZwRPnc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 557, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: LayoutLMv2ForQuestionAnswering.from_pretrained('dperales/layoutlmv2-base-uncased_finetuned_docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including the LayoutLMv2ForQuestionAnswering model from transformers.\\n2. Load the pre-trained 'dperales/layoutlmv2-base-uncased_finetuned_docvqa' model using the LayoutLMv2ForQuestionAnswering class.\\n3. This model can then be used to analyze the text and layout information of a scanned document to provide answers to questions related to the content.\\n4. Pass the image containing the document and the question to the model to obtain the answer.\\n<<>>: from transformers import LayoutLMv2ForQuestionAnswering\\nmodel = LayoutLMv2ForQuestionAnswering.from_pretrained('dperales/layoutlmv2-base-uncased_finetuned_docvqa')\\n# Load your image and question\\nimage = 'path/to/image.png'\\nquestion = 'What is the total amount due?'\\n# Process the document\\nanswer = model(image, question)\\n\"", "answer_id": "J6TMgLpvWUJVRtNwHoxvzw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 558, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, including AutoModelForDocumentQuestionAnswering and AutoTokenizer.\\n2. Load the pre-trained model 'tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa' using the from_pretrained method of the AutoModelForDocumentQuestionAnswering class. This model is designed for extracting answers from documents based on given questions and contextual information.\\n3. Load the tokenizer associated with the model using the AutoTokenizer.from_pretrained() method.\\n4. Tokenize the input question and document content using the tokenizer.\\n5. Pass the tokenized inputs to the model to obtain the answer.\\n<<>>: from transformers import AutoModelForDocumentQuestionAnswering, AutoTokenizer\\nmodel = AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\ntokenizer = AutoTokenizer.from_pretrained('tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\nquestion = \\\"What is the main topic of the document?\\\"\\ncontext = \\\"The document is about renewable energy and its benefits.\\\"\\ninputs = tokenizer(question, context, return_tensors=\\\"pt\\\")\\noutputs = model(**inputs)\\nanswer = tokenizer.decode(outputs.start_logits.argmax(), outputs.end_logits.argmax() + 1)\\n\"", "answer_id": "DprcJ6EwAGfYYLPoACdD9k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 559, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries such as the pipeline function from the transformers package.\\n2. Create a question answering pipeline using the 'Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa' model, which is specifically designed for answering questions from document contents.\\n3. Use the created qa_pipeline to find the specific information from an invoice image, such as total amount due, invoice number, and due date.\\n4. To achieve this, you need to preprocess the image, convert it to the required format, and pass it to the pipeline along with your query.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa')\\n# Preprocess the image, convert it to RGB format, and ensure the text is legible\\n# ...\\n# Now pass the image and your query to the pipeline to find the desired information\\nresult = qa_pipeline(image='path/to/invoice_image.jpg', query='What is the total amount due?')\"", "answer_id": "d9hBb2vsJeZ3nQK9anhXc5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 560, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-sqa')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary class from the transformers package, which is TapasForQuestionAnswering in this case.\\n2. Use the from_pretrained() method to load the pre-trained model 'google/tapas-base-finetuned-sqa'. This model is specifically designed for question answering tasks involving tables.\\n3. The model can then be used to analyze the given financial report table and answer the question of total revenues for the last quarter.\\n<<>>: from transformers import TapasForQuestionAnswering\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-sqa')\\nresult = model(question=\\\"What were the total revenues for the last quarter?\\\", table=financial_report_table)\\n\"", "answer_id": "VMoRb7z8tkkrkHeHPigpWr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 561, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModelForDepthEstimation.from_pretrained('nielsr/dpt-large-redesign')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries, such as torch, transformers, and PIL.Image for working with the model and images.\\n2. Use the from_pretrained method of the AutoModelForDepthEstimation class to load the pre-trained model 'nielsr/dpt-large-redesign'. This model is specifically designed for estimating depth in images.\\n3. Load the input image of the construction site.\\n4. Process the image with the model to estimate the depth of objects and people present in the site.\\n5. Based on the estimated depth, analyze the distance between tools and workers to assess safety risks in the construction site.\\n<<>>: from transformers import AutoModelForDepthEstimation\\nfrom PIL import Image\\nimport torch\\nmodel = AutoModelForDepthEstimation.from_pretrained('nielsr/dpt-large-redesign')\\nimage = Image.open('construction_site_image.jpg')\\n# replace 'construction_site_image.jpg' with path to your image\\ninput_tensor = torch.tensor(image.resize((224, 224))).unsqueeze(0)\\noutput = model(input_tensor)\\ndepth_estimation = output.predict(input_tensor)\\n\"", "answer_id": "QgdzoyXkdXpVEgM5UzysHA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 562, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221122-014502')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, specifically the AutoModel class from the transformers library provided by Hugging Face.\\n2. Load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221122-014502' using the AutoModel.from_pretrained() method. This model is fine-tuned for depth estimation tasks.\\n3. Given an input image, the model can be used to estimate the depth of objects within the image. This can be useful for applications such as 3D reconstruction, object tracking, and scene understanding.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221122-014502')\\ninput_image = 'path/to/input/image'\\ndepth_estimate = model(input_image)\\n\"", "answer_id": "95CMq8zgjxMoqahfhVeegR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 563, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vits8')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package. This includes ViTFeatureExtractor for extracting features from the images and ViTModel for the actual image classification model.\\n2. Load the pre-trained model 'facebook/dino-vits8', which is trained on ImageNet using the DINO method that is particularly efficient for image classification tasks.\\n3. Create a feature extractor with the 'facebook/dino-vits8' model, which will be used to preprocess the input images.\\n4. Use the feature extractor to process the input images and convert them into tensors.\\n5. Pass the processed tensors into the pre-trained model to obtain the classification results.\\n6. Analyze the results to classify the images and protect the relevant species of animals.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimage = Image.open('nature_image_path.jpg')\\n# replace 'nature_image_path.jpg' with the path to your image\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits8')\\nmodel = ViTModel.from_pretrained('facebook/dino-vits8')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\\n# Perform image classification on the last_hidden_state and analyze the results.\"", "answer_id": "3qqtHaueSe8tAKG79ScXsk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 564, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes, including YolosForObjectDetection for the object detection model and Image for processing image data.\\n2. Use the from_pretrained method of the YolosForObjectDetection class to load the pre-trained model 'hustvl/yolos-tiny'. This model has been trained for object detection tasks, which is what we need to detect objects in an image.\\n3. Load the image data from a file or a user-provided image.\\n4. This model can then be used to analyze the image and output the detected objects and their locations.\\n<<>>: from transformers import YolosFeatureExtractor, YolosForObjectDetection\\nfrom PIL import Image\\nimage = Image.open('image_file_path.jpg')\\n# replace 'image_file_path.jpg' with path to your image\\nfeature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-tiny')\\nmodel = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "E438QAszjtne2BdRvKbyLE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 565, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8s-csgo-player-detection')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from ultralyticsplus package, which includes YOLO for object detection.\\n2. Use the YOLO class to load the pre-trained model 'keremberke/yolov8s-csgo-player-detection'. This model has been trained for object detection tasks, specifically for detecting Counter-Strike: Global Offensive players in images.\\n3. Set the model's parameters such as confidence threshold, IoU threshold, agnostic NMS, and maximum detections.\\n4. Use the model to predict the locations of players in the given image and display the results.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8s-csgo-player-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "ioV5BeAFpjjzgLLgCtUx8W", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 566, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-base-coco')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers and PIL packages. This includes MaskFormerForInstanceSegmentation for the image segmentation model and Image for processing image data.\\n2. We then use the from_pretrained method of the MaskFormerForInstanceSegmentation class to load the pre-trained model 'facebook/maskformer-swin-base-coco'. This model has been trained for image segmentation tasks, which is what we need to recognize objects and draw boundaries in an image.\\n3. We load the image data from a file or an URL.\\n4. This model can then be used to analyze an image and segment it into individual objects with their respective boundaries.\\n<<>>: from transformers import MaskFormerForInstanceSegmentation, AutoImageProcessor\\nfrom PIL import Image\\nimport requests\\nprocessor = AutoImageProcessor.from_pretrained('facebook/maskformer-swin-base-coco')\\nmodel = MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-base-coco')\\nurl = 'your_image_url'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "oGMTarKKxYy7Gv97NjGkri", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 567, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-hed')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and classes, including ControlNetModel and StableDiffusionControlNetPipeline, as well as Image and HEDdetector.\\n2. Load the HED detector using the HEDdetector.from_pretrained method.\\n3. Load the input image and apply the HED detector to create a control image.\\n4. Load the pretrained ControlNetModel using the from_pretrained method.\\n5. Create a pipeline object by combining the StableDiffusionControlNetPipeline and the loaded ControlNetModel.\\n6. Modify the pipeline's scheduler using the pipeline.scheduler = pipeline.scheduler.from_config method.\\n7. Enable memory-efficient attention and CPU offloading for the pipeline.\\n8. Generate an artistic variation of the input image using the pipeline with the specified number of inference steps and control image.\\n9. Save the resulting image.\\n<<>>: from PIL import Image\\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\\nimport torch\\nfrom controlnet_aux import HEDdetector\\nfrom diffusers.utils import load_image\\nhed = HEDdetector.from_pretrained('lllyasviel/ControlNet')\\nimage = load_image('path/to/image.png')\\nimage = hed(image)\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-hed', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\noutput_image = pipe(image, num_inference_steps=20).images[0]\\noutput_image.save('images/image_out.png')\\n\"", "answer_id": "BHAUi2yqRRiidFionaeCHz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 568, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include Swin2SRForConditionalGeneration from transformers.\\n2. Load the pre-trained model 'condef/Swin2SR-lightweight-x2-64', which is specifically designed for lightweight image super-resolution tasks and can upscale images by 2x without losing quality.\\n3. Read the low-resolution image from the user and preprocess it according to the model requirements.\\n4. Use the pre-trained model to upscale the input image, and save the resulting high-resolution image.\\n<<>>: from transformers import Swin2SRForConditionalGeneration\\nfrom PIL import Image\\nimport torchvision.transforms as T\\nswin2sr = Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\ninput_image = Image.open('path/to/low_res_image.jpg')\\n# Replace 'path/to/low_res_image.jpg' with the path to the low-resolution image\\noutput_image = swin2sr(input_image)\\noutput_image.save('path/to/high_res_image.jpg')\\n\"", "answer_id": "Si4S5m9H2628HUfAmYCwqn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 569, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15s2_scribble')\\n<<>>: Diffusers\\n<<>>: 1. Import the necessary libraries and classes, such as ControlNetModel and StableDiffusionControlNetPipeline, from the diffusers package.\\n2. Load the pre-trained ControlNet model 'lllyasviel/control_v11p_sd15s2_scribble'.\\n3. Create a pipeline using StableDiffusionControlNetPipeline.from_pretrained() that combines the ControlNet model with Stable Diffusion.\\n4. Generate the image based on the given description using the pipeline, specifying the number of inference steps and a random generator.\\n5. Save the generated image as a file.\\n<<>>: import torch\\nfrom diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nfrom diffusers.utils import load_image\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15s2_scribble', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\ngenerator = torch.manual_seed(0)\\nimage = pipe(\\\"A magical forest with unicorns and a rainbow\\\", num_inference_steps=20, generator=generator, image=control_image).images[0]\\nimage.save('images/image_out.png')\\nenjoy!\"", "answer_id": "GZozFkkEKU5C32vRswEWBn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 570, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'EleutherAI/gpt-neo-2.7B'.\\n3. The loaded model will be used for text generation, which is a task where the model generates text based on a given prompt.\\n4. Provide a relevant prompt for the model to generate a slogan for an e-commerce website that sells eco-friendly products.\\n5. The model will generate a slogan based on the provided prompt.\\n<<>>: from transformers import pipeline\\ngenerator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B')\\nprompt = \\\"Create a slogan for an e-commerce website that sells eco-friendly products:\\\"\\ngenerated_slogan = generator(prompt, do_sample=True, min_length=50)[0]['generated_text']\\n\"", "answer_id": "2Fb3V8geCEjMT3Rn5jx2cz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 571, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-ema-celebahq-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the diffusers package. This includes DDPMPipeline for the unconditional image generation model.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'google/ddpm-ema-celebahq-256'. This model has been trained for high-quality image synthesis tasks, which is exactly what we need for generating faces for video game characters.\\n3. The model can then be used to generate an image, which can serve as the basis for a video game character's face.\\n<<>>: from diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-ema-celebahq-256')\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_face.png')\\n\"", "answer_id": "dgFGr6iVdMW8ZKDpEG6KZa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 572, "text": "<<>>: Computer Vision Video Classification\\n<<>>: TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries and classes, including AutoImageProcessor and TimesformerForVideoClassification from the transformers package, numpy for array manipulation, and torch for running the model on the GPU.\\n2. Load the pre-trained model 'facebook/timesformer-base-finetuned-ssv2' using TimesformerForVideoClassification.from_pretrained method.\\n3. Prepare the video data by processing it into the required format using the AutoImageProcessor.\\n4. Use the model to analyze the video and output the classifier's predictions for the categories of interest.\\n5. This solution provides a quick and efficient way to classify video content without worrying about the accuracy of the results.\\n###Code:\\n```python\\nfrom transformers import AutoImageProcessor, TimesformerForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(8, 3, 224, 224))\\nprocessor = AutoImageProcessor.from_pretrained('facebook/timesformer-base-finetuned-ssv2')\\nmodel = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-ssv2')\\ninputs = processor(images=video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])```", "answer_id": "JsHAoPCnpTmDR84VEYKWrS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 573, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, which include the pipeline function from the transformers package provided by Hugging Face.\\n2. Use the pipeline function to create an image classification model.\\n3. Specify the model 'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup' to be loaded. This model is trained for zero-shot image classification tasks and can classify images into a variety of categories.\\n4. Provide a path to the image file and a list of vehicle class names (e.g. 'car', 'motorcycle', 'truck', 'bicycle') to the model for classification.\\n5. The model will return predictions for the given image classifying it into one of the provided vehicle classes.\\n<<>>: from transformers import pipeline\\nvehicle_classifier = pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\nvehicle_classification = vehicle_classifier('path/to/image.jpg', ['car', 'motorcycle', 'truck', 'bicycle'])\\n\"", "answer_id": "gJJExwYJVF6ab8UcxmynyW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 574, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vitb16')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries: ViTFeatureExtractor and ViTModel from transformers, Image from PIL, and requests.\\n2. Load the pre-trained Vision Transformer (ViT) model 'facebook/dino-vitb16' using the ViTModel.from_pretrained() function.\\n3. Create an instance of the ViTFeatureExtractor class and apply the necessary transformations to the input image to match the model's requirements.\\n4. Pass the transformed image through the feature extractor and obtain the feature output.\\n5. Finally, use the model to classify the transformed image by passing the extracted features as inputs.\\n6. The outputs from the model will provide the image's category labels, which can be used for further processing or display.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimport requests\\nurl = 'http://example.com/animal_image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vitb16')\\nmodel = ViTModel.from_pretrained('facebook/dino-vitb16')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\\n\"", "answer_id": "HyQ8wQbEnps9BBtGtrz4Qy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 575, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: StreetCLIP('StreetCLIP/streetclip-vit-base-patch16')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'StreetCLIP' from the transformers package.\\n2. Instantiate the StreetCLIP model using the provided model name 'StreetCLIP/streetclip-vit-base-patch16'.\\n3. Preprocess the input image and define a list of cities to classify.\\n4. Use the model to generate logits for each city image, and then apply softmax to obtain probabilities.\\n<<>>: from transformers import StreetCLIP\\nimport torch\\nmodel = StreetCLIP('StreetCLIP/streetclip-vit-base-patch16')\\nimage = 'path_to_image.jpg' # Replace with the path to the image of the potential location\\ncities = ['San Jose', 'San Diego', 'Los Angeles', 'Las Vegas', 'San Francisco']\\nlogits_per_image = model(image).logits_per_image\\nprobs = logits_per_image.softmax(dim=1)\\n\"", "answer_id": "PkEPT33gih8yjFcuXvEhpR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 576, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='siebert/sentiment-roberta-large-english')\\n<<>>: Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create a sentiment analysis model, which is capable of understanding and categorizing text sentiment.\\n3. We specify the model 'siebert/sentiment-roberta-large-english' to be loaded. This is a fine-tuned model that performs sentiment analysis on various types of text in English.\\n4. The created sentiment analysis classifier can be used to analyze the reviews or comments that you have received for the new product.\\n5. The classifier will assign a sentiment label (positive or negative) to each input text, allowing you to understand customer reactions.\\n<<>>: from transformers import pipeline\\nsentiment_analysis = pipeline('sentiment-analysis', model='siebert/sentiment-roberta-large-english')\\ncomments = [\\\"I love the new product!\\\", \\\"This is the worst thing I've ever bought.\\\"]\\nresult = sentiment_analysis(comments)\\nprint(result)\\n\"", "answer_id": "izjZEKLu4fboC5aMEcSrtD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 577, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='michellejieli/NSFW_text_classifier')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'michellejieli/NSFW_text_classifier'.\\n3. The loaded model will be used for text classification, specifically for detecting NSFW content.\\n4. Pass the chat room message to the classifier and it will return the classification results, indicating if the message contains any harmful content or not.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('text-classification', model='michellejieli/NSFW_text_classifier')\\nmessage = \\\"Chat room message here...\\\"\\nresult = classifier(message)\\n\"", "answer_id": "27nGrJJAMtmdYXRVpT3SE8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 578, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model.\\n3. Specify the model 'tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa' to be loaded. This model is designed for document question answering and can efficiently retrieve relevant information based on user queries.\\n4. The created question-answering model can be used to retrieve information from a collection of documents by providing the document content and a user query.\\n<<>>: from transformers import pipeline\\ndocument_qa = pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa')\\nquery = \\\"What are the benefits of renewable energy?\\\"\\ndocuments = [\\n \\\"Document 1 content here...\\\",\\n \\\"Document 2 content here...\\\",\\n \\\"Document 3 content here...\\\",\\n]\\nresult = document_qa(query=query, context=documents)\\n\"", "answer_id": "8XtaJfiLBsSXfNYNTxSbun", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 579, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/ner-english-large')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the Flair library, including Sentence and SequenceTagger.\\n2. We then use the load method of the SequenceTagger class to load the pre-trained NER model 'flair/ner-english-large'. This model is specifically designed to recognize named entities in text, such as people's names, locations, and other key entities.\\n3. We create a Sentence object around each news article text and use the predict method of the SequenceTagger to identify named entities within the text.\\n4. We then print the named entities by iterating over the detected spans in the 'ner' attribute of the Sentence object.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/ner-english-large')\\nsentences = [\\\"News article text goes here...\\\"]\\ntags = tagger.predict(sentences)\\nfor sentence in sentences:\\n named_entities = sentence.get_spans('ner')\\n print(named_entities)\"", "answer_id": "Js2vzPNZdpLxHCca3bmtAa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 580, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-job_all-903929564', use_auth_token=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes and functions from the transformers library.\\n2. Load the pre-trained entity extraction model 'ismail-lucifer011/autotrain-job_all-903929564' using the AutoModelForTokenClassification class.\\n3. Load the tokenizer for the model using AutoTokenizer.\\n4. Tokenize the provided sentence and pass the tokens to the model.\\n5. The model will then analyze the sentence and extract entities from it.\\n6. The extracted entities will consist of person names (celebrities and company CEOs), organization names, and locations.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('ismail-lucifer011/autotrain-job_all-903929564', use_auth_token=True)\\ntokenizer = AutoTokenizer.from_pretrained('ismail-lucifer011/autotrain-job_all-903929564', use_auth_token=True)\\ntext = \\\"CEO of Microsoft, Satya Nadella, announced Microsoft's acquisition of GitHub at a press conference in San Francisco.\\\"\\ninputs = tokenizer(text, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "3tjDKx4YZ8KvU3tKgM5Biw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 581, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Dizex/InstaFoodRoBERTa-NER')\\n<<>>: Transformers\\n<<>>:1. Import the necessary classes and functions from the transformers library.\\n2. Create an NER pipeline using the 'Dizex/InstaFoodRoBERTa-NER' model. This model is specifically trained for recognizing food-related named entities in text.\\n3. Use the created NER pipeline to process user input text for recognizing food keywords.\\n4. The NER pipeline will return a list of detected named entities, which can be further processed or displayed in the food application.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline\\nmodel = AutoModelForTokenClassification.from_pretrained('Dizex/InstaFoodRoBERTa-NER')\\ntokenizer = AutoTokenizer.from_pretrained('Dizex/InstaFoodRoBERTa-NER')\\nner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)\\ntext = \\\"I love eating @[] with my friends.\\\"\\nner_results = ner_pipeline(text)\\nprint(ner_results)\"", "answer_id": "VpdnamNr6GuJjDJPf2P3sH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 582, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: PunctuationModel()\\n<<>>: Transformers\\n<<>>: 1. We first import the PunctuationModel class from the deepmultilingualpunctuation package.\\n2. We then create an instance of the PunctuationModel class for English, since this model only supports English punctuation.\\n3. The instance of the PunctuationModel class can be used to predict the missing punctuation in a text input.\\n4. The text input can be obtained from the user in a chat app, and the model can predict and add the appropriate punctuation to the end of the message.\\n<<>>: from deepmultilingualpunctuation import PunctuationModel\\nmodel = PunctuationModel()\\nuser_message = \\\"Hello, how are you?\\\"\\npredictions = model.restore_punctuation(user_message)\\nprint(predictions)\\n\"", "answer_id": "ApEF5XBAbNGFEFNw6X4i5C", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 583, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('dslim/bert-large-NER')\\n<<>>: Transformers\\n<<>>: 1. We import the necessary Named Entity Recognition (NER) model and tokenizer from the transformers library provided by Hugging Face.\\n2. The model 'dslim/bert-large-NER' is loaded using AutoModelForTokenClassification.from_pretrained(). This is a pre-trained BERT model fine-tuned for NER tasks, which recognizes entities such as people's names and organizations in a given text.\\n3. We also load the corresponding tokenizer using AutoTokenizer.from_pretrained().\\n4. Then, we use the tokenizer to preprocess the input text and pass the preprocessed text to the model to extract the named entities.\\n5. Finally, we can print the extracted entities, which in this case are the names of companies and people mentioned in the provided text.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('dslim/bert-large-NER')\\ntokenizer = AutoTokenizer.from_pretrained('dslim/bert-large-NER')\\ninput_text = \\\"The history of technology companies includes names like Apple, Google, Microsoft, and IBM.\\\"\\ninputs = tokenizer(input_text, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "cVGmPa8YCPEoUduUpL9kk2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 584, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/ner-english-fast')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the Flair package. This includes Sentence for representing textual data and SequenceTagger for the named entity recognition (NER) model.\\n2. We then use the load method of the SequenceTagger class to load the pre-trained model 'flair/ner-english-fast'. This model has been trained for NER tasks, which is exactly what we need for identifying names and locations in a diary entry.\\n3. We create a Sentence object to represent the diary entry text.\\n4. The model is used to predict NER tags for the entry. This will identify names of people and locations mentioned in the diary.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/ner-english-fast')\\ndiary_entry = \\\"On September 1st, I went to the park with John and we saw Jane.\\\"\\nsentence = Sentence(diary_entry)\\ntagger.predict(sentence)\\nfor entity in sentence.get_spans('ner'):\\n print(entity)\\n\"", "answer_id": "PmVpczjKREELaAnJVKej9B", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 585, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Jean-Baptiste/camembert-ner')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, including AutoTokenizer and AutoModelForTokenClassification.\\n2. Load the pre-trained model 'Jean-Baptiste/camembert-ner' using the from_pretrained method of the AutoModelForTokenClassification class. This model is trained on multilingual ner data and can perform named entity recognition.\\n3. Load the tokenizer for the model using the from_pretrained method of the AutoTokenizer class.\\n4. Tokenize the multilingual text provided to your company using the tokenizer.\\n5. Use the model to detect named entities in the tokenized text, such as locations of meetings.\\n6. Parse the output to extract the relevant information.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification\\ntokenizer = AutoTokenizer.from_pretrained('Jean-Baptiste/camembert-ner')\\nmodel = AutoModelForTokenClassification.from_pretrained('Jean-Baptiste/camembert-ner')\\ntext = \\\"Multilingual meeting text provided to your company...\\\"\\n# Tokenize the text\\ninputs = tokenizer(text, return_tensors='pt')\\n# Perform Named Entity Recognition\\noutputs = model(**inputs)\\n# Parse the output for relevant information\\n\"", "answer_id": "4gigwEoexkoPpiJwJupaYj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 586, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('lysandre/tiny-tapas-random-wtq')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, specifically TapasTokenizer and TapasForQuestionAnswering.\\n2. Load the pre-trained TAPAS model 'lysandre/tiny-tapas-random-wtq' using the from_pretrained method of the TapasForQuestionAnswering class.\\n3. Load the corresponding tokenizer for this model using the TapasTokenizer.from_pretrained method.\\n4. Prepare the table data in a Pandas DataFrame or a list of dictionaries, where each row represents a year and each column represents a revenue source.\\n5. Tokenize the user's question and the table using the tokenizer's table and query parameters.\\n6. Pass the tokenized input to the model to get the predicted answer.\\n7. Extract the answer from the model's output and format it for human consumption.\\n<<>>: from transformers import TapasTokenizer, TapasForQuestionAnswering\\nimport pandas as pd\\nmodel = TapasForQuestionAnswering.from_pretrained('lysandre/tiny-tapas-random-wtq')\\ntokenizer = TapasTokenizer.from_pretrained('lysandre/tiny-tapas-random-wtq')\\ntable = [\\n {\\\"year\\\": 2020, \\\"revenue_source\\\": \\\"Cloud Platform\\\"},\\n {\\\"year\\\": 2021, \\\"revenue_source\\\": \\\"AI Services\\\"},\\n {\\\"year\\\": 2022, \\\"revenue_source\\\": \\\"Hardware\\\"}\\n]\\nquery = \\\"What was the revenue from AI Services in 2021?\\\"\\ninputs = tokenizer(table=table, queries=query, padding='max_length', return_tensors='pt')\\noutputs = model(**inputs)\\npredicted_answer_coordinates, _ = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach())\\nanswer_index = predicted_answer_coordinates[0].item()\\nanswer = tokenizer.convert_coordinates_to_answers(answer_index)\\nprint(answer)\"", "answer_id": "CFHJMxXpu5gu39fvfFQRut", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 587, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries including TapexTokenizer and BartForConditionalGeneration from the transformers library, and pandas for handling tables.\\n2. Instantiate the tokenizer and the model using the respective from_pretrained methods and the provided model name 'microsoft/tapex-base'.\\n3. Create a pandas DataFrame to represent your table data.\\n4. Encode the table and the query using the tokenizer.\\n5. Use the model to generate an output based on the query and table.\\n6. Decode the output to obtain the answer to the query based on the table data.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-base')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-base')\\ndata = {\\n 'year': [1896, 1900, 1904, 2004, 2008, 2012],\\n 'city': ['athens', 'paris', 'st. louis', 'athens', 'beijing', 'london']\\n}\\ntable = pd.DataFrame.from_dict(data)\\nquery = 'In which year did beijing host the Olympic Games?'\\nencoding = tokenizer(table=table, query=query, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nprint(tokenizer.batch_decode(outputs, skip_special_tokens=True))\"", "answer_id": "3qrDYY3kprK6exGmw8jyuw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 588, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which include TapasForQuestionAnswering and TapasTokenizer from the transformers package.\\n2. Load the 'google/tapas-base-finetuned-wtq' model using the TapasForQuestionAnswering.from_pretrained() method.\\n3. Load the tokenizer of the same model.\\n4. Create a function that takes a question and a table as input, and tokenizes the question and table using the TapasTokenizer.\\n5. Pass the tokenized input to the model to get the answer, and return the answer text.\\n<<>>: from transformers import TapasForQuestionAnswering, TapasTokenizer\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-base-finetuned-wtq')\\ntokenizer = TapasTokenizer.from_pretrained('google/tapas-base-finetuned-wtq')\\ndef answer_question(question, table):\\n inputs = tokenizer(table=table, queries=question, padding='max_length', return_tensors='pt')\\n outputs = model(**inputs)\\n answer_start = torch.argmax(outputs.start_logits)\\n answer_end = torch.argmax(outputs.end_logits)\\n answer = tokenizer.convert_ids_to_tokens(inputs[\\\"input_ids\\\"][0][answer_start:answer_end + 1])\\n return answer\\nquestion = \\\"What is the highest salary?\\\"\\ntable = [\\\"Name\\\", \\\"Salary\\\"]\\nprint(answer_question(question, table))\"", "answer_id": "9sZGeH2mSuGGTr9JgKwWx5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 589, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='bigwiz83/sapbert-from-pubmedbert-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create a question-answering model, which is capable of accurately answering questions based on a provided context.\\n3. We specify the model 'bigwiz83/sapbert-from-pubmedbert-squad2' to be loaded. This model is specifically fine-tuned for the SQuAD2.0 dataset, which is aimed at answering medical questions.\\n4. The created model can be used to answer medical questions based on a provided context.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='bigwiz83/sapbert-from-pubmedbert-squad2')\\nanswer = qa_pipeline({'context': context, 'question': question})\\n\"", "answer_id": "LsyrdzCgT8vWgR55RnfvUv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 590, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-base-finetuned-wikisql')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. To answer the question, we need to import the required libraries: 'BartForConditionalGeneration' for the model and 'TapexTokenizer' for tokenizing the table.\\n2. The 'microsoft/tapex-base-finetuned-wikisql' model is used for table-based question answering tasks. We load this model using 'BartForConditionalGeneration.from_pretrained'.\\n3. The students' data about European capitals can be represented as a table in the WikiSQL format, which is a human-readable format for the TAPEX model.\\n4. We tokenize the table using 'TapexTokenizer.from_pretrained' and provide the input to the model by specifying the question and table tokens.\\n5. The model generates an answer based on the table data and provides the capital city of Germany.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-base-finetuned-wikisql')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-base-finetuned-wikisql')\\ndef answer_question_about_table(question, table):\\n inputs = tokenizer(table=table, queries=question, return_tensors='pt')\\n outputs = model.generate(**inputs)\\n answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\n return answer[0]\\ncapital_city = answer_question_about_table(\\\"What is the capital of Germany?\\\", [\\n [\\n \\\"City\\\",\\n \\\"Germany\\\",\\n \\\"Capital\\\"\\n \\n ]\\n])\\n\"", "answer_id": "iKXgMRrvPmYSXRgmvtZDE9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 591, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/roberta-base-squad2')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, including AutoModelForQuestionAnswering, AutoTokenizer, and pipeline.\\n2. Load the pre-trained model 'deepset/roberta-base-squad2' using the AutoModelForQuestionAnswering class. This model has been trained on the SQuAD 2.0 dataset for question answering.\\n3. Initialize the tokenizer using the AutoTokenizer class and the same model name.\\n4. Create a pipeline object with the question-answering task and the model and tokenizer we just set up.\\n5. You can now use this pipeline to provide answers to customer questions by passing the question and context in which the answer can be found.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel_name = 'deepset/roberta-base-squad2'\\nnlp = pipeline('question-answering', model=model_name, tokenizer=model_name)\\nQA_input = {\\n 'question': 'Why is model conversion important?',\\n 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'\\n}\\nres = nlp(QA_input)\\n\"", "answer_id": "J9JWhTC6Yr4TwrWHFwmTAA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 592, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/bert-base-cased-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including AutoModelForQuestionAnswering, AutoTokenizer, and pipeline from the transformers package.\\n2. Load the pre-trained model 'deepset/bert-base-cased-squad2' using the AutoModelForQuestionAnswering.from_pretrained() function.\\n3. Load the tokenizer using the AutoTokenizer.from_pretrained() function with the same model name.\\n4. Create a question-answering pipeline using the pipeline() function, passing the model and tokenizer.\\n5. Provide the input context and question, and use the pipeline to get the answer to the question.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel = AutoModelForQuestionAnswering.from_pretrained('deepset/bert-base-cased-squad2')\\ntokenizer = AutoTokenizer.from_pretrained('deepset/bert-base-cased-squad2')\\nnlp = pipeline('question-answering', model=model, tokenizer=tokenizer)\\nQA_input = {\\n 'question': 'What is the cause of price inflation?',\\n 'context': 'Price inflation is caused by increases in the supply of money or credit, leading to a fall in the value of money. This can be seen when the inflation rate rises above 0%.'\\n}\\nanswer = nlp(QA_input)\\n\"", "answer_id": "PD6dKP9egXheMzaZTfcmv4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 593, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='typeform/squeezebert-mnli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'typeform/squeezebert-mnli'.\\n3. The loaded model will be used for zero-shot classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. The model will classify the given text message into different categories like 'finances', 'health', and 'entertainment' based on the semantic similarity of the text and the predefined categories.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='typeform/squeezebert-mnli')\\ntext_message = \\\"I am considering opening a savings account.\\\"\\ncandidate_labels = ['finances', 'health', 'entertainment']\\nresult = classifier(text_message, candidate_labels)\"", "answer_id": "PhtPwhhxV49W5Phn7HLSih", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 594, "text": "\\n<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='baptisteDoyen/camembert-base-xnli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a zero-shot classification model, which can classify text into predefined categories even if it has not been explicitly trained on those categories.\\n3. Load the model 'baptisteDoyen/camembert-base-xnli', which is a French version of the CamemBERT model, trained on a larger dataset of French text and fine-tuned for zero-shot classification tasks.\\n4. Use the created classifier to classify French articles into various categories like 'sport', 'politics', 'health', and 'technology'.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='baptisteDoyen/camembert-base-xnli')\\narticle_text = \\\"Texte de l'article en fran\\u00e7ais...\\\"\\ncandidate_labels = ['sport', 'politique', 'sante', 'technologie']\\nresult = classifier(article_text, candidate_labels)\\n\"", "answer_id": "3murUPqMMRMJ8qdAP7wDf9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 595, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_es', model='Helsinki-NLP/opus-mt-en-es')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary functions from the transformers library, which includes the 'pipeline' function for translation tasks.\\n2. Create a translation pipeline with the 'Helsinki-NLP/opus-mt-en-es' model, which has been trained to translate text from English to Spanish.\\n3. Pass the user manual text to the translation pipeline, which will return the translated text in Spanish.\\n4. Repeat this process for any other languages you need to support besides English and Spanish.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_es', model='Helsinki-NLP/opus-mt-en-es')\\nuser_manual = \\\"This is the English user manual text.\\\"\\ntranslated_manual = translator(user_manual, max_length=512, num_return_sequences=1)\\n\"", "answer_id": "kmrBTrcamKXmanNYbAcM7N", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 596, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='svalabs/gbert-large-zeroshot-nli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, specifically the 'pipeline' function from the transformers library provided by Hugging Face.\\n2. Create a zero-shot classification pipeline with the model 'svalabs/gbert-large-zeroshot-nli'. This model is trained for zero-shot classification in German, which is what we need for classifying movie synopses.\\n3. Prepare a list of candidate labels, which are the three categories we want to classify movie synopses into: crime, tragedy, and theft.\\n4. Use the classifier to predict the category for a given movie synopsis.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='svalabs/gbert-large-zeroshot-nli')\\nsequence = 'Synopsis auf Deutsch hier einf\\u00fcgen...'\\ncandidate_labels = ['crime', 'tragedy', 'theft']\\nresult = classifier(sequence, candidate_labels, multi_label=False)\\ncategory = result['labels'][0]\"", "answer_id": "68Etxc9aBjRZk5MUBDtWeQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 597, "text": "Social media has both positive and negative impacts on mental health, according to several studies. While it allows users to connect and stay in touch with friends and family, excessive use can lead to anxiety, depression, and other mental health issues. Studies suggest limiting screen time and practicing self-care to reduce the negative effects of social media on mental health.\"", "answer_id": "RyUXA9qqZ5B5HqLtWUCCA7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 598, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_es_to_en', model='Helsinki-NLP/opus-mt-es-en')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary functions from the transformers library, which are 'pipeline' and 'AutoTokenizer'.\\n2. Use the 'pipeline' function to create a translation pipeline, specifying the task as 'translation_es_to_en' and the model as 'Helsinki-NLP/opus-mt-es-en'.\\n3. This model is specifically designed for translating from Spanish to English.\\n4. Pass the Spanish text into the translation pipeline, and the output will be the English translation of the given text.\\n<<>>: from transformers import pipeline, AutoTokenizer\\ntranslation_pipeline = pipeline('translation_es_to_en', model='Helsinki-NLP/opus-mt-es-en')\\nspanish_text = \\\"Lo siento, pero no puedo ir a la reuni\\u00f3n debido a una emergencia personal. Avisar\\u00e9 al equipo y nos pondremos en contacto para reprogramar la reuni\\u00f3n.\\\"\\nenglish_translation = translation_pipeline(spanish_text)[0]['translation_text']\"", "answer_id": "QYYcTWDChrVuAgdWkSLz2k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 599, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-generation model.\\n3. Specify the model 'Zixtrauce/BDBot4Epoch' to be loaded. This is a GPT-2 based conversational model trained on conversational data and can generate human-like responses.\\n4. The created model can be used to generate responses based on user questions in a conversational context, which can be tailored to the persona of an elderly person.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\nresponse = chatbot(\\\"What's the weather like today?\\\")\\n\"", "answer_id": "b349SGbdmB2fbwsrRAAGgG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 600, "text": "\\n<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelForCausalLM.from_pretrained('facebook/blenderbot-90M')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face. In this case, we will need AutoModelForCausalLM and AutoTokenizer.\\n2. Load the pre-trained chatbot model 'facebook/blenderbot-90M' using AutoModelForCausalLM.from_pretrained(). This model is designed to generate conversational responses and can engage with users on a variety of topics.\\n3. Load the corresponding tokenizer for the model using AutoTokenizer.from_pretrained().\\n4. To generate a response from the chatbot, provide an input message text that follows the format the model expects. Then, tokenize this input and pass it to the model.\\n5. Decode the generated tokens back to a text string and this will be the chatbot's response.\\n<<>>: from transformers import AutoModelForCausalLM, AutoTokenizer\\nmodel = AutoModelForCausalLM.from_pretrained('facebook/blenderbot-90M')\\ntokenizer = AutoTokenizer.from_pretrained('facebook/blenderbot-90M')\\ninput_text = \\\"Tell me an interesting fact about the universe.\\\"\\ninput_ids = tokenizer.encode(input_text, return_tensors='pt')\\noutput_ids = model.generate(input_ids, max_length=50)\\nresponse = tokenizer.decode(output_ids[:, len(input_text):][0], skip_special_tokens=True)\\n\"", "answer_id": "ctM6wmeB6qNyRwvTeDw3xh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 601, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='decapoda-research/llama-7b-hf')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' and 'set_seed' from transformers.\\n2. Use the 'pipeline' function to create a text generation model using the 'decapoda-research/llama-7b-hf' model.\\n3. The model should be set to a specific seed value to ensure reproducibility of the generated text.\\n4. Provide a short description as input to the model, and it will generate a creative story idea based on the given text.\\n<<>>: from transformers import pipeline, set_seed\\nset_seed(42)\\ngenerator = pipeline('text-generation', model='decapoda-research/llama-7b-hf')\\nstory_idea = generator(\\\"Once upon a time in a small village,\\\", max_length=100, num_return_sequences=1)\\n\"", "answer_id": "HSWCytufoTNnbty2e6MJi8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 602, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('facebook/opt-13b')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes and functions from the transformers library provided by Hugging Face.\\n2. Use the from_pretrained method of AutoModelForCausalLM to load the pre-trained model 'facebook/opt-13b'. This model is designed for generating human-like text and can be used in a variety of applications, including chatbot responses.\\n3. Create a tokenizer to process the input text into a format suitable for the model.\\n4. Use the model and tokenizer to generate text based on the input, which can then be used as the response to customer queries.\\n<<>>: \\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\\nimport torch\\nmodel = AutoModelForCausalLM.from_pretrained('facebook/opt-13b', torch_dtype=torch.float16).cuda()\\ntokenizer = AutoTokenizer.from_pretrained('facebook/opt-13b', use_fast=False)\\ninput_text = \\\"Customer query goes here\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids.cuda()\\noutput = model.generate(input_ids)\\ngenerated_text = tokenizer.batch_decode(output, skip_special_tokens=True)[0]\\n\"", "answer_id": "b9brrpk9M3oaYRsb6oPBMf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 603, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='bigscience/bloom-7b1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is the 'pipeline' function from the transformers package.\\n2. Use the 'pipeline' function to load the 'bigscience/bloom-7b1' model, which is a text generation model designed to generate natural-sounding text.\\n3. Call the model and provide a text prompt to generate a more natural and conscious-sounding explanation.\\n4. This will output a text string that can be used for various automated explanations.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='bigscience/bloom-7b1')\\nprompt = \\\"In order to improve customer satisfaction, we want to automate the creation of written explanations that sound more natural and less robotic. Here is a scenario that requires a human-like explanation...\\\"\\noutput = text_generator(prompt)[0]['generated_text']\\n\"", "answer_id": "fW9GnnDVyHFCxVAtU9FjCK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 604, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: pipeline('translation_en_to_de', model='sshleifer/tiny-marian-en-de')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a translation model that translates English text to German text.\\n3. Specify the model 'sshleifer/tiny-marian-en-de' to be loaded, which is a small and efficient model for English to German translation.\\n4. The created translator can be used to translate any English text to German to ensure effective communication across different offices and countries.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_de', model='sshleifer/tiny-marian-en-de')\\nenglish_text = \\\"Hello, how are you?\\\"\\ngerman_translation = translator(english_text)[0]['translation_text']\\n\"", "answer_id": "6JuKPPo9h26UUYmE4F3eZA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 605, "text": "<<>>: Natural Language Processing Translation\\n<<>>: MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-pl-es'), MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-pl-es')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package, including the MarianMTModel and MarianTokenizer models for translation.\\n2. We then use the from_pretrained method of the MarianMTModel and MarianTokenizer classes to load the pre-trained model 'Helsinki-NLP/opus-mt-pl-es'. This model has been trained for translating from Spanish to Polish, which is exactly what we need for creating Polish subtitles for YouTube videos.\\n3. With the model and tokenizer loaded, we can then use them to translate a given Spanish text into Polish using the translate method of the tokenizer.\\n4. The translated text can be used as subtitles for the desired YouTube video.\\n<<>>: from transformers import MarianMTModel, MarianTokenizer\\nmodel = MarianMTModel.from_pretrained('Helsinki-NLP/opus-mt-pl-es')\\ntokenizer = MarianTokenizer.from_pretrained('Helsinki-NLP/opus-mt-pl-es')\\ntext = \\\"Hola, esta es una prueba.\\\"\\ntokenized_text = tokenizer.translate(text)\\ntranslated_text = model.generate(tokenized_text)\\n\"", "answer_id": "3tWT9aBTvYqzBFu3WLQup3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 606, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilbert-base-uncased')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'distilbert-base-uncased'.\\n3. The loaded model will be used for fill-mask tasks, which is to generate words or phrases that fit in the blank of a given sentence.\\n4. Provide the input sentence with the word \\\"happy\\\" replaced with a [MASK] token, and the model will generate meaningful alternatives for the masked word.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilbert-base-uncased')\\nhappy_sentence = \\\"I am [MASK] and happy.\\\"\\nresult = unmasker(happy_sentence)\\n\"", "answer_id": "VyvDue4Pf6tMrPnECQbqaX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 607, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilbert-base-uncased')\\n<<>>: Transformers\\n<<>>:1. Import the pipeline function from the transformers library.\\n2. Create an instance of the fill-mask pipeline using the 'distilbert-base-uncased' model.\\n3. Use the unmasker to complete sentences where a word has been replaced with a masked token [MASK].\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilbert-base-uncased')\\ninput_sentence = \\\"Hello, I'm a [MASK] specialist.\\\"\\ncompleted_sentence = unmasker(input_sentence)\\n\"", "answer_id": "VFRSfHuvqwFqFF24NeeWjo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 608, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa'.\\n3. The loaded model is designed for document question answering tasks, which involve filling in gaps in a given text.\\n4. Provide the context and the gap where the model should fill in the missing content in the legal document.\\n5. The model will return a prediction based on its understanding of the context and the gap, aiming to fill in the missing content in a coherent and relevant manner.\\n<<>>: from transformers import pipeline\\nqa_model = pipeline('question-answering', model='Sayantan1993/layoutlmv2-base-uncased_finetuned_docvqa')\\ncontext = \\\"Legal document context here...\\\"\\ngap = \\\"Replace this word or sentence with the missing content...\\\"\\nquestion = \\\"What is the missing content?\\\"\\nanswer = qa_model({'context': context, 'question': question})\\nfilled_context = answer['answer']\\n\"", "answer_id": "dPvngkxsFoD4wT7s57Kb5y", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 609, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import SentenceTransformer from the sentence_transformers library provided by Hugging Face.\\n2. Instantiate the SentenceTransformer model using the 'sentence-transformers/all-MiniLM-L12-v2' model.\\n3. This model can encode sentences as vectors in a 384-dimensional dense vector space.\\n4. We can then use this model to calculate the similarity between sentences by computing their cosine similarity or other distance metrics.\\n5. By keeping the most important sentences, our system can help with the study and analysis of English literature.\\n<<>>: from sentence_transformers import SentenceTransformer\\nimport numpy as np\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nsentences = ['a', 'b', 'c', 'd', 'e', 'f', 'g']\\n# replace with your sentences\\nmodel = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')\\nembeddings = model.encode(sentences)\\nmost_important_indices = np.argsort(embeddings[0].detach().numpy(), axis=1)[:5]\\nimportant_sentences = sentences[most_important_indices[0]]\\n\"", "answer_id": "XUuy6EtzbyHERWT76W8rgK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 610, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary class from the sentence_transformers package.\\n2. Instantiate the SentenceTransformer class with the model 'sentence-transformers/all-mpnet-base-v2'.\\n3. Use the encode method of the SentenceTransformer class to create embeddings for a list of sentences.\\n4. The embeddings can then be compared using a similarity metric like cosine similarity to find similar sentences in a text document.\\n<<>>: from sentence_transformers import SentenceTransformer\\nsentences = [\\\"This is an example sentence.\\\", \\\"Each sentence is converted.\\\"]\\nmodel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\nembeddings = model.encode(sentences)\\n\"", "answer_id": "5mDyckuJTy92kFrUpqfwUB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 611, "text": "1. Import the SentenceTransformer class from the sentence_transformers package.\\n2. Instantiate the SentenceTransformer model, which is pre-trained to capture semantic similarity between sentences.\\n3. Encode user questions and a set of reference questions using the model's encode method. This will generate embeddings for each question.\\n4. Compute the cosine similarity between the embeddings of the user question and the reference questions to determine how similar they are. A higher cosine similarity score indicates that the questions are more similar.\\n<<>>: from sentence_transformers import SentenceTransformer\\nmodel = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')\\nuser_question = \\\"What is the capital of France?\\\"\\nreference_questions = [\\\"What is the capital of France?\\\", \\\"What is the capital of Italy?\\\", \\\"What is the capital of Spain?\\\"]\\nembeddings = model.encode(reference_questions + [user_question])\\nsimilarity_scores = cosine_similarity(embeddings[0].detach().numpy(), embeddings[1:].detach().numpy())[0]\\n\"", "answer_id": "dwEyT4kR5HjpvYAJZzLYrA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 612, "text": "<<>>: Audio Text-to-Speech\\n<<>>: pipeline('text-to-speech', model='microsoft/synthesizer-ms')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then use the pipeline function to create a Text-to-Speech model, specifying the 'microsoft/synthesizer-ms' model which is capable of generating high-quality speech from text.\\n3. We then provide the text announcement to the model, and it will generate an audio file containing the synthesized speech.\\n4. This synthesized speech can then be played to customers using appropriate audio playback software.\\n<<>>: from transformers import pipeline\\ntext_to_speech = pipeline('text-to-speech', model='microsoft/synthesizer-ms')\\nannouncement = \\\"Dear valued customers, we are glad to announce a 30% discount on our special offer. Hurry up!\\\"\\naudio_output = text_to_speech(announcement, vocoder=None, sf=24000)\"", "answer_id": "k2jD5DUmh2395RYCV7WXPU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 613, "text": "<<>>: Audio Text-to-Speech\\n<<>>: Text2Speech.from_pretrained('espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan')\\n<<>>: ESPnet\\n<<>>: 1. Import the necessary libraries, which are 'Text2Speech' from ESPnet and 'torchaudio'.\\n2. Load a pre-trained Text-to-Speech model using the 'espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan' identifier.\\n3. Convert the text of the book to audio using the loaded model. Save the audio file for further use or distribution.\\n4. This process can be applied to various texts to create multiple audiobook versions.\\n<<>>: import torchaudio\\nfrom ESPnet import Text2Speech\\nmodel = Text2Speech.from_pretrained('espnet/kan-bayashi_ljspeech_joint_finetune_conformer_fastspeech2_hifigan')\\n# Replace the \\\"book text\\\" with the actual text of the book\\ntext = \\\"Book text goes here\\\"\\naudio = model(text)\\ntorchaudio.save('audiobook_version.wav', audio.unsqueeze(0).cpu(), 22050)\\n\"", "answer_id": "8HyBqiVJxnBhYVpRZNYH5L", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 614, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/tat-tts-hk-layer12.km2500-frame-wise')\\n<<>>: Fairseq\\n<<>>: 1. We import the required libraries and functions, including 'load_model_ensemble_and_task_from_hf_hub' from the 'fairseq.checkpoint_utils' package and 'TTSHubInterface' from the 'fairseq.models.text_to_speech.hub_interface' package.\\n2. We then load the pre-trained model 'facebook/tat-tts-hk-layer12.km2500-frame-wise' from the Hugging Face model hub using the 'load_model_ensemble_and_task_from_hf_hub' function.\\n3. With the loaded model, we can convert the input text in Hokkien (e.g., 'tung houi leih ngoh hng') to speech using the 'TTSHubInterface.get_model_input' and 'TTSHubInterface.get_prediction' functions.\\n4. The generated audio waveform can be saved or played back as needed in the language application.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/tat-tts-hk-layer12.km2500-frame-wise')\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = \\\"tung houi leih ngoh hng\\\"\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "kNMmjCwH5dRNDXT9FandJA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 615, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2ForCTC.from_pretrained('jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required Wav2Vec2ForCTC class from the transformers library provided by Hugging Face.\\n2. Load the pre-trained model 'jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli' using the from_pretrained method. This model specializes in transcribing audio with punctuation marks for accurate speech recognition.\\n3. Create a ctc_generator to process the audio input and generate the transcriptions.\\n4. Process the audio file (e.g., using librosa) to obtain the input values and retrieve the start and end times.\\n5. Use the ctc_generator to generate the transcription for the given audio file, and then decode the output to obtain the final text transcript with punctuation marks.\\n6. Return the transcriptions with punctuation for the given audio file.\\n<<>>: from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\\nimport torch\\nmodel = Wav2Vec2ForCTC.from_pretrained('jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli')\\nprocessor = Wav2Vec2Processor.from_pretrained('jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli')\\n# Load and preprocess audio file\\naudio_input, sample_rate = librosa.load('audio_file_path.wav', sr=None)\\ninput_values = processor(audio_input, sampling_rate=sample_rate, return_tensors='pt').input_values\\n# Generate transcription\\nlogits = model(input_values).logits\\npredicted_ids = torch.argmax(logits, dim=-1)\\ntranscription = processor.decode(predicted_ids[0])\\n# Return transcription with punctuation\\ntranscription_with_punctuation = ' ' + transcription + ' '\\n\"", "answer_id": "DxNRTyQpD9zqXjsfojz2dq", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 616, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2Model.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class and library from the huggingsound package.\\n2. We then use the SpeechRecognitionModel class to load the pre-trained model 'jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn'. This model has been specifically trained to recognize Chinese speech and has high performance and accuracy.\\n3. We provide a list of audio file paths containing the Chinese podcasts as input to the transcribe function. This function then transcribes the audio files into text format.\\n4. The transcriptions can be saved to a file or used for further processing, such as translation or text mining.\\n<<>>: from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel('jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn')\\naudio_paths = ['/path/to/chinese_podcast1.mp3', '/path/to/chinese_podcast2.wav']\\n# replace '/path/to/chinese_podcast1.mp3' and '/path/to/chinese_podcast2.wav' with your audio file paths\\ntranscriptions = model.transcribe(audio_paths)\\n\"", "answer_id": "V4wZ5mWEkhqSsANNEXxZM4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 617, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the transformers package. This includes BaseModel for the audio enhancement model.\\n2. We then use the from_pretrained method of the BaseModel class to load the pre-trained model 'JorisCos/DCUNet_Libri1Mix_enhsingle_16k'. This model has been trained for audio enhancement tasks, which is exactly what we need for cleaning up the background noise in our podcast library.\\n3. This model can then be used to process the noisy podcast audio files and output cleaned audio files with enhanced quality.\\n<<>>: from transformers import BaseModel\\nmodel = BaseModel.from_pretrained('JorisCos/DCUNet_Libri1Mix_enhsingle_16k')\\ncleaned_audio = model(podcast_noisy_audio)\\n\"", "answer_id": "fsLa2STUx5ddPtmkQUJkYm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 618, "text": "1. Use a noise reduction algorithm to remove background noise from the audio files.\\n2. Apply audio denoising techniques, such as filtering, to reduce noise and improve audio quality.\\n3. Use an audio encoder to encode the denoised audio file, which will save space and optimize file transfer.\\n4. Save the denoised and encoded audio file for use in the final podcast.\\n###code: import torchaudio\\nfrom torchaudio.transforms import NoiseSuppression\\nnoise_suppression = NoiseSuppression().to('cuda')\\ndenoised_audio = noise_suppression(path_to_audio_file)\\n# Replace 'path_to_audio_file' with the path to your audio file\\n# Save the denoised and encoded audio file\\n# Note: Make sure to resample audio to 22,088 Hz before saving\\ndenoised_audio.save('denoised_audio.wav')\\n\"", "answer_id": "LYUPnUsE9WNk3tapmET8HH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 619, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: SpeechT5ForSpeechToSpeech.from_pretrained('microsoft/speecht5_vc')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes and modules from the transformers, datasets, and soundfile packages.\\n2. Load the required pre-trained models, 'microsoft/speecht5_vc' for SpeechT5 and 'microsoft/speecht5_hifigan' for CNG, from Hugging Face Transformers.\\n3. Load the customer call audio into a variable and preprocess it as necessary.\\n4. Use the 'text' attribute from the loaded SpeechT5 model and the 'model.hparams.pretrained_vocoder' attribute to select the vocoder.\\n5. Encode the text input using the Tacotron2 model, which generates a spectrogram representation of the audio.\\n6. Use the HiFi-GAN model to generate a waveform from the spectrogram.\\n7. Save the generated waveform as an audio file with a different voice, but without changing the content of the original customer call.\\n<<>>: from transformers import SpeechT5Processor, SpeechT5ForSpeechToSpeech, SpeechT5HifiGan\\nfrom datasets import load_dataset\\nimport torch\\nimport soundfile as sf\\nimport numpy as np\\nimport librosa\\ntext = \\\"your_text_here\\\"\\n# replace 'your_text_here' with the text from the customer call\\nprocessor = SpeechT5Processor.from_pretrained('microsoft/speecht5_vc')\\nmodel = SpeechT5ForSpeechToSpeech.from_pretrained('microsoft/speecht5_vc')\\nvocoder = SpeechT5HifiGan.from_pretrained('microsoft/speecht5_hifigan')\\n# Load and preprocess the customer call audio\\naudio, sr = load_audio('customer_call.wav')\\ninput_values = processor(text, audio, sampling_rate=sr, return_tensors='pt').input_values\\n# Encode the text using Tacotron2\\nencoding = tacotron2.encode_text(text)\\n# Generate spectrogram using HiFi-GAN\\ngenerated_wav, rate = vocoder.decode_batch(encoding)\\n# Save the converted audio as a different voice\\nsf.write('converted_call.wav', generated_wav.squeeze(axis=0), sample_rate=rate)\"", "answer_id": "XTVbTDdLgUu2MvfoRbVkTu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 620, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: Wav2Vec2Model.from_pretrained(cached_download('https://huggingface.co/facebook/textless_sm_cs_en/resolve/main/model.pt'))\\n<<>>: Fairseq\\n<<>>:1. We first import the necessary class from the fairseq package. This includes Wav2Vec2Model for audio-to-audio translation.\\n2. We then use the from_pretrained method of the Wav2Vec2Model class to load the pre-trained model 'facebook/textless_sm_cs_en'. This model has been trained for speech-to-speech translation tasks, which is what we need for translating the customer complaint from Czech to English.\\n3. We load the audio file containing the customer complaint in Czech.\\n4. This model can then be used to translate the audio file into English, preserving the audio format of the output audio file.\\n<<>>: from fairseq.models.wav2vec.wav2vec2_asr import Wav2Vec2Model\\nmodel = Wav2Vec2Model.from_pretrained(cached_download('https://huggingface.co/facebook/textless_sm_cs_en/resolve/main/model.pt'))\\n# Load the audio file in Czech\\naudio_czech = cached_download('path/to/czech_audio_file.wav')\\n# Translate the Czech audio file into English\\nenglish_translation = model(audio_czech)\\n\"", "answer_id": "3T2Z9LM92xAhvbFHFAEmHR", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 621, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Awais/Audio_Source_Separation'.\\n3. This model is trained for audio source separation tasks, which is perfect for separating speakers from a recorded audio.\\n4. Pass the audio file to the model, and it will return the separated audio sources as separate files, one for each speaker.\\n5. You can then save these separated audio sources as separate files to be used in your podcast editing process.\\n<<>>: from transformers import pipeline\\naudio_separator = pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\nseparated_audio_sources = audio_separator(audio_file)\\n# Save separated audio sources as separate files\\nfor speaker_index, separated_audio_source in enumerate(separated_audio_sources):\\n speaker_separated_audio_file = f'speaker_{speaker_index}.wav'\\n speaker_separated_audio = separated_audio_source[speaker_index]\\n speaker_separated_audio.write(speaker_separated_audio_file)\"", "answer_id": "Qiz5uozVtLAbjaP896fnRa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 622, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_s2ut_en-hk')\\n<<>>: Fairseq\\n<<>>: 1. Import necessary libraries, such as fairseq, torchaudio, and huggingface_hub.\\n2. Load the pre-trained speech-to-speech translation model 'facebook/xm_transformer_s2ut_en-hk' using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Load an audio file containing the English speech input.\\n4. Prepare the audio input and obtain the translated Hokkien text using the S2THubInterface class.\\n5. Use the HiFiGAN vocoder to convert the translated text into speech.\\n6. Play the synthesized Hokkien audio using IPython.display.Audio.\\n<<>>: import json\\nimport os\\nfrom pathlib import Path\\nimport IPython.display as ipd\\nfrom fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nfrom fairseq.models.text_to_speech import CodeHiFiGANVocoder\\nfrom fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\\nfrom huggingface_hub import snapshot_download\\ncache_dir = os.getenv('HUGGINGFACE_HUB_CACHE')\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_s2ut_en-hk', arg_overrides={'config_yaml': 'config.yaml', 'task': 'speech_to_text'}, cache_dir=cache_dir)\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\naudio, _ = torchaudio.load('/path/to/an/audio/file')\\nsample = S2THubInterface.get_model_input(task, audio)\\nunit = S2THubInterface.get_prediction(task, model, sample)\\ncache_dir = snapshot_download('facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS', cache_dir=cache_dir, library_name='fairseq')\\nx = hub_utils.from_pretrained(cache_dir, 'model.pt', '.', archive_map=CodeHiFiGANVocoder.hub_models(), config_yaml='config.json', fp16=False, is_vocoder=True)\\nvocoder = CodeHiFiGANVocoder(x['args']['model_path'][0], x['cfg'])\\ntts_model = VocoderHubInterface(vocoder_cfg, vocoder)\\ntts_sample = tts_model.get_model_input(unit)\\nwav, sr = tts_model.get_prediction(tts_sample)\\nipd.Audio(wav, rate=sr)\\n\"", "answer_id": "eNv5ZHnzQLrJ5jNdAQ8GD5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 623, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_unity_hk-en')\\n<<>>: Fairseq\\n<<>>:1. Import the necessary libraries and functions from fairseq, torchaudio, and Hugging Face Hub.\\n2. Load the pre-trained speech-to-speech translation model 'facebook/xm_transformer_unity_hk-en' from Hugging Face Hub using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Load the audio file containing the Hokkien speech using torchaudio.load function.\\n4. Process the loaded audio using the S2THubInterface's get_model_input function, which converts the audio into an appropriate format for the translation model.\\n5. Get the translated text using the model's get_prediction function, which outputs the translated English text.\\n6. The model can then be used to convert the input Hokkien audio to English text.\\n<<>>: import torchaudio\\nfrom fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/xm_transformer_unity_hk-en')\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\ngenerator = task.build_generator([model], cfg)\\naudio, _ = torchaudio.load('/path/to/an/audio/file')\\nsample = S2THubInterface.get_model_input(task, audio)\\ntranslation = S2THubInterface.get_prediction(task, model, generator, sample)\\n\"", "answer_id": "9XRTaUdy76EC2K2fDF8Z42", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 624, "text": "<<>>: Audio Classification\\n<<>>: AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary function and model classes from the transformers library provided by Hugging Face.\\n2. We create an audio classification model using the 'MIT/ast-finetuned-speech-commands-v2' model, which is fine-tuned for speech command recognition, a task where the model has to classify spoken commands into different categories.\\n3. The model can be used to analyze and classify audio files containing people's voices, and based on the category, it can return relevant information about the spoken trivia.\\n4. By integrating this model into the interactive platform for children, users can speak the trivia question aloud and get an immediate response about the category or fact.\\n<<>>: from transformers import AutoModelForAudioClassification, Wav2Vec2Processor\\nmodel = AutoModelForAudioClassification.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\nprocessor = Wav2Vec2Processor.from_pretrained('MIT/ast-finetuned-speech-commands-v2')\\n# Load and process an audio file containing spoken trivia\\n# Note: Replace the 'path/to/audio/file' with the actual path to the audio file\\naudio_path = 'path/to/audio/file'\\ninputs = processor(audio_path, return_tensors=\\\"pt\\\", sampling_rate=16000)\\noutputs = model(**inputs)\\ncategory = outputs.logits.argmax(-1).item()\\n\"", "answer_id": "BHkGdYYnKT2ELYERZ3b3nV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 625, "text": "<<>>: Audio Audio Classification\\n<<>>: AutoModelForAudioXVector.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers and torchaudio packages.\\n2. Load the pre-trained model 'anton-l/wav2vec2-base-superb-sv' which is trained for voice recognition and identification tasks.\\n3. Use the processor to extract features from the user's voice input, and compare these features to the stored voiceprints.\\n4. If the extracted features match any of the stored voiceprints, then the user is authenticated.\\n<<>>: from transformers import AutoProcessor, AutoModelForAudioXVector\\nimport torchaudio\\nprocessor = AutoProcessor.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\nmodel = AutoModelForAudioXVector.from_pretrained('anton-l/wav2vec2-base-superb-sv')\\n# Load the user's voice input\\nuser_audio, sample_rate = torchaudio.load('user_voice_input.wav')\\n# Extract features from the user's voice input\\ninput_features = processor(user_audio, sampling_rate=sample_rate, return_tensors='pt').input_features\\n# Compare the extracted features to the stored voiceprints\\nvoiceprint_indices = model(input_features).logits.argmax(-1).squeeze().tolist()\\n# The voiceprint indices correspond to the stored voiceprints\\n\"", "answer_id": "bSezzNKojtbkdBJvjX44UE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 626, "text": "<<>>: Multimodal Graph Machine Learning\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('janpase97/codeformer-pretrained')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary Python libraries, including torch, torch.nn, torch.optim, torchvision, torchvision.transforms, and transformers.\\n2. Use the transformers library to create a language model by loading the pretrained 'janpase97/codeformer-pretrained' model.\\n3. Define a custom tokenizer to tokenize the input prompts.\\n4. Create a function to generate programs based on the given prompts and settings.\\n5. Use the tokenizer and model to process the input prompts and generate code.\\n6. The generated code can be executed to infer the demographics of callers based on their spoken language.\\n<<>>: import torch\\nimport torch.nn as nn\\nimport torch.optim as optim\\nfrom torchvision import transforms\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('janpase97/codeformer-pretrained')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('janpase97/codeformer-pretrained')\\nprogram_generator = generate_program\\nprompt = \\\"Speech-to-Speech Idiomatic Expression Encoder\\\"\\nsettings = \\\"vocab_size=1000, src_lang=torch.language.medium, tgt_lang=torch.language.medium\\\"\\nmodel.eval()\\ntokenizer.eval()\\nif torch.cuda.is_available():\\n model.cuda()\\n tokenizer.cuda()\\nencoded_prompt = tokenizer.encode(prompt, return_tensors='pt', padding=True, truncation=True)\\ngenerated_code = program_generator(encoded_prompt, **settings)\\ngenerated_code = generated_code.squeeze()\\nprint(tokenizer.decode(generated_code, skip_special_tokens=True))\\n\"", "answer_id": "e4cTq3pmLkwbpswd4M2QXs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 627, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/hubert-base-superb-ks')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers and 'load_dataset' from datasets.\\n2. Use the 'pipeline' function from transformers to create an audio classification model by specifying the model 'superb/hubert-base-superb-ks'.\\n3. The created classifier will be used to recognize and classify voice commands based on the given command categories, such as 'lights_on', 'music_play', 'timer_set'.\\n4. The command classifier can be incorporated into a voice assistant that responds to voice commands for various tasks like turning on the lights, playing music, or setting a timer.\\n<<>>: from transformers import pipeline\\nfrom datasets import load_dataset\\nclassifier = pipeline('audio-classification', model='superb/hubert-base-superb-ks')\\n# Replace 'file_path.wav' with your audio file\\ncommands = ['lights_on', 'music_play', 'timer_set']\\nresult = classifier('file_path.wav', commands)\\n\"", "answer_id": "R2LiPkdtSjSFgwbbvd2Mwf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 628, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: VoiceActivityDetection(segmentation='pyannote/segmentation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary class from the pyannote.audio.pipelines package.\\n2. Then, create an instance of the VoiceActivityDetection class with the specified model 'pyannote/segmentation'.\\n3. Set the hyperparameters for the pipeline, including onset, offset, min_duration_on, and min_duration_off.\\n4. Instantiate the pipeline for voice activity detection.\\n5. Finally, use the pipeline to process an audio file, which will return the voice activity status and other relevant information.\\n<<>>: from pyannote.audio.pipelines import VoiceActivityDetection\\npipeline = VoiceActivityDetection(segmentation='pyannote/segmentation')\\nHYPER_PARAMETERS = {\\n 'onset': 0.5,\\n 'offset': 0.5,\\n 'min_duration_on': 0.0,\\n 'min_duration_off': 0.0\\n}\\npipeline.instantiate(HYPER_PARAMETERS)\\nvad = pipeline('audio.wav')\"", "answer_id": "nhxkgzfND7kGnbUJx2Bqfg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 629, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: Pipeline.from_pretrained('pyannote/voice-activity-detection')\\n<<>>: pyannote.audio\\n<<>>:1. Import the Pipeline class from the pyannote.audio library.\\n2. Create a VoiceActivityDetection pipeline by loading the pretrained model 'pyannote/voice-activity-detection'. This model can detect speech and silence in audio files.\\n3. Use the pipeline to process the audio stream of the conference call, which can be stored as an audio file or streamed directly from the conference call provider.\\n4. The pipeline will return a VoiceActivityDetection object that can be used to detect interruptions during the conference call. This information can then be used to control the program's behavior accordingly.\\n<<>>: from pyannote.audio import Pipeline\\nvad = Pipeline.from_pretrained('pyannote/voice-activity-detection')\\nconference_call_audio_file_path = 'conference_call.wav'\\n# replace 'conference_call.wav' with the path to your conference call audio file\\nvoice_activity_detection = vad(conference_call_audio_file_path)\\n\"", "answer_id": "cZ6PSyQiRZiuRiAcFN6844", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 630, "text": "<<>>: Tabular Tabular Classification\\n<<>>: load_model(cached_download(hf_hub_url('danupurnomo/dummy-titanic', 'titanic_model.h5')))\\n<<>>: Scikit-learn\\n<<>>: 1. Import the necessary libraries for this task, such as huggingface_hub, joblib, pandas, numpy, and tensorflow.\\n2. Load the pre-trained model 'danupurnomo/dummy-titanic' from the Hugging Face model hub using cached_download and hf_hub_url functions.\\n3. Load the candidate data into a pandas DataFrame and preprocess it by selecting the necessary features.\\n4. Use the loaded model to predict the labels (employee or not_employee) for each row in the DataFrame.\\n5. The predictions can then be used by the HR department to determine which candidates to hire.\\n<<>>: from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nimport numpy as np\\nfrom tensorflow.keras.models import load_model\\nREPO_ID = 'danupurnomo/dummy-titanic'\\nPIPELINE_FILENAME = 'final_pipeline.pkl'\\nTF_FILENAME = 'titanic_model.h5'\\nmodel_pipeline = joblib.load(cached_download(hf_hub_url(REPO_ID, PIPELINE_FILENAME)))\\nmodel_seq = load_model(cached_download(hf_hub_url(REPO_ID, TF_FILENAME)))\\nfeatures = ['age', 'sex', 'smoker', 'class', 'fare']\\ndata = pd.DataFrame([[x, y] for x in features for y in [0, 1]])\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model_seq.predict(data)\"", "answer_id": "iEn3jcXZFErretsEGxmfEv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 631, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>:1. Import the necessary libraries like joblib and pandas.\\n2. Load the pre-trained model using joblib.load() function, which in this case is 'model.joblib'.\\n3. Read the dataset from a CSV file using the pandas library.\\n4. Prepare the dataset by selecting the appropriate features and renaming the columns as required by the pre-trained model.\\n5. Finally, apply the model on the prepared dataset to classify it.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\n# Assuming the dataset is in 'data.csv' format\\nfeatures = ['feature1', 'feature2', 'feature3']\\npredictions = model.predict(data[features])\\n\"", "answer_id": "JWhLPS5xCGr6yR3jNEv7HH", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 632, "text": "<<>>: Tabular Tabular Regression\\n<<>>: pipeline('regression', model='julien-c/pokemon-predict-hp')\\n<<>>: Hugging Face\\n<<>>: 1. We first import the necessary library (pipeline) from the Hugging Face transformers package.\\n2. We create a regression pipeline by specifying the required 'regression' task and the pre-trained model 'julien-c/pokemon-predict-hp'.\\n3. This pipeline function can be used to predict the HP of a Pokemon based on the input attributes such as base_stats, abilities, etc.\\n4. We can use the create_pipeline() method to create a pipeline object that can be used for predicting the HP of a Pokemon.\\n<<>>: from transformers import pipeline\\nhp_predictor = pipeline('regression', model='julien-c/pokemon-predict-hp')\\ninput_data = {'base_stats': 50, 'abilities': ['keen eyesight', 'tough hide']}\\npredicted_hp = hp_predictor(input_data)\\n\"", "answer_id": "Wn37T27FiSdy6zSXhLic2S", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 633, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='bigscience/bloom-7b1')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text generation model for creating marketing messages.\\n3. Specify the model 'bigscience/bloom-7b1' to be loaded. This is a large, multilingual model based on the BLOOM architecture that can generate high-quality text.\\n4. Provide an initial seed text or prompt for the model to generate interesting marketing messages around.\\n5. The model will generate a sequence of text that can be used as a basis for creating marketing messages.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='bigscience/bloom-7b1')\\nseed_text = \\\"Introducing our new product, a revolutionary all-in-one gadget that simplifies your life...\\\"\\ngenerated_text = text_generator(seed_text)\\n\"", "answer_id": "J6hUndSjZ6Zx8d4B2aavn7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 634, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-distilroberta-v1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'SentenceTransformer' from sentence_transformers.\\n2. Load the pre-trained model 'sentence-transformers/all-distilroberta-v1' using the SentenceTransformer class.\\n3. Encode the input sentences using the model's 'encode' method, which converts the sentences into vector representations.\\n4. Calculate the cosine similarity between the vectors to find the most similar pairs of sentences.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nmodel = SentenceTransformer('sentence-transformers/all-distilroberta-v1')\\nsentences = ['This is an example sentence.', 'Each sentence is converted.']\\nembeddings = model.encode(sentences)\\nsimilarities = cosine_similarity(embeddings)\\nprint(similarities)\\n\"", "answer_id": "XhXoxsUDogHLRciYmKf8et", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 635, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('lanwuwei/BERTOverflow_stackoverflow_github')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library, including AutoTokenizer and AutoModelForTokenClassification.\\n2. Load the tokenizer and token classification model for 'lanwuwei/BERTOverflow_stackoverflow_github'.\\n3. Tokenize the input text from StackOverflow using the loaded tokenizer.\\n4. Pass the tokenized input to the loaded token classification model.\\n5. The model will output code syntax and named entities, which can be extracted and processed further as needed.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification\\nimport torch\\ntokenizer = AutoTokenizer.from_pretrained('lanwuwei/BERTOverflow_stackoverflow_github')\\nmodel = AutoModelForTokenClassification.from_pretrained('lanwuwei/BERTOverflow_stackoverflow_github')\\nstack_overflow_text = \\\"StackOverflow question or answer text here...\\\"\\ntokens = tokenizer(stack_overflow_text, return_tensors='pt')\\npredictions = model(**tokens)\\n\"", "answer_id": "MPULQm3BKK78SJR2Qn3q7B", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 636, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='valhalla/distilbart-mnli-12-3')\\n<<>>: Transformers\\n<<>>: 1. First, import the pipeline function from the transformers library.\\n2. Create the zero-shot classification pipeline with the model 'valhalla/distilbart-mnli-12-3'.\\n3. The pipeline function will instantiate the model and prepare it for zero-shot classification tasks.\\n4. Use the pipeline to classify movie titles into genres without the benefit of genre labels.\\n5. Provide a list of candidate genres, and the pipeline will return the most likely genre for each movie.\\n<<>>: from transformers import pipeline\\nzc_pipeline = pipeline('zero-shot-classification', model='valhalla/distilbart-mnli-12-3')\\nmovie_titles = ['The Lord of the Rings: The Fellowship of the Ring', 'The Matrix', 'Star Wars: Episode V - The Empire Strikes Back']\\ncandidate_genres = ['action', 'sci-fi', 'fantasy', 'drama', 'comedy']\\nresult = zc_pipeline(movie_titles, candidate_genres)\"", "answer_id": "UbHsoA7iPJuN2pKPM5Dn6k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 637, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2', scheduler=EulerDiscreteScheduler.from_pretrained('stabilityai/stable-diffusion-2', subfolder=scheduler), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries: StableDiffusionPipeline and EulerDiscreteScheduler from diffusers, and torch.\\n2. Load the pretrained model 'stabilityai/stable-diffusion-2' and the EulerDiscreteScheduler from the model checkpoint and the description file respectively.\\n3. Move the model to the GPU by calling the .to() method on the model and .to() method on the scheduler.\\n4. Provide the text prompt (\\\"a lighthouse on a foggy island\\\") and call the pipeline with the prompt to generate the image.\\n5. Save the generated image to a file (e.g., 'lighthouse_foggy_island.png').\\n<<>>: from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\\nimport torch\\nmodel_id = 'stabilityai/stable-diffusion-2'\\nscheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder='scheduler')\\npipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nprompt = 'a lighthouse on a foggy island'\\nimage = pipe(prompt).images[0]\\nimage.save('lighthouse_foggy_island.png')\\n\"", "answer_id": "6zVUw2n8RSPAnHMHkAWLeZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 638, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', vae='AutoencoderKL.from_pretrained(stabilityai/sd-vae-ft-mse)')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary classes from the diffusers and transformers packages. This includes StableDiffusionPipeline and AutoencoderKL for text-to-image generation.\\n2. We load the pre-trained text-to-image model 'CompVis/stable-diffusion-v1-4' and the fine-tuned VAE decoder 'stabilityai/sd-vae-ft-mse' using their respective from_pretrained methods.\\n3. Provide the text description as the input prompt for the image generation model.\\n4. Use the generated image and optional parameters (e.g., brightness, contrast, saturation) to fine-tune the visual appearance of the image.\\n5. Save the images for future use or incorporate it into a commercial product.\\n<<>>: from diffusers import StableDiffusionPipeline\\nfrom transformers import AutoencoderKL\\nmodel = 'CompVis/stable-diffusion-v1-4'\\nvae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse')\\nvae.eval()\\nprompt = 'a photo of an astronaut riding a horse on mars'\\nimage = StableDiffusionPipeline.from_pretrained(model, vae=vae).images[0]\\nimage.save('astronaut_rides_horse.png')\"", "answer_id": "KPqdLbsJbj3VSzWuH2ZE9o", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 639, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: pipeline('text-to-image', model='prompthero/openjourney-v4')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-to-image model using the 'prompthero/openjourney-v4' pre-trained model.\\n3. The created model can be used to generate images based on textual input, such as a request for an astronaut playing guitar in space.\\n4. We provide the textual input to the model, and it will generate a corresponding image.\\n<<>>: from transformers import pipeline\\ntext_to_image = pipeline('text-to-image', model='prompthero/openjourney-v4')\\ntext_input = \\\"Astronaut playing guitar in space\\\"\\ngenerated_image = text_to_image(text_input)\"", "answer_id": "MRcaLffEnYBKuAss5aQGjy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 640, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('darkstorm2150/Protogen_v2.2_Official_Release')\\n<<>>: Hugging Face\\n<<>>:1. Import the StableDiffusionPipeline class and Image class from the diffusers library.\\n2. Create a model instance using the from_pretrained method with the 'darkstorm2150/Protogen_v2.2_Official_Release' model.\\n3. Move to the forest scene by using the model to generate a beautiful fantasy landscape image based on the provided description.\\n4. Save the generated image to your preferred location.\\n<<>>: from diffusers import StableDiffusionPipeline\\nfrom diffusers.utils import load_image\\nimport torch\\nmodel_id = 'darkstorm2150/Protogen_v2.2_Official_Release'\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\ntext_description = 'a peaceful scene in a lush green forest with a crystal-clear river flowing through it, under a blue sky with fluffy white clouds'\\nimage = pipe(text_description).images[0]\\nimage.save('forest_scene.png')\\n\"", "answer_id": "hdXFL6xNR6SEXugR6NyWcr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 641, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-textcaps-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, which includes Pix2StructForConditionalGeneration for the image-to-text generation model and Pix2StructProcessor for preprocessing the image.\\n2. Load the pre-trained model 'google/pix2struct-textcaps-base' using the from_pretrained method of the Pix2StructForConditionalGeneration class.\\n3. Load the image with the provided URL and preprocess it using the Pix2StructProcessor.\\n4. Use the model to generate the summary and answer the question.\\n5. Print the summary and answer.\\n<<>>: from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor\\nimport requests\\nfrom PIL import Image\\nmodel = Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-textcaps-base')\\nprocessor = Pix2StructProcessor.from_pretrained('google/pix2struct-textcaps-base')\\nimg_url = \\\"https://example.com/image.jpg\\\"\\nimage = Image.open(requests.get(img_url, stream=True).raw)\\nquestion = \\\"What is the main color of the object?\\\"\\ninputs = processor(images=image, text_question=question, return_tensors='pt')\\npredictions = model.generate(**inputs)\\nsummary = processor.decode(predictions[0], skip_special_tokens=True)\\nprint(summary)\\n\"", "answer_id": "7C734iEaLtYYd2m4ELpRiT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 642, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: BlipForConditionalGeneration.from_pretrained('Salesforce/blip-image-captioning-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import necessary libraries: PIL for image processing, requests for fetching image from URL, and BlipProcessor and BlipForConditionalGeneration from transformers.\\n2. Load the pretrained Blip model and processor using model='Salesforce/blip-image-captioning-base' and processor='BlipProcessor.from_pretrained(model)'.\\n3. Open the image using PIL.Image.open and preprocess it with the processor's 'text' method.\\n4. Use the model's 'generate' method to create a text description of the image (caption).\\n5. Decode the caption with the processor's 'decode' method.\\n<<>>: import requests\\nfrom PIL import Image\\nfrom transformers import BlipProcessor, BlipForConditionalGeneration\\nmodel = BlipForConditionalGeneration.from_pretrained('Salesforce/blip-image-captioning-base')\\nprocessor = BlipProcessor.from_pretrained('Salesforce/blip-image-captioning-base')\\nurl = 'https://your-image-url.com/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = processor(text=model.config.id2label[0], image=image, return_tensors='pt')\\noutputs = model.generate(**inputs)\\ncaption = processor.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "GCUpc4eQnoR8oscfnHFeZd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 643, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: pipeline('text-to-video', model='ImRma/Brucelee')\\n<<>>: Hugging Face\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-to-video model.\\n3. Specify the model 'ImRma/Brucelee' to be loaded. This model is designed for Persian and English text-to-video generation.\\n4. The created model can be used to generate short videos based on text input in Persian and English.\\n5. To generate a video, pass the Persian or English text to the model as input, and the model will generate a video clip that corresponds to the given text.\\n6. Note that a separate API call is needed for decoding the video output.\\n<<>>: from transformers import pipeline\\ntext_to_video_pipeline = pipeline('text-to-video', model='ImRma/Brucelee')\\nenglish_text = \\\"Hello, how are you?\\\"\\npersian_text = \\\"\\u0645\\u0631\\u062d\\u0628\\u064b\\u0627 \\u060c\\u0647\\u0630\\u0627 \\u0627\\u062e\\u062a\\u0628\\u0627\\u0631 \\u062a\\u0634\\u063a\\u064a\\u0644.\\\"\\nresult_english = text_to_video_pipeline(english_text)\\nresult_persian = text_to_video_pipeline(persian_text)\\n\"", "answer_id": "WoxKT5P5AcSBmhVyezoN7K", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 644, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: pipeline('text-to-video-synthesis', model_dir.as_posix())\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and functions, such as snapshot_download from huggingface_hub, pipeline from modelscope.pipelines, and required modelscope outputs.\\n2. Download the pretrained model 'damo-vilab/modelscope-damo-text-to-video-synthesis' using the snapshot_download function from huggingface_hub.\\n3. Create a pipeline object using the 'text-to-video-synthesis' task and the downloaded model directory.\\n4. Provide a text description (e.g., \\\"cats playing with laser pointer\\\") as input to the pipeline object, and it will generate a short autogenerated video based on the provided text.\\n5. Save the generated video to a file, if desired.\\n<<>>: from huggingface_hub import snapshot_download\\nfrom modelscope.pipelines import pipeline\\nfrom modelscope.outputs import OutputKeys\\nimport pathlib\\nmodel_dir = pathlib.Path('weights')\\nsnapshot_download('damo-vilab/modelscope-damo-text-to-video-synthesis', repo_type='model', local_dir=model_dir)\\npipe = pipeline('text-to-video-synthesis', model_dir.as_posix())\\ntest_text = {'text': 'cats playing with laser pointer.'}\\noutput_video_path = pipe(test_text, )[OutputKeys.OUTPUT_VIDEO]\\nprint('output_video_path:', output_video_path)\"", "answer_id": "fN8JG664GcSJ9QuaFqq9JT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 645, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='pardeepSF/layoutlm-vqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the 'pardeepSF/layoutlm-vqa' model, which has been fine-tuned for document question answering tasks.\\n3. The loaded model will parse the document and answer questions related to the provided text.\\n4. The system can be used to answer questions based on the document provided.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='pardeepSF/layoutlm-vqa')\\ndocument = \\\"Your document text here...\\\"\\nquestion = \\\"Your question here...\\\"\\nanswer = qa_pipeline(question=question, context=document)\\n\"", "answer_id": "VqwbrGmoaLJh5MFytXw9w6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 646, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which include AutoModelForDocumentQuestionAnswering and AutoTokenizer from transformers.\\n2. Load the pre-trained model 'tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa' using the AutoModelForDocumentQuestionAnswering.from_pretrained() method.\\n3. Load the tokenizer using the AutoTokenizer.from_pretrained() method.\\n4. Preprocess the OCR text to handle layout and convert it to a tokenized input format.\\n5. Use the model to answer questions based on the OCR text.\\n6. Post-process the answer to be human-readable.\\n<<>>: from transformers import AutoModelForDocumentQuestionAnswering, AutoTokenizer\\nmodel = AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa')\\ntokenizer = AutoTokenizer.from_pretrained('tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa')\\nocr_text = \\\"\\\"\\ninputs = tokenizer(ocr_text, return_tensors=\\\"pt\\\")\\noutputs = model(**inputs)\\nanswer_start = torch.argmax(outputs.start_logits)\\nanswer_end = torch.argmax(outputs.end_logits) + 1\\nanswer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs[\\\"input_ids\\\"][0][answer_start:answer_end]))\"", "answer_id": "mEGohzMnv8ZRS4ryxGFik9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 647, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221122-082237')\\n<<>>: Hugging Face Transformers\\n<<>>:1. To use the model for depth estimation, first import the necessary classes from the transformers package, such as AutoModel.\\n2. Load the pre-trained model 'sayakpaul/glpn-nyu-finetuned-diode-221122-082237' using the from_pretrained method of AutoModel.\\n3. The loaded model is designed for depth estimation tasks, and it can be used to analyze video frames for the depth information of objects in the scene.\\n4. By understanding the depth of objects in the video stream, the home security system can make decisions based on the depth information, such as whether to trigger an alarm if an object approaches the door.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221122-082237')\\n# Analyze video frames for depth information\\n# depth_analysis = model(video_frame_tensor)\\n\"", "answer_id": "jgUUNR8gmwE6Dz2qtcxCRv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 648, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: DPTForDepthEstimation.from_pretrained('hf-tiny-model-private/tiny-random-DPTForDepthEstimation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers and PIL packages. This includes DPTForDepthEstimation for the depth estimation model and Image for processing image data.\\n2. Use the from_pretrained method to load the pre-trained model 'hf-tiny-model-private/tiny-random-DPTForDepthEstimation' from Hugging Face Transformers.\\n3. Load the image data from a file or acquire it in real-time from a camera.\\n4. Then process the image with the DPTForDepthEstimation model to get the depth information. This information can be used to help the robot navigate in its environment.\\n<<>>: from transformers import DPTForDepthEstimation\\nfrom PIL import Image\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with the path to your image\\nmodel = DPTForDepthEstimation.from_pretrained('hf-tiny-model-private/tiny-random-DPTForDepthEstimation')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\npredicted_depth = outputs.predicted_depth\\n\"", "answer_id": "ZbNPAAPGggGMBC95Y3Ubuc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 649, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: DPTForDepthEstimation.from_pretrained('hf-tiny-model-private/tiny-random-DPTForDepthEstimation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library, such as DPTForDepthEstimation for depth estimation.\\n2. Use the from_pretrained method to load the pre-trained model 'hf-tiny-model-private/tiny-random-DPTForDepthEstimation' for depth estimation.\\n3. Load the image data from a file or acquire it in real-time from the robot's camera.\\n4. Use the model to perform depth estimation on the input image, which will provide critical information for the robot's navigation system.\\n<<>>: from transformers import DPTForDepthEstimation\\nfrom PIL import Image\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with path to your image\\nmodel = DPTForDepthEstimation.from_pretrained('hf-tiny-model-private/tiny-random-DPTForDepthEstimation')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\ndepth_estimate = outputs.predicted_depth\\n\"", "answer_id": "YfDnzadb2CVkSvos4VgGzk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 650, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='laion/CLIP-ViT-B-16-laion2B-s34B-b88K')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from transformers.\\n2. Create a zero-shot classification model by loading the 'laion/CLIP-ViT-B-16-laion2B-s34B-b88K' model using the pipeline function.\\n3. The model is capable of zero-shot classification, meaning it can make predictions for examples that don't match any of the examples in the training data.\\n4. Use the model to classify the person's age as an adult, which can be used to control the smart security system.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='laion/CLIP-ViT-B-16-laion2B-s34B-b88K')\\nresult = classifier(image, labels=['adult', 'child'])\\n\"", "answer_id": "QfJg7zYDpPfbrh5nsLMmXZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 651, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', model='fxmarty/resnet-tiny-beans')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create an image classification pipeline using the 'fxmarty/resnet-tiny-beans' model, which has been trained specifically for classifying bean crops.\\n3. The created pipeline can be used to classify images of bean crop leaves, by providing an image as input, and it will return the predicted disease class.\\n4. This program can help farmers detect diseases in their bean crops and take appropriate action to minimize crop loss.\\n<<>>: from transformers import pipeline\\nleaf_disease_classifier = pipeline('image-classification', model='fxmarty/resnet-tiny-beans')\\nclassification_result = leaf_disease_classifier('path/to/leaf_image.jpg')\"", "answer_id": "LfizaiGDkK8NTPnZcSSFmB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 652, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-ViT-B-16-laion2B-s34B-b88K')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an image-classification model.\\n3. Specify the model 'laion/CLIP-ViT-B-16-laion2B-s34B-b88K' to be loaded. This model is a CLIP model trained on a large dataset for zero-shot image classification tasks, making it suitable for recognizing and identifying objects in household items.\\n4. The created classifier can be used to classify images of household items into various categories like furniture, electronics, and ornaments.\\n5. Provide the image path and class names to the classifier to identify the objects in the picture.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-ViT-B-16-laion2B-s34B-b88K')\\nclass_names = ['furniture', 'electronics', 'ornaments']\\n# categories to classify household items\\nresult = image_classifier(path='path/to/image.jpg', class_names=class_names)\\n\"", "answer_id": "N6jyXCdpez75RxRKUFzpQi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 653, "text": "<<>>: Computer Vision Object Detection\\n<<>>: yolov5.load('keremberke/yolov5s-license-plate')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install and import the required library 'yolov5'.\\n2. Load the pre-trained YOLOv5-based license plate detection model 'keremberke/yolov5s-license-plate' using the yolov5.load() function.\\n3. Configure the model by setting the confidence threshold, Intersection over Union (IoU) threshold, and other options.\\n4. Use the loaded model to detect license plates in the given car images by providing the image URL or a local image path.\\n5. Retrieve the detected license plate coordinates and generate a list of bounding boxes around the plates.\\n6. Optionally, use the render_result() function to visualize the detection results.\\n<<>>: import yolov5\\nmodel = yolov5.load('keremberke/yolov5s-license-plate')\\nmodel.conf = 0.25\\nmodel.iou = 0.45\\nmodel.agnostic = False\\nmodel.multi_label = False\\nmodel.max_det = 1000\\nimg = 'https://example.com/car_image.jpg'\\nresults = model(img, size=640, augment=True)\\npredictions = results.pred[0]\\nboxes = predictions[:, :4]\\nlabels = predictions[:, 4]\\nresults.show()\\n\"", "answer_id": "oHKkxXZePceAQGiTP2cA4P", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 654, "text": "1. Import the required library, which is 'requests', 'Image' from 'PIL', and 'Image_processing' from 'transformers'.\\n2. Use the 'Image_processing' library to load the 'hiking-eye/socialpav-hiking-eye-2.0' model, which is specialized for identifying objects related to outdoor activities in images.\\n3. Use the loaded model to perform image manipulation and processing, including resizing, normalization, and converting the image to a format suitable for the model.\\n4. Feed the processed image into the model, and the model will return the predicted locations of various objects in the image.\\n5. Use the model outputs to identify objects in the image and provide relevant features to the social media app for outdoor enthusiasts.\\n### Code:\\n```python\\nimport requests\\nfrom PIL import Image\\nfrom transformers import ImageProcessor, AutoFeatureExtractor, AutoModelForImageClassification\\nimage_url = 'https://path_to_your_image.jpg'\\nimage = Image.open(requests.get(image_url, stream=True).raw)\\nprocessor = ImageProcessor.from_pretrained('hiking-eye/socialpav-hiking-eye-2.0')\\nmodel = AutoModelForImageClassification.from_pretrained('hiking-eye/socialpav-hiking-eye-2.0')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\nbest_labels = logits.argmax(-1).squeeze().tolist()\\nobject_identifications = processor.post_process_object_identification(outputs, logits.shape[0], template_mask_size=model.config.image_resolution)\\n```\"", "answer_id": "5Mx6eGJexheKuAMjgGgZTh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 655, "text": "\\n<<>>: Computer Vision Image Segmentation\\n<<>>: SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries such as SegformerFeatureExtractor, SegformerForSemanticSegmentation, Image, and requests.\\n2. Load the Segformer feature extractor and model using the from_pretrained methods.\\n3. Fetch the image using the Image.open() function with the URL you want. You can use any image URL here.\\n4. Use the feature_extractor to preprocess the image and create a tensor.\\n5. Pass the processed tensor to the model for semantic segmentation.\\n6. The output will contain logits for each class, which can then be used to reconstruct the segmented image.\\n<<>>: from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = SegformerFeatureExtractor.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512')\\nmodel = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512')\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\n\"", "answer_id": "SfsMeNiR2PJf7nq8ucYB96", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 656, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-printed')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required libraries, which include TrOCRProcessor and VisionEncoderDecoderModel from transformers, Image from PIL, and requests.\\n2. Load the pretrained model 'microsoft/trocr-large-printed' using VisionEncoderDecoderModel.from_pretrained().\\n3. Load the image to be analyzed from a URL using the Image module from the PIL library and requests.\\n4. Convert the image into pixel values using the TrOCRProcessor.from_pretrained() method.\\n5. Use the pretrained model to perform optical character recognition (OCR) on the pixel values.\\n6. Extract the textual information from the generated IDs using the processor's batch_decode() method.\\n<<>>: from transformers import TrOCRProcessor, VisionEncoderDecoderModel\\nfrom PIL import Image\\nimport requests\\nurl = 'https://example.com/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw).convert('RGB')\\nprocessor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-printed')\\nmodel = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-printed')\\npixel_values = processor(images=image, return_tensors='pt').pixel_values\\ngenerated_ids = model.generate(pixel_values)\\ngenerated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\"", "answer_id": "Gxm8NMCNwUunydED8kH6oE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 657, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the necessary classes and packages, including the Mask2FormerForUniversalSegmentation model, Image, and torch.\\n2. Load the pre-trained Mask2Former model for instance segmentation using the from_pretrained method with the model name 'facebook/mask2former-swin-tiny-coco-instance'.\\n3. Open the aerial photograph (image) to process.\\n4. Use the AutoImageProcessor to prepare the image for segmentation and process it using the Mask2Former model.\\n5. Post-process the instance segmentation results to obtain the predicted segmentation masks for streets, buildings, and trees.\\n<<>>: from transformers import Mask2FormerForUniversalSegmentation, AutoImageProcessor\\nfrom PIL import Image\\nimport requests\\nimport torch\\nprocessor = AutoImageProcessor.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-tiny-coco-instance')\\nurl = 'https://your_aerial_photo_url.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = processor(images=image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\nresult = processor.post_process_instance_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_instance_map = result['segmentation']\"", "answer_id": "KGLqDHEZSgcvPNYCt7N5du", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 658, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-large-ade')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries, including MaskFormerImageProcessor and MaskFormerForInstanceSegmentation from the transformers package, and Image from the PIL package.\\n2. Load the pretrained MaskFormer model using the from_pretrained method.\\n3. Create a processor object by calling the from_pretrained method on the MaskFormerImageProcessor class.\\n4. Define the image to be processed.\\n5. Preprocess the image using the processor object with the 'return_tensors' parameter set to 'pt'.\\n6. Run the model on the preprocessed image inputs and obtain the outputs.\\n7. Post-process the outputs using the processor object, which returns the segmentation masks.\\n<<>>: from transformers import MaskFormerImageProcessor, MaskFormerForInstanceSegmentation\\nfrom PIL import Image\\nimage = Image.open('bird_image.jpg')\\n# Replace 'bird_image.jpg' with the path to your image\\nprocessor = MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-large-ade')\\ninputs = processor(images=image, return_tensors='pt')\\nmodel = MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-large-ade')\\noutputs = model(**inputs)\\npredicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\n\"", "answer_id": "nAQ68s2kcG4CkbqGAP9r4N", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 659, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: DPTForDepthEstimation.from_pretrained('Intel/dpt-large')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'DPTForDepthEstimation' from transformers and 'torch'.\\n2. Load the pre-trained depth estimation model 'Intel/dpt-large'.\\n3. Use the loaded model to estimate the depth of each frame in the movie scene. This will help determine the distance between the objects in the scene.\\n4. The depth estimation can be used to create a realistic and immersive viewing experience for the viewer.\\n<<>>: from transformers import DPTForDepthEstimation\\nimport torch\\nmodel = DPTForDepthEstimation.from_pretrained('Intel/dpt-large')\\n# Process each frame in the movie scene\\nfor frame_index in range(0, len(movie_scene)): # assuming movie_scene is a list of frames\\n inputs = feature_extractor(images=frame_index, return_tensors='pt')\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\n # Perform additional processing if needed\\n\"", "answer_id": "BYRVFbhikHrgczM2jHYb68", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 660, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-normal')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries and modules, such as PIL, diffusers, numpy, and torch.\\n2. Load the pre-trained ControlNet model using 'lllyasviel/sd-controlnet-normal' checkpoint from Hugging Face.\\n3. The image acquired from the drone can be loaded and preprocessed using the Image module from the PIL library.\\n4. The normalize_image method can be used to resize and adjust the brightness of the image, making it suitable for the model input.\\n5. Create a diffusion pipeline using the StableDiffusionControlNetPipeline and specify the pre-trained ControlNet model.\\n6. Set the pipeline scheduler using UniPCMultistepScheduler.\\n7. Enable memory-efficient attention and CPU offloading for the model.\\n8. Use the pipeline to process the pre-processed image, and save the resulting image to a file.\\n9. The processed image will have enhanced clarity and sharpness, which can be utilized for navigation purposes.\\n<<>>: from PIL import Image\\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\\nimport torch\\nimport numpy as np\\nfrom diffusers.utils import load_image\\nimage = load_image('drone_image_path.png')\\nimage = np.array(image)\\nimage = image.resize((512, 512))\\nimage = image.normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))\\nimage = Image.fromarray(image)\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-normal', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nimage_output = pipe(image, num_inference_steps=20).images[0]\\nimage_output.save('processed_drone_image.png')\"", "answer_id": "oAZLB9SfuzPUeLrbCpefYY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 661, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-church-256')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the 'diffusers' package, which contains the necessary classes and methods to work with Denoising Diffusion Probabilistic Models (DDPM).\\n2. Import the DDPMPipeline class from the diffusers package.\\n3. Load the pre-trained DDPM model with the model id 'google/ddpm-church-256', which is specifically trained to generate high-quality images of realistic-looking churches.\\n4. Generate a new image using the loaded model by calling the generate() method.\\n5. Save the generated image to a file with the given name, 'ddpm_generated_image.png'.\\n<<>>: !pip install diffusers\\nfrom diffusers import DDPMPipeline\\nmodel_id = 'google/ddpm-church-256'\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\\n\"", "answer_id": "bQGAsCqtzixLJgRPphqdJP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 662, "text": "DiffusionPipeline.from_pretrained('google/ncsnpp-ffhq-256')\\n\"", "answer_id": "8mccnHy2w8P7eEGmW5cSZJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 663, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('dreamlike-art/dreamlike-diffusion-1.0', torch_dtype=torch.float16)(prompt).images[0]\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, including StableDiffusionPipeline from the diffusers package and PIL.Image for handling the image file.\\n2. Load the pre-trained model 'dreamlike-art/dreamlike-diffusion-1.0' using the from_pretrained method of the StableDiffusionPipeline class.\\n3. Set the prompt to describe the desired image characteristics (e.g., \\\"vintage, high quality, and nostalgic\\\").\\n4. Generate the image using the model and the prompt. The generated image is converted to an RGB format and saved as a file.\\n5. Use this pipeline to generate the desired high-quality, nostalgia-inducing image for your magazine cover.\\n<<>>: from diffusers import StableDiffusionPipeline\\nfrom PIL import Image\\nimport torch\\nmodel_id = 'dreamlike-art/dreamlike-diffusion-1.0'\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\nprompt = 'vintage, high quality, and nostalgic'\\nimage = pipe(prompt).images[0]\\nimage.save('magazine_cover_image.jpg')\"", "answer_id": "QjnVd8rx7syp6rzs4opv84", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 664, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries from the transformers package, including VideoMAEImageProcessor and VideoMAEForVideoClassification.\\n2. Load the pre-trained model 'MCG-NJU/videomae-small-finetuned-ssv2' using the from_pretrained method of the VideoMAEForVideoClassification class.\\n3. Create an instance of the VideoMAEImageProcessor class, which is used to preprocess the video for the model.\\n4. Process the video frames using the processor and retrieve the inputs required for the model.\\n5. Run the model on the processed inputs and retrieve the predicted class index.\\n6. Finally, obtain the class label by indexing the model.config.id2label dictionary with the predicted class index.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224)) # Replace this line with the video frames\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-small-finetuned-ssv2')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-ssv2')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "5c5Voogza5rkoHpXvvgkyr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 665, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including the VideoMAEImageProcessor and VideoMAEForPreTraining classes from the transformers package.\\n2. Load the pre-trained VideoMAE model ('MCG-NJU/videomae-base-short') with the from_pretrained method. This model is designed for video classification tasks.\\n3. Create an instance of the VideoMAEImageProcessor to pre-process the video frames before they are fed to the model.\\n4. Process the video frames and pass them to the VideoMAE model. The model will then output classification scores for different action categories.\\n5. Analyze the model's output to identify the most likely action for each video frame.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForPreTraining\\nimport numpy as np\\nimport torch\\nnum_frames = 16\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-short')\\nmodel = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short')\\npixel_values = processor(video, return_tensors='pt').pixel_values\\nnum_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2\\nseq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame\\nbool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool()\\noutputs = model(pixel_values, bool_masked_pos=bool_masked_pos)\\n# Perform action classification on the model output\\n\"", "answer_id": "eudG7Eg3ifpDHjGs5nfUdL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 666, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required library 'pipeline' from the transformers package.\\n2. Create an image classification pipeline using the 'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup' model.\\n3. This model is trained for zero-shot image classification, which means it can classify images into categories it has not been explicitly trained on.\\n4. Use the created classifier to classify the given images into various categories like cats, dogs, birds, and more.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup')\\nresult = image_classifier(image_path, class_names=['cat', 'dog', 'bird', 'fish'])\\n\"", "answer_id": "iuhwhe8aQA23GpAL6CfyS6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 667, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', model='julien-c/julien-c-v1-0')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an image classification model.\\n3. Specify the model 'julien-c/julien-c-v1-0' to be loaded. This model is trained to classify images based on their content.\\n4. The created classifier can be used to predict the category of the image, which in this case is a smartphone brand.\\n5. We predict the image contains a high luxury level.\\n<<>>: from transformers import pipeline\\nfrom PIL import Image\\nimage_classifier = pipeline('image-classification', model='julien-c/julien-c-v1-0')\\nimage = Image.open('path_to_image.jpg')\\nprediction = image_classifier(image)\\n\"", "answer_id": "KnpFfJ6yKDCyA8LyqbNf5b", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 668, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTForImageClassification.from_pretrained('google/vit-base-patch16-384')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including ViTFeatureExtractor, ViTForImageClassification, Image, and requests.\\n2. Use the ViTFeatureExtractor.from_pretrained() method to load the pretrained feature extractor.\\n3. Use the ViTForImageClassification.from_pretrained() method to load the pretrained image classification model: 'google/vit-base-patch16-384'.\\n4. Open the image file using the Image.open() method from the PIL library.\\n5. Use the feature_extractor to convert the image into a format suitable for the model.\\n6. Pass the inputs to the model and obtain the outputs, which will be logits representing the probability of the image belonging to each category.\\n7. Select the category with the highest probability as the predicted class for the image.\\n<<>>: from transformers import ViTFeatureExtractor, ViTForImageClassification\\nfrom PIL import Image\\nimport requests\\nurl = 'https://your-image-url.com/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-384')\\nmodel = ViTForImageClassification.from_pretrained('google/vit-base-patch16-384')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "fQ3PZcZYwupEp7dJNixSSw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 669, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: CLIPModel.from_pretrained('flax-community/clip-rsicd-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes and functions from the PIL, transformers, and requests libraries.\\n2. Define the CLIP model using the provided model name 'flax-community/clip-rsicd-v2'.\\n3. Load the image from the provided URL and convert it to an RGB image using the PIL library.\\n4. Define city_labels as a list of city names that you want to geolocalize the image.\\n5. Create a hypothesis_template using the model's prefixes.\\n6. Use the model to generate logits_per_image, which are the probabilities for each city.\\n7. Convert logits_per_image to probabilities and print the result.\\n<<>>: from PIL import Image\\nimport requests\\nfrom transformers import CLIPProcessor, CLIPModel\\nmodel = CLIPModel.from_pretrained('flax-community/clip-rsicd-v2')\\nprocessor = CLIPProcessor.from_pretrained('flax-community/clip-rsicd-v2')\\nurl = 'https://example.com/image.jpg' # Replace with the image URL\\nimage = Image.open(requests.get(url, stream=True).raw).convert('RGB')\\nlabels = ['San Jose', 'San Diego', 'Los Angeles', 'Las Vegas', 'San Francisco']\\nhypothesis_template = \\\"I see a {}.\\\"\\nimage_features = processor(images=image, return_tensors='pt', padding=True)\\nlogits_per_image = model(hypothesis_template, image_features).logits_per_image\\nprobs = logits_per_image.softmax(dim=-1).tolist()[0]\\nprint(probs)\\n\"", "answer_id": "CFnvasroL2b7frzET8Y436", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 670, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vitb16')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers and PIL packages. This includes ViTFeatureExtractor and ViTModel for the image classification model and Image for processing image data.\\n2. Load the pre-trained model 'facebook/dino-vitb16' using the from_pretrained method of the ViTModel class. This model has been trained on a large collection of images and can be used for automatic classification without retraining.\\n3. Load the product image data from a file or use the Image.open() method to load the image from a URL.\\n4. Extract features from the image using the ViTFeatureExtractor class.\\n5. Finally, pass the extracted features into the pre-trained model to obtain the classification results.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimage = Image.open('product_image_path.jpg')\\n# replace 'product_image_path.jpg' with path to your image\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vitb16')\\nmodel = ViTModel.from_pretrained('facebook/dino-vitb16')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\"", "answer_id": "kc8SumjajJtHLXcof4ebzC", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 671, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which is 'finiteautomata/beto-sentiment-analysis'.\\n3. This model is trained for sentiment analysis on Twitter data, and it can classify text into positive, negative, or neutral sentiment categories.\\n4. We can use the loaded model to analyze the sentiment of the customer support messages to determine if they are happy or unhappy with the telecommunication services.\\n5. The model will output the sentiment classification for the given message text.\\n<<>>: from transformers import pipeline\\nsentiment_analyzer = pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\\nmessage_text = \\\"I am very happy with the telecommunication services provided by your company.\\\"\\nresult = sentiment_analyzer(message_text)\\n\"", "answer_id": "Aj6TwKSNYWDXgViKdUZJND", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 672, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L-2-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'AutoTokenizer' and 'AutoModelForSequenceClassification' from transformers.\\n2. Load the pre-trained model using 'AutoModelForSequenceClassification.from_pretrained()' and the provided model name 'cross-encoder/ms-marco-TinyBERT-L-2-v2'.\\n3. Also, load the tokenizer using 'AutoTokenizer.from_pretrained()' with the same model name.\\n4. Tokenize and create tensors for the question and candidate passages using the tokenizer's 'encode()' method.\\n5. Pass the tensors through the model to get logits, which indicate the relevancy of each passage to the question.\\n6. Sort the candidate passages based on their logits in descending order to get the most relevant passage for the given question.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSequenceClassification\\nimport torch\\nmodel = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-TinyBERT-L-2-v2')\\ntokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-TinyBERT-L-2-v2')\\nquestion = \\\"What is the main use of renewable energy sources?\\\"\\ncandidate_passages = [\\\"Renewable energy sources are becoming more prevalent due to their environmental benefits.\\\", \\\"Non-renewable energy sources are depleting at a rapid rate.\\\"]\\nfeatures = tokenizer([question] * len(candidate_passages), candidate_passages, padding=True, truncation=True, return_tensors='pt')\\nwith torch.no_grad():\\n scores = model(**features).logits\\n sorted_idx = torch.argsort(scores, descending=True)\\ntop_passage = candidate_passages[sorted_idx[0]]\"", "answer_id": "MiFwcFW7bAkvVa85YpD9dd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 673, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='martin-ha/toxic-comment-model')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'martin-ha/toxic-comment-model'.\\n3. The loaded model is a fine-tuned DistilBERT model trained specifically to identify toxic comments on forums and online communities.\\n4. The model can be used to classify forum comments into different categories, such as 'toxic' or 'not toxic', and flag any comments that are identified as toxic.\\n5. As the community manager, you can review and address these flagged comments as needed.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('text-classification', model='martin-ha/toxic-comment-model')\\ncomment = \\\"This is a test text.\\\"\\nresult = classifier(comment)\\n\"", "answer_id": "kZ5czR7EmP3tnHJkVxxehr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 674, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the SentenceTransformer class from the sentence_transformers library.\\n2. Initialize the model by providing the model name 'sentence-transformers/multi-qa-MiniLM-L6-cos-v1'.\\n3. The model will be used for mapping input sentences to dense 384-dimensional vectors. These vectors can then be compared using cosine similarity to rank the relevance of text passages to a given keyword.\\n4. Encode the text passage and the provided keywords (i.e., search terms) using the model.\\n5. Calculate the similarity scores between the encoded text passage and the keywords.\\n6. Rank the text passage based on its similarity scores. The highest scoring passages are most likely to contain relevant information regarding the given keyword.\\n<<>>: from sentence_transformers import SentenceTransformer\\nmodel = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')\\ntext_passage = \\\"Text passage to rank based on keywords...\\\"\\nsearch_terms = [\\\"keyword1\\\", \\\"keyword2\\\", \\\"keyword3\\\"] # List of keywords to rank by\\nembeddings = model.encode([search_terms] + [text_passage])\\n\"", "answer_id": "TnSNUooxW39Q96pSKTPvhS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 675, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base', return_all_scores=True)\\n<<>>: Transformers\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We then create a text classification model using the pipeline function, specifying the model as 'j-hartmann/emotion-english-distilroberta-base'. This is a model trained to classify emotions in English text based on Ekman's 6 basic emotions and a neutral class: anger, disgust, fear, joy, neutral, sadness, and surprise.\\n3. We set the return_all_scores flag to True, which returns the scores for all emotions, not just the most likely emotion.\\n4. We pass the movie review as input to the classifier, and it will identify the type of emotion in the text.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base', return_all_scores=True)\\nresult = classifier('I love this movie! The acting was amazing and the story was captivating.')\\n\"", "answer_id": "QeqtrmcbmHyDkitnZkPaPa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 676, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('d4data/biomedical-ner-all')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, including AutoTokenizer and AutoModelForTokenClassification.\\n2. Load the pre-trained NER model 'd4data/biomedical-ner-all', which is designed for recognizing biomedical entities in text.\\n3. Load the corresponding tokenizer for this model using AutoTokenizer.from_pretrained().\\n4. Create an NLP pipeline instance for token classification, specifying the model and tokenizer.\\n5. Use the created pipeline to process case reports, identifying and labeling biomedical entities within the text.\\n<<>>: from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification\\nmodel = AutoModelForTokenClassification.from_pretrained('d4data/biomedical-ner-all')\\ntokenizer = AutoTokenizer.from_pretrained('d4data/biomedical-ner-all')\\nbiomedical_ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy='simple')\\nanalyzed_case_report = biomedical_ner(\\\"Case report text here...\\\")\"", "answer_id": "7RkCzrjK4dvDShMbzcyVJf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 677, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('dslim/bert-base-NER')\\n<<>>: Transformers\\n<<>>: 1. Import the required classes AutoTokenizer and AutoModelForTokenClassification from the transformers library.\\n2. Load the pre-trained BERT model for named entity recognition (NER) using the from_pretrained method with the model name 'dslim/bert-base-NER'.\\n3. Create a tokenizer using the AutoTokenizer.from_pretrained() method and model name to tokenize the input text.\\n4. Process the input text (customer reviews) using the tokenizer and model to extract named entities, specifically company names.\\n5. Filter the extracted entities to keep only those that are associated with the 'company' concept.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification\\ntokenizer = AutoTokenizer.from_pretrained('dslim/bert-base-NER')\\nmodel = AutoModelForTokenClassification.from_pretrained('dslim/bert-base-NER')\\ninput_text = \\\"Customer reviews text here...\\\"\\ninput_tokens = tokenizer(input_text, return_tensors='pt')\\noutput = model(**input_tokens)\\ncompany_entities = extract_entities_from_output(output, 'company')\\n\"", "answer_id": "fVjWh95rZZtoXjn75WSc6a", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 678, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModel.from_pretrained('ckiplab/bert-base-chinese-ws')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary functions from the transformers library, which include BertTokenizerFast and AutoModel.\\n2. Load the pre-trained tokenizer 'bert-base-chinese' for token classification and modeling using BertTokenizerFast.from_pretrained().\\n3. Load the pre-trained model 'ckiplab/bert-base-chinese-ws' for word segmentation using AutoModel.from_pretrained().\\n4. Use the tokenizer to tokenize the input Chinese text and convert it into tokens for further processing.\\n<<>>: from transformers import BertTokenizerFast, AutoModel\\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')\\nmodel = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ws')\\ninput_text = '\\u4f60\\u597d\\uff0c\\u6211\\u4eec\\u697d\\u662f\\u6211\\u7684\\u597d\\u8fdb\\u662f'\\ntokens = tokenizer.tokenize(input_text)\\n\"", "answer_id": "cmcyjnMnP25k6kA6ivqoCz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 679, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, including AutoTokenizer and AutoModelForTokenClassification.\\n2. Load the tokenizer and pretrained model 'Davlan/distilbert-base-multilingual-cased-ner-hrl' using the from_pretrained method. This model is specifically designed for Named Entity Recognition (NER) tasks and can extract well-known named entities such as person names, locations, and organizations.\\n3. Use the tokenizer to preprocess the input text, turning it into a format that can be processed by the model.\\n4. The model will then analyze the text and identify the named entities present in the text.\\n5. Post-process the model's output to obtain the final NER tags for each identified entity.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nmodel = AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nnlp = pipeline('ner', model=model, tokenizer=tokenizer)\\nner_results = nlp(news_article)\\n\"", "answer_id": "aJGaQuqKPL5MVAyQCDGdfM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 680, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\n<<>>: PyTorch Transformers\\n<<>>: 1. Import required libraries TapexTokenizer and BartForConditionalGeneration from the transformers library provided by Hugging Face.\\n2. Load the tokenizer and model, using the 'microsoft/tapex-large-finetuned-wikisql' pre-trained model.\\n3. Define the table, which contains the list of Olympic Game host cities and their corresponding years.\\n4. Provide the query, which asks for the year the games were held in Beijing.\\n5. Tokenize the table and query using the tokenizer and create the input tensor.\\n6. Generate the output using the model and the input tensor.\\n7. Decode the output to obtain the year the Olympic Games were held in Beijing.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wikisql')\\ntable = [\\n ['Year', 'City', 'Country'],\\n ['2004', 'Athens', 'Greece'],\\n ['2008', 'Beijing', 'China'],\\n ['2012', 'London', 'UK'],\\n ['2016', 'Rio de Janeiro', 'Brazil'],\\n]\\nquery = 'SELECT Year WHERE City = \\'Beijing\\''\\ninputs = tokenizer(table=table, queries=query, return_tensors='pt')\\noutputs = model.generate(**inputs)\\nprint(tokenizer.batch_decode(outputs, skip_special_tokens=True))\\n\"", "answer_id": "cP94f6pRTaU6QoQSax6KKc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 681, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-base-finetuned-wtq')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers and pandas packages. This includes TapexTokenizer, BartForConditionalGeneration for the table question answering model.\\n2. Load the data from your CSV file using pandas read_csv function.\\n3. Define the query, which is the question you want to ask about the table.\\n4. Encode the table and query using the TapexTokenizer.\\n5. Generate the answer using the BartForConditionalGeneration model.\\n6. Decode the generated answer using the tokenizer's batch_decode function.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-base-finetuned-wtq')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-base-finetuned-wtq')\\ndata = pd.read_csv('path/to/your/csv/file')\\nquery = \\\"What is the best bard's magical ability?\\\"\\nencoding = tokenizer(table=data, query=query, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nanswer = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\n\"", "answer_id": "ZLa3krKFLJxhcYgHGKZWLN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 682, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='bert-large-cased-whole-word-masking-finetuned-squad')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model.\\n3. Specify the model 'bert-large-cased-whole-word-masking-finetuned-squad' to be loaded. This model is trained on the SQuAD dataset and is capable of answering questions based on a given context.\\n4. The created question-answering model can be used to provide answers to questions about historical facts by providing the context and question as input.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='bert-large-cased-whole-word-masking-finetuned-squad')\\ncontext = 'The historical fact is a context.'\\nquestion = 'What is the historical fact?'\\nanswer = qa_pipeline({'context': context, 'question': question})\\nprint(answer)\"", "answer_id": "TLByvgJMXu6wHJUEiEStM4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 683, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('ahotrod/electra_large_discriminator_squad2_512')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline', 'AutoTokenizer', and 'AutoModelForQuestionAnswering' from transformers.\\n2. Load the pretrained model 'ahotrod/electra_large_discriminator_squad2_512' using the 'AutoModelForQuestionAnswering.from_pretrained' method.\\n3. Load the tokenizer using 'AutoTokenizer.from_pretrained'.\\n4. Use the 'pipeline' function to create a question-answering pipeline with the model and tokenizer.\\n5. Provide the context and question to the pipeline, and it will generate an answer based on the provided context.\\n<<>>: from transformers import pipeline, AutoTokenizer, AutoModelForQuestionAnswering\\ntokenizer = AutoTokenizer.from_pretrained('ahotrod/electra_large_discriminator_squad2_512')\\nmodel = AutoModelForQuestionAnswering.from_pretrained('ahotrod/electra_large_discriminator_squad2_512')\\nQA_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer)\\ncontext = \\\"This is a context example.\\\"\\nquestion = \\\"What is this example for?\\\"\\nanswer = QA_pipeline({'context': context, 'question': question})\"", "answer_id": "EYQTU4tRpk569zNBj7gGjg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 684, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/roberta-base-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face, including AutoModelForQuestionAnswering, AutoTokenizer, and pipeline.\\n2. Load the pre-trained model 'deepset/roberta-base-squad2' using the from_pretrained method of the AutoModelForQuestionAnswering class. This model is trained on the SQuAD 2.0 dataset and is designed for question answering tasks.\\n3. Use the pipeline function to create a question-answering pipeline with the loaded model and tokenizer.\\n4. To use the pipeline, provide it with a question and a context (the text passage). The model will then analyze the context and try to provide an appropriate answer to the question.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel_name = 'deepset/roberta-base-squad2'\\nnlp = pipeline('question-answering', model=model_name, tokenizer=model_name)\\nQA_input = {\\n 'question': 'Why is model conversion important?',\\n 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'\\n}\\nres = nlp(QA_input)\\n\"", "answer_id": "TrhA3JjMCh3Ryd57oAqUbb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 685, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/deberta-v3-large-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required classes AutoModelForQuestionAnswering and AutoTokenizer from the transformers library.\\n2. Load the pre-trained model 'deepset/deberta-v3-large-squad2' using the AutoModelForQuestionAnswering class. This model specializes in question-answering tasks and is trained on the SQuAD 2.0 dataset.\\n3. Load the tokenizer for the model using AutoTokenizer.\\n4. The loaded model and tokenizer can now be used to extract answers from the given knowledge base text using the model's 'qa' method.\\n5. The extracted answer can be used to enhance the FAQ bot.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer\\nmodel = AutoModelForQuestionAnswering.from_pretrained('deepset/deberta-v3-large-squad2')\\ntokenizer = AutoTokenizer.from_pretrained('deepset/deberta-v3-large-squad2')\\ntext = \\\"\\\"\\nquestion = \\\"What is the main difference between renewable and non-renewable energy sources?\\\"\\ninputs = tokenizer(question, text, return_tensors='pt')\\noutputs = model(**inputs)\\nanswer_start = outputs.start_logits.argmax().item()\\nanswer_end = outputs.end_logits.argmax().item()\\nanswer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_start:answer_end+1]))\\n\"", "answer_id": "GRd2WFmKWjjDyGvcKfgYUu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 686, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='Recognai/bert-base-spanish-wwm-cased-xnli')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers package provided by Hugging Face.\\n2. Use the pipeline function to create a zero-shot classification model, which can categorize text into predefined categories even if it has not been explicitly trained on those categories.\\n3. Specify the model 'Recognai/bert-base-spanish-wwm-cased-xnli', which has been trained on Spanish text and is fine-tuned with the help of the Spanish National Language Institute (xnli) for zero-shot classification tasks.\\n4. The created classifier can then be used to classify customer reviews into different categories like 'travel', 'cooking', and 'dancing'.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='Recognai/bert-base-spanish-wwm-cased-xnli')\\nreview = \\\"Este viaje fue fant\\u00e1stico. La gente es muy amable y hospitalario.\\\"\\ncategories = ['viajes', 'cocina', 'danza']\\nresult = classifier(review, categories)\"", "answer_id": "bXPdtWFJvWa4qaukrZDydg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 687, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='typeform/distilbert-base-uncased-mnli')\\n<<>>: Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Create a classifier using the 'pipeline' function from transformers, specifying the model as 'typeform/distilbert-base-uncased-mnli'.\\n3. The classifier function can be called with the given text and a list of candidate categories. It will return the most likely category based on the text.\\n4. The category with the highest probability can be used to recommend suitable entertainment options.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='typeform/distilbert-base-uncased-mnli')\\ntext = \\\"I'm going for a run today after work.\\\"\\ncategories = ['sports', 'movies', 'tv', 'video games']\\nresult = classifier(text, categories)\\nentertainment_category = result['labels'][0]\"", "answer_id": "QAy5diWPJ7aMFwpmEQq29W", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 688, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: CrossEncoder('cross-encoder/nli-distilroberta-base')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries and functions, including CrossEncoder and the 'cross-encoder/nli-distilroberta-base' model from Transformers.\\n2. Use the CrossEncoder function to load the aforementioned model.\\n3. Use the created classifier to evaluate the consistency of the provided summary by comparing it with the original book's text.\\n4. If the score for the contradiction relationship is higher than the entailment relationship, it means the summary might contain conflicting information.\\n<<>>: from sentence_transformers import CrossEncoder\\nmodel = CrossEncoder('cross-encoder/nli-distilroberta-base')\\nsummary_text = \\\"The book's plot summary goes here...\\\"\\noriginal_text = \\\"The book's original text goes here...\\\"\\nscores = model.predict([(summary_text, original_text)])\\nconflict_score = scores[0][1]\"", "answer_id": "iXWQwnCmwLLQstH9c8uoQA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 689, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: CrossEncoder('cross-encoder/nli-distilroberta-base')\\n<<>>: Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes CrossEncoder for the zero-shot classification model.\\n2. We then use the CrossEncoder class to create a model instance with the specified pre-trained model 'cross-encoder/nli-distilroberta-base'. This model has been trained on natural language inference tasks and can classify text into different categories based on the semantic similarities.\\n3. We can then use the model to classify the given user comment into different labels, such as 'positive' and 'negative', to determine the sentiment of the comment.\\n4. The model will provide scores for each label, and we can select the label with the highest score as the predicted sentiment.\\n<<>>: from transformers import CrossEncoder\\nclassifier = CrossEncoder('cross-encoder/nli-distilroberta-base')\\ncomment = \\\"I recently purchased this product and it completely exceeded my expectations! The build quality is top-notch, and I've already recommended it to several friends.\\\"\\nsentiment_labels = ['positive', 'negative']\\nscores = classifier.predict([(comment, sentiment_label) for sentiment_label in sentiment_labels])\\npredicted_label = sentiment_labels[scores.argmax()]\\n\"", "answer_id": "dbvRCVTyCNFQ7kRQL3maie", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 690, "text": "Studies have shown that owning a dog is good for you. Dog ownership can decrease stress levels, improve mood, and increase physical activity.\"", "answer_id": "kTaHr55kX8N6HHbwFhM8xp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 691, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: PegasusForConditionalGeneration.from_pretrained('google/pegasus-cnn_dailymail')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the PegasusForConditionalGeneration and PegasusTokenizer classes from the transformers library provided by Hugging Face.\\n2. Load the 'google/pegasus-cnn_dailymail' pre-trained model for abstractive text summarization using the from_pretrained method of the PegasusForConditionalGeneration class.\\n3. Create a tokenizer for the model using PegasusTokenizer.from_pretrained().\\n4. Tokenize the input text and generate a summary using the model.generate() method, specifying parameters for the summary length and other generation parameters.\\n5. Decode the generated summary using the tokenizer.batch_decode() method.\\n<<>>: from transformers import PegasusForConditionalGeneration, PegasusTokenizer\\nmodel = PegasusForConditionalGeneration.from_pretrained('google/pegasus-cnn_dailymail')\\ntokenizer = PegasusTokenizer.from_pretrained('google/pegasus-cnn_dailymail')\\ntext = \\\"Long article content goes here...\\\"\\ninputs = tokenizer(text, max_length=2048, return_tensors='pt')\\nsummary_ids = model.generate(inputs['input_ids'])\\nsummary = tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\\n\"", "answer_id": "P2MYeRdU9BfUo2tmmod5RK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 692, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'MBartForConditionalGeneration' and 'MBart50TokenizerFast' from transformers.\\n2. Load the pre-trained model 'facebook/mbart-large-50' and create a tokenizer for the model using 'MBart50TokenizerFast'.\\n3. Tokenize the property description text in English using the tokenizer, then encode the tokens and input them to the model.\\n4. Generate the translated French text using the model and decode the output tokens.\\n5. The translated French text can be used on your real estate website to reach a wider audience.\\n<<>>: from transformers import MBartForConditionalGeneration, MBart50TokenizerFast\\nmodel = MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50')\\ntokenizer = MBart50TokenizerFast.from_pretrained('facebook/mbart-large-50')\\nenglish_description = \\\"This luxurious property features 5 bedrooms, 5 bathrooms, and an expansive garden.\\\"\\ninputs = tokenizer(english_description, return_tensors='pt', padding=True, truncation=True)\\noutputs = model.generate(**inputs)\\nfrench_translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\"", "answer_id": "cFcwFPJzZW8bxZoZoJyqtf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 693, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_ar', model='Helsinki-NLP/opus-mt-en-ar')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the 'pipeline' function from the 'transformers' library.\\n2. Load the English to Arabic translation model 'Helsinki-NLP/opus-mt-en-ar' for translating the given text.\\n3. Use the 'pipeline' function to translate the given text, which is in English, into Arabic.\\n4. The translated text can then be shared with your family to inform them about the upcoming holiday plans.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_ar', model='Helsinki-NLP/opus-mt-en-ar')\\ntext = \\\"My friend is planning a holiday trip for our families. He found a beautiful place with a beach, swimming pool, and a wide range of outdoor activities for kids. There's also a famous seafood restaurant nearby! I think our families will have a great time together.\\\"\\ntranslation = translator(text)\\ntranslated_text = translation[0]['translation_text']\\n\"", "answer_id": "NsDDjMixZkp68va3LgwbJu", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 694, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5Model.from_pretrained('t5-large')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are T5Tokenizer and T5Model from the transformers package.\\n2. Instantiate the tokenizer and the model using the T5Model.from_pretrained() and T5Tokenizer.from_pretrained() method with 't5-large' as the argument.\\n3. Tokenize the customer feedback document using the tokenizer and convert it into input_ids.\\n4. Generate a summary of the document with the model, specifying max_length and num_beams to control the length of the summary and the number of alternative summaries.\\n5. Decode the generated IDs back into text using the tokenizer, creating a summarized version of the customer feedback document.\\n<<>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('t5-large')\\nmodel = T5Model.from_pretrained('t5-large')\\ninput_text = \\\"summarize: \\\" + customer_feedback_document\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutputs = model.generate(input_ids, max_length=100, num_beams=5)\\nsummary = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "bsMjyFgDsZ2qj5KVsYYfjj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 695, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Create a summarizer using the 'pipeline' function, specifying the model 'Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2'.\\n3. Pass the given conversation (with named roles like Anna, Tom, and Sarah) to the summarizer, and it will generate a summary with the main points discussed during the team meeting.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2')\\nconversation = \\\"Anna: In today's meeting, we discussed increasing marketing budget. Tom: I suggested allocating more funds to social media campaigns. Sarah: I proposed focusing on improving SEO. Anna: We agreed on investing in content creation, too. Tom: The team will revise the strategy and present it next week. Sarah: Let's determine new KPIs for evaluating our progress.\\\"\\nsummary = summarizer(conversation, max_length=150)[0]['summary_text']\\n\"", "answer_id": "PCwYMDjpuAxcdMpAAtvj5W", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 696, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='mywateriswet/ShuanBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face. This will be used to create a conversational chatbot model.\\n2. We specify the model 'mywateriswet/ShuanBot' to be loaded. This is a GPT-2 based conversational chatbot model that can engage in dialogue and respond appropriately to user inputs.\\n3. The created chatbot can be used to engage users in conversation and answer their queries while remaining contextually relevant.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='mywateriswet/ShuanBot')\\nresponse = chatbot('What is your favorite color?')\"", "answer_id": "M6ddrmZB4hFHqU4YKdBEVv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 697, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilbert-base-uncased')\\n<<>>: Transformers\\n<<>>: 1. We first import the necessary class from the transformers package. This includes the 'pipeline' function.\\n2. We then use the 'pipeline' function to create a fill-mask pipeline with the 'distilbert-base-uncased' model.\\n3. This fill-mask pipeline allows us to fill in the '' token in a given text.\\n4. We provide the text with a '' token in place of the missing word, and the pipeline will generate possible completions for the masked word.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilbert-base-uncased')\\ntext = \\\"In the story, the antagonist represents the nature of humanity.\\\"\\ncompleted_text = unmasker(text)\\n\"", "answer_id": "dYAczvX7dTJw8T5awSqurk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 698, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='camembert-base', tokenizer='camembert-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a fill-mask model, which is capable of completing sentences with missing words.\\n3. Specify the model 'camembert-base' to be loaded. This model is pretrained on French text and can be used to predict the missing word in a given sentence.\\n4. The created pipeline can be used to provide suggestions for completing the missing word in a given sentence in French.\\n<<>>: from transformers import pipeline\\ncamembert_fill_mask = pipeline('fill-mask', model='camembert-base', tokenizer='camembert-base')\\nresults = camembert_fill_mask('Le camembert est :)')\\n\"", "answer_id": "eyR4UHGG6gZs67aqTEDsEG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 699, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilbert-base-multilingual-cased')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Create a fill-mask pipeline using the 'distilbert-base-multilingual-cased' model. This model is specially designed for handling multiple languages in a single model.\\n3. Provide an example sentence with a missing word, represented by the [MASK] token.\\n4. Run the pipeline on the example sentence to fill in the missing word.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilbert-base-multilingual-cased')\\nunmasker(\\\"Quand je suis all\\u00e9e au [MASK] pour acheter des livres, j'ai rencontr\\u00e9e un ami.\\\")\\n\"", "answer_id": "Mok3U2F5dGDvqSEoSWbRyr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 700, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_hindi_to_french', model='facebook/nllb-200-distilled-600M')\\n<<>>: PyTorch Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a translation pipeline using the 'translation_hindi_to_french' task and the 'facebook/nllb-200-distilled-600M' model.\\n3. This model is designed to translate text from 200 different languages into French. It can handle text in Hindi, which is the input language in this case.\\n4. We provide the Hindi text as input to the translation pipeline, and it will return the French translation.\\n5. The translated text will be in French.\\n<<>>: from transformers import pipeline\\ntranslation_pipeline = pipeline('translation_hindi_to_french', model='facebook/nllb-200-distilled-600M')\\nhindi_text = \\\"\\u0906\\u092a\\u0915\\u0940 \\u092a\\u094d\\u0930\\u0947\\u091c\\u093c\\u091f\\u0947\\u0936\\u0928 \\u0915\\u093e \\u0906\\u0927\\u093e\\u0930 \\u0905\\u091a\\u094d\\u091b\\u093e \\u0925\\u093e, \\u0932\\u0947\\u0915\\u093f\\u0928 \\u0921\\u0947\\u091f\\u093e \\u0935\\u093f\\u0936\\u094d\\u0932\\u0947\\u0937\\u0923 \\u092a\\u0930 \\u0927\\u094d\\u092f\\u093e\\u0928 \\u0926\\u0947\\u0928\\u093e \\u091a\\u093e\\u0939\\u093f\\u090f\\u0964\\\"\\ntranslated_text = translation_pipeline(hindi_text)[0]['translation_text']\"", "answer_id": "Kamied8MVp44QiypNqbyAs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 701, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='dmis-lab/biobert-base-cased-v1.2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Use the pipeline function to create a fill-mask model with the 'dmis-lab/biobert-base-cased-v1.2' pre-trained model.\\n3. The created model will be used to complete the given sentence with the missing word.\\n4. The model is designed for biomedical text mining tasks and can handle clinical text better than general-purpose language models.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='dmis-lab/biobert-base-cased-v1.2')\\nresult = fill_mask(\\\"The patient was admitted with [MASK] shock and needed immediate treatment.\\\")\\n\"", "answer_id": "LFXwzewt39Vt52Aob9NSFW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 702, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='GroNLP/bert-base-dutch-cased')\\n<<>>: Transformers\\n<<>>: 1. We import the necessary function from the transformers library: the 'pipeline' function.\\n2. We create a fill-mask pipeline using the 'GroNLP/bert-base-dutch-cased' model, which is specifically designed for Dutch Language Processing tasks.\\n3. We provide the incomplete sentence to the fill-mask pipeline, with the word [MASK] marking the position where the model should fill in the missing word.\\n4. The model returns the completed sentence with the missing word filled in according to its knowledge of the Dutch language.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='GroNLP/bert-base-dutch-cased')\\nsentence = \\\"Het is vandaag erg koud, dus vergeet niet je [MASK] mee te nemen.\\\"\\nfilled_sentence = fill_mask(sentence)\\n\"", "answer_id": "6GJJTEZ2xJEVwHKJfMYxg2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 703, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='bert-base-cased')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the model 'bert-base-cased'.\\n3. The loaded model will be used for fill-mask tasks, which is to generate a completion for a given sentence with a masked word.\\n4. Provide the sentence with a masked word ('[MASK]') as input and the model will fill in the missing word.\\n5. This can be used to generate a fill-in-the-blank question for students.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='bert-base-cased')\\ninput_sentence = \\\"The cat chased the mouse and then climbed the tree.\\\"\\nfilled_sentence = fill_mask(input_sentence)[0]['sequence']\\nmask_index = filled_sentence.index('[MASK]')\\nfilled_quiz_question = filled_sentence[:mask_index] + '.' + filled_sentence[mask_index + 4:]\"", "answer_id": "YvMm9587DCTBrdcFyjSSvt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 704, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-cos-v1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary libraries, which are SentenceTransformer and util from the sentence-transformers package.\\n2. Load the pre-trained multilingual sentence-transformers/multi-qa-mpnet-base-cos-v1 model that maps sentences to a 768-dimensional dense vector space.\\n3. Encode the customer query and the list of FAQ sentences using the model.\\n4. Compute the similarity scores between the query and each FAQ sentence using the util.dot_score() function.\\n5. Sort the FAQ sentences based on their similarity scores and return the most related FAQ.\\n<<>>: from sentence_transformers import SentenceTransformer, util\\nquery = \\\"How many people work at your company?\\\"\\nfaq_sentences = [\\\"Around 5,000 people work at our company\\\", \\\"We have approximately 3,000 employees\\\"]\\nmodel = SentenceTransformer('sentence-transformers/multi-qa-mpnet-base-cos-v1')\\nquery_emb = model.encode(query)\\nfaq_emb = model.encode(faq_sentences)\\nscores = util.dot_score(query_emb, faq_emb)[0].cpu().tolist()\\nsorted_faq = sorted(zip(faq_sentences, scores), key=lambda x: x[1], reverse=True)\\nbest_faq = sorted_faq[0][0]\\n\"", "answer_id": "eXoPQ7JLTbTR7zyVuYYg6c", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 705, "text": "<<>>: Audio Text-to-Speech\\n<<>>: SpeechT5ForTextToSpeech.from_pretrained('microsoft/speecht5_tts')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library: SpeechT5Processor, SpeechT5ForTextToSpeech, and SpeechT5HifiGan. Additionally, import soundfile as sf to save the generated audio files.\\n2. Load the pre-trained SpeechT5 model and processor as well as the HIFIGAN vocoder using the from_pretrained method.\\n3. Process the email message text with the processor and obtain the input_ids and decoder_input_ids.\\n4. Use the HIFIGAN vocoder to generate a waveform from the input_ids and specify the sample rate.\\n5. Save the generated waveform as an audio file using the sf.write method.\\n6. The resulting audio file can be played back to listen to the message in different voices.\\n<<>>: from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan\\nimport torch\\nimport soundfile as sf\\nmessage = 'Your email message here...'\\nprocessor = SpeechT5Processor.from_pretrained('microsoft/speecht5_tts')\\nmodel = SpeechT5ForTextToSpeech.from_pretrained('microsoft/speecht5_tts')\\nvocoder = SpeechT5HifiGan.from_pretrained('microsoft/speecht5_hifigan')\\ninputs = processor(message, return_tensors='pt')\\nnum_channels = torch.tensor([1, 2, 4]).unsqueeze(0)\\noutputs = model.generate_speech(inputs['input_ids'], num_channels=num_channels, vocoder=vocoder)\\nsf.write('message_speech.wav', outputs[0], samplerate=16000)\"", "answer_id": "CJ7UXrkuKiEfwhvtoJK3PA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 706, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-zh-cv7_css10', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries, including load_model_ensemble_and_task_from_hf_hub from fairseq.checkpoint_utils and TTSHubInterface from fairseq.models.text_to_speech.hub_interface.\\n2. Load the pre-trained model 'facebook/tts_transformer-zh-cv7_css10' using the load_model_ensemble_and_task_from_hf_hub function.\\n3. Update the model configuration using TTSHubInterface.update_cfg_with_data_cfg().\\n4. Build the generator from the task.build_generator() method.\\n5. Provide the Chinese text to be converted into speech.\\n6. Use the TTSHubInterface.get_model_input() function to create the input for the model.\\n7. Use TTSHubInterface.get_prediction() to generate the audio waveform and sample rate.\\n###Code:\\n```python\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/tts_transformer-zh-cv7_css10', arg_overrides={'vocoder': 'hifigan', 'fp16': False})\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = \\\"\\u6625\\u6c5f\\u6f6e\\u6c34\\u8fde\\u6d77\\u5e73\\uff0c\\u6d77\\u4e2d\\u6dd8\\u6211\\u662f\\u6708\\u5171\\u6f6e\\u751f\\\"\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\"", "answer_id": "DeshJ9DUwMFa9bpiKf8xsy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 707, "text": "<<>>: Audio Text-to-Speech\\n<<>>: TextToSpeech('NeuML/ljspeech-jets-onnx')\\n<<>>: ONNX\\n<<>>: 1. Import the necessary libraries and modules, including the 'TextToSpeech' class from the 'transformers' library.\\n2. Load the 'NeuML/ljspeech-jets-onnx' model using the 'TextToSpeech' class. This model is designed to convert text to speech using the ljspeech dataset.\\n3. With the model, we can then call 'text2speech' method to convert the given text to synthesized human-like speech audio.\\n4. Finally, play the generated audio or save it as a file to be used in your AI chatbot.\\n<<>>: from transformers import TextToSpeech\\ntts = TextToSpeech('NeuML/ljspeech-jets-onnx')\\naudio = tts('Hello, how are you?')\\naudio.play()\\n\"", "answer_id": "WatSrJD2iFQi7U338auSQV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 708, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: VoiceActivityDetection(segmentation='pyannote/segmentation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the pyannote.audio package using pip.\\n2. Import the VoiceActivityDetection class from the pyannote.audio.pipelines module.\\n3. Instantiate the VoiceActivityDetection class with a 'pyannote/segmentation' model.\\n4. Set the hyperparameters for the model, such as onset, offset, min_duration_on, and min_duration_off.\\n5. Instantiate the model with the hyperparameters.\\n6. Call the 'instantiate' method on the model to create a pipeline object.\\n7. Use the pipeline to analyze the audio file to detect speech.\\n<<>>: from pyannote.audio.pipelines import VoiceActivityDetection\\npipeline = VoiceActivityDetection(segmentation='pyannote/segmentation')\\nHYPER_PARAMETERS = {\\n 'onset': 0.5,\\n 'offset': 0.5,\\n 'min_duration_on': 0.0,\\n 'min_duration_off': 0.0\\n}\\npipeline.instantiate(HYPER_PARAMETERS)\\nvad = pipeline('audio.wav')\\n\"", "answer_id": "mdscVTK3dHokqLfVDa2cGY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 709, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, including WhisperProcessor and WhisperForConditionalGeneration.\\n2. Load the pre-trained whisper model 'openai/whisper-large' using the from_pretrained method of the WhisperForConditionalGeneration class.\\n3. Load the corresponding processor using the from_pretrained method of the WhisperProcessor class.\\n4. Process your audio recordings using the processor, which will convert them into input features that can be used by the model.\\n5. Use the generate method of the model to create text from the input features.\\n6. Decode the generated text using the processor's batch_decode method, which will convert the text back into human-readable form.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-large')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\n# replace 'audio_file_path' with path to your audio file\\nsample = {'array': 'audio_file_path', 'sampling_rate': 16000}\\ninput_features = processor(sample['array'], sampling_rate=sample['sampling_rate'], return_tensors='pt').input_features\\ngenerated_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(generated_ids, skip_special_tokens=True)\\n\"", "answer_id": "GXi6N5cmXm5GiApPnsn3sb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 710, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-large-960h-lv60-self')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers and torch packages. This includes Wav2Vec2ForCTC for the speech recognition model and Wav2Vec2Processor for pre-processing the audio.\\n2. We then use the from_pretrained method of the Wav2Vec2ForCTC class to load the pre-trained model 'facebook/wav2vec2-large-960h-lv60-self'. This model has been trained for automatic speech recognition tasks and can transcribe spoken language to text.\\n3. We load the customer service audio data from a file or it can be acquired in real-time from the call recording interface.\\n4. We preprocess the audio data using the Wav2Vec2Processor class and then feed it to the model for transcription.\\n5. The transcribed text can be used for further analysis or stored as a file for future reference.\\n<<>>: from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor\\nimport torch\\nprocessor = Wav2Vec2Processor.from_pretrained('facebook/wav2vec2-large-960h-lv60-self')\\nmodel = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-large-960h-lv60-self')\\ninput_values = processor(audio_data, return_tensors='pt', padding='longest').input_values\\nlogits = model(input_values).logits\\npredicted_ids = torch.argmax(logits, dim=-1)\\ntranscription = processor.batch_decode(predicted_ids)\"", "answer_id": "XsisZRhY2H3aoBnM7tkKGF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 711, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: separator.from_hparams(source='speechbrain/sepformer-whamr', savedir='pretrained_models/sepformer-whamr')\\n<<>>: SpeechBrain\\n<<>>: 1. Import the necessary libraries, which are 'SepformerSeparation' from speechbrain.pretrained and 'torchaudio'.\\n2. Load the pre-trained SepFormer model from the SpeechBrain model hub using the 'from_hparams' method and the provided model source and savedir.\\n3. The loaded model is capable of performing speech enhancement, a task where it focuses on denoising audio files.\\n4. To apply noise suppression to a given audio file, use the 'separate_file' method of the model and provide the path to the noisy audio file as input.\\n5. After processing, save the enhanced audio file using the 'torchaudio.save' method.\\n<<>>: from speechbrain.pretrained import SepformerSeparation as separator\\nimport torchaudio\\nmodel = separator.from_hparams(source='speechbrain/sepformer-whamr', savedir='pretrained_models/sepformer-whamr')\\nest_sources = model.separate_file(path='path/to/noisy_audio_file.wav')\\ntorchaudio.save('enhanced_audio_file.wav', est_sources[:, :, 0].detach().cpu(), 8000)\"", "answer_id": "n2m7PM5coBKREKt2KQKwyU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 712, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: separator.from_hparams(source='speechbrain/sepformer-whamr', savedir='pretrained_models/sepformer-whamr')\\n<<>>: SpeechBrain\\n<<>>: 1. Install the SpeechBrain library using pip.\\n2. Import the SepformerSeparation class from speechbrain.pretrained.\\n3. Load the pretrained Sepformer model from the source 'speechbrain/sepformer-whamr' and save it to a directory 'pretrained_models/sepformer-whamr'.\\n4. The model can then be used to perform source separation on the overlapping audio recording. This will remove the overlaps and produce a single speaker recording.\\n<<>>: from speechbrain.pretrained import SepformerSeparation as separator\\nimport torchaudio\\nmodel = separator.from_hparams(source='speechbrain/sepformer-whamr', savedir='pretrained_models/sepformer-whamr')\\nest_sources = model.separate_file(path='overlapping_speakers.wav')\\ntorchaudio.save('single_speaker.wav', est_sources[:, :, 0].detach().cpu(), 8000)\\n\"", "answer_id": "LwVYMKfXWZUiGrsEaWU8Av", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 713, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: WaveformEnhancement.from_hparams('speechbrain/mtl-mimic-voicebank', 'pretrained_models/mtl-mimic-voicebank')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, we need to install the necessary library, which is 'speechbrain'.\\n2. Then, we import the necessary class, which is 'WaveformEnhancement' from 'speechbrain.pretrained'.\\n3. We use the 'from_hparams' method to load the pre-trained model 'speechbrain/mtl-mimic-voicebank', which is designed for speech enhancement tasks.\\n4. After loading the model, we can use it to enhance the speech signals in the audio recordings that our users upload.\\n5. The enhanced audio will be cleaner and free of noise, making for a better listening experience.\\n<<>>: from speechbrain.pretrained import WaveformEnhancement\\nenhance_model = WaveformEnhancement.from_hparams(\\n source='speechbrain/mtl-mimic-voicebank',\\n savedir='pretrained_models/mtl-mimic-voicebank',\\n)\\n# Apply speech enhancement to an audio file\\n# audio = enhance_model.load_audio('path/to/user_audio_file.wav').enhanced_audio\\n\"", "answer_id": "78tRK7VRx4J5tFCgS3KjZa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 714, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-to-audio', model='facebook/textless_sm_roberta_large')\\n<<>>: Fairseq\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from the transformers library to load the 'facebook/textless_sm_roberta_large' model, which is designed for audio-to-audio translation tasks.\\n3. Provide the loaded model with the Spanish voice message as input, and it will output a translated English voice message.\\n4. You can then listen to the translated English voice message to understand what your coworker was trying to say in the Spanish audio message.\\n<<>>: from transformers import pipeline\\naudio_translation = pipeline('audio-to-audio', model='facebook/textless_sm_roberta_large')\\nenglish_voice_message = audio_translation(spanish_voice_message.wav)\"", "answer_id": "YsPGiEzqjmcXcuWs7iCWE4", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 715, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: separator.from_hparams(source='speechbrain/sepformer-wham-enhancement', savedir='pretrained_models/sepformer-wham-enhancement')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'SepformerSeparation' from speechbrain.pretrained and 'torchaudio'.\\n2. Use the 'separator.from_hparams' function to load the pretrained model 'speechbrain/sepformer-wham-enhancement'.\\n3. The loaded model is designed for speech enhancement, which is a task where the model adds noise to a noisy recording to create a cleaner version of the audio.\\n4. Next, call the 'separate_file' method on the model to denoise a given audio file.\\n5. Finally, save the enhanced audio file using 'torchaudio.save'.\\n<<>>: from speechbrain.pretrained import SepformerSeparation as separator\\nimport torchaudio\\nmodel = separator.from_hparams(source='speechbrain/sepformer-wham-enhancement', savedir='pretrained_models/sepformer-wham-enhancement')\\nest_sources = model.separate_file(path='path_to_input_audio_file.wav')\\ntorchaudio.save('enhanced_audio_file.wav', est_sources[:, :, 0].detach().cpu(), 8000)\"", "answer_id": "boJisxMZQ9SbGSGmhM2Sup", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 716, "text": "<<>>: Audio Classification\\n<<>>: pipeline('audio-classification', model='mazkooleg/0-9up-hubert-base-ls960-ft')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create an audio classification model for recognizing spoken digits.\\n3. Specify the model 'mazkooleg/0-9up-hubert-base-ls960-ft' to be loaded. This model is fine-tuned for recognizing spoken digits in a variety of languages, making it suitable for a kid's educational toy.\\n4. The created classifier can be used to process audio input and classify spoken digits, making the toy more interactive and engaging for children.\\n<<>>: from transformers import pipeline\\ndigit_classifier = pipeline('audio-classification', model='mazkooleg/0-9up-hubert-base-ls960-ft')\\npredicted_digit = digit_classifier(child_audio_input)\\n\"", "answer_id": "Ets8pEqFDmomKJ7i5ue7TT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 717, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model= 'mazkooleg/0-9up-wavlm-base-plus-ft')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary functions from the transformers package. This includes 'pipeline' for creating the audio classification model.\\n2. We then use the 'pipeline' function to create the audio classification model, specifying the 'mazkooleg/0-9up-wavlm-base-plus-ft' model. This model is trained for identifying numbers in audio.\\n3. This model can be used to analyze the customer phone call recordings and identify the specific numbers mentioned by customers during the call.\\n<<>>: from transformers import pipeline\\naudio_classifier = pipeline('audio-classification', model='mazkooleg/0-9up-wavlm-base-plus-ft')\\nresult = audio_classifier(call_recording_file_path)\\n# replace 'call_recording_file_path' with path to your audio file\\n\"", "answer_id": "RpjFoTof3AZH55QYMziwF9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 718, "text": "<<>>: Audio Audio Classification\\n<<>>: EncoderClassifier.from_hparams(source='TalTechNLP/voxlingua107-epaca-tdnn')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which include 'EncoderClassifier' from speechbrain.pretrained and 'torchaudio'.\\n2. Use the 'EncoderClassifier.from_hparams()' function to load the model, which in this case is 'TalTechNLP/voxlingua107-epaca-tdnn'.\\n3. The loaded model is trained on the VoxLingua107 dataset and can be used to identify languages from audio samples.\\n4. Load the audio file and apply the classification model to identify the language spoken in the audio.\\n5. This model can be integrated into the virtual global tour guide to identify languages spoken by tourists and provide assistance accordingly.\\n<<>>: import torchaudio\\nfrom speechbrain.pretrained import EncoderClassifier\\nlanguage_id = EncoderClassifier.from_hparams(source='TalTechNLP/voxlingua107-epaca-tdnn')\\nsignal = language_id.load_audio('audio_file_path.mp3')\\nprediction = language_id.classify_batch(signal)\\nlanguage_code = prediction.argmax(axis=1).item()\\n\"", "answer_id": "JosjdtEUzSC4VF7TEJpDvG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 719, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'finiteautomata/beto-sentiment-analysis'. This model is designed for sentiment analysis in Spanish.\\n3. The model will analyze the text to determine the sentiment of the customer's last call with the support agents. Supported sentiment categories include 'positive', 'negative', and 'neutral'.\\n4. We will get the sentiment results as output, which can help to understand and improve customer service in the Spanish-speaking market.\\n<<>>: from transformers import pipeline\\nsentiment_pipeline = pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\\nresult = sentiment_pipeline(customer_comment)\\n\"", "answer_id": "aW87qWTiAhM2Ujb7ofZb8d", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 720, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: Pipeline.from_pretrained('pyannote/voice-activity-detection')\\n<<>>: pyannote.audio\\n<<>>: 1. Import the Pipeline class from the pyannote.audio library.\\n2. Load the pre-trained model for Voice Activity Detection ('pyannote/voice-activity-detection') using the Pipeline.from_pretrained() method.\\n3. Use the VoiceActivityDetection pipeline to process the audio file. It will return a VoiceActivityDetection result object that contains the following properties: start, stop, and duration of the voice segments.\\n4. Iterate over the segments and filter the voice segments from the silent ones based on the properties: start, stop, and duration.\\n5. Keep the voice segments and remove the silent parts.\\n<<>>: from pyannote.audio import Pipeline\\nvad = Pipeline.from_pretrained('pyannote/voice-activity-detection')\\nvoice_activity_result = vad('audio.wav')\\nvoice_segments = [segment for segment in voice_activity_result.get_timeline().support() if segment.end > segment.start]\\nnon_voice_segments = [segment for segment in voice_activity_result.get_timeline().support() if segment.end < segment.start]\\nvoiced_parts = voice_segments[:] # Modify this line to extract the wanted parts\\n\"", "answer_id": "fokTCewCRCg6zizXPXacvE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 721, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load(cached_download(hf_hub_url('julien-c/wine-quality', 'winequality-red.csv')))\\n<<>>: Scikit-learn\\n<<>>: 1. Import the necessary libraries, which include huggingface_hub, joblib, and pandas.\\n2. Use the Hugging Face Hub to download the pre-trained model and wine quality dataset.\\n3. Load the model using the joblib library.\\n4. Load the wine quality dataset using pandas and preprocess it to match the model's input requirements.\\n5. Use the pre-trained model to predict the quality of the wine samples.\\n<<>>: from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nREPO_ID = 'julien-c/wine-quality'\\nFILENAME = 'sklearn_model.joblib'\\nmodel = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))\\ndata_file = cached_download(hf_hub_url(REPO_ID, 'winequality-red.csv'))\\nwine_df = pd.read_csv(data_file, sep=';')\\nX = wine_df.drop(['quality'], axis=1)\\nlabels = model.predict(X[:3])\"", "answer_id": "WugU58BsDdkctxtVAUxi9C", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 722, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Speech2TextForConditionalGeneration.from_pretrained('facebook/s2t-medium-librispeech-asr')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes Speech2TextProcessor and Speech2TextForConditionalGeneration for the speech recognition model.\\n2. Load the pre-trained model 'facebook/s2t-medium-librispeech-asr', which is designed for automatic speech recognition tasks.\\n3. Use the from_pretrained method to instantiate the processor and the model.\\n4. Load the audio file that contains the speakers' voices in the podcast and convert it into an array format that the model can understand.\\n5. Process the audio array with the processor and generate the input features.\\n6. Use the model to generate a transcript with timestamps for each speaker.\\n<<>>: from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration\\nimport librosa\\nmodel = Speech2TextForConditionalGeneration.from_pretrained('facebook/s2t-medium-librispeech-asr')\\nprocessor = Speech2TextProcessor.from_pretrained('facebook/s2t-medium-librispeech-asr')\\n# Load the audio file\\naudio_array, sampling_rate = librosa.load('podcast_audio.wav', sr=16000)\\n# Process the audio array and generate the input features\\ninput_features = processor(audio_array, sampling_rate=sampling_rate, return_tensors='pt').input_features\\n# Generate transcript with timestamps\\ntranscription = model.generate(input_features)\\n\"", "answer_id": "G3bsYok2zdU38q5qfyLKix", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 723, "text": "1. Import the necessary classes from the transformers package, including TabTransformer and the relevant tokenizer.\\n2. Load your dataset as a pandas DataFrame, and ensure that the numerical and categorical features are stored in separate columns.\\n3. Instantiate a TabTransformer model with the desired configuration, such as 'feature_selection:auto' for automatic feature selection.\\n4. Process the dataset using the TabTransformer model, which will perform feature extraction and segmentation based on the customer behavior data.\\n5. The model output will include the selected features and their respective categorization labels, which can be used for segmentation and targeted marketing.\\n###Code:\\n```python\\nimport pandas as pd\\nfrom transformers import TabTransformer\\nmodel = TabTransformer.from_pretrained('microsoft/tab-base-finetuned-seg')\\ndata = pd.read_csv('customer_behavior.csv')\\n# replace 'customer_behavior.csv' with the path to your dataset\\nselected_features = model.process_dataset(data)\\n# Now you can analyze the selected_features to segment your customers\\n```\"", "answer_id": "nYsjL8zzgmM5i7VMWFYchg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 724, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'cross-encoder/nli-deberta-v3-xsmall'.\\n3. The loaded model will be used for zero-shot classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. The model will infer if an employee's annual salary meets or exceeds $50000 based on the provided text.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\ntext = \\\"Annual salary: $50000 or more\\\"\\nresult = classifier(text)\\n\"", "answer_id": "bAUhLqT2BJptsnTjNUSyQj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 725, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. We import the necessary libraries, including json, joblib, and pandas.\\n2. The model is loaded using the joblib library's load function, which accesses a pre-trained binary classification model for carbon emissions.\\n3. We read the data from a CSV file and preprocess it, converting the numerical data into the correct format expected by the model.\\n4. We then use the model to predict whether the newly-setup chemical plant is exceeding carbon emission limits or not.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "Hpnvp7nuTqsA2AYaEsYgUB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 726, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, which are 'joblib' for loading the trained model and 'pandas' for handling data.\\n2. Load the pre-trained model using the joblib.load function, which has been trained for single-column regression tasks, such as predicting housing mortgage amounts.\\n3. Load the feature configuration from the model using the config attribute.\\n4. Read the input housing data using pandas 'read_csv' function, and select the necessary features as specified in the config.\\n5. Rename the columns of the data to match the expected input format for the model.\\n6. Use the loaded model to predict the mortgage amount for the input housing data.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = {'features': [...]}\\ndata = pd.read_csv('housing_data.csv')\\ndata = data[config['features']]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "PB6vdvbFKDchinSmPUx64R", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 727, "text": "<<>>: Reinforcement Learning Robotics\\n<<>>: AutoModel.from_pretrained('edbeeching/decision-transformers-gym-hopper-medium')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'AutoModel' from transformers.\\n2. Load the pretrained Decision Transformer model for the Gym Hopper environment using the AutoModel.from_pretrained() function and the provided model name 'edbeeching/decision-transformers-gym-hopper-medium'.\\n3. The Decision Transformer model has been trained on medium trajectories sampled from the Gym Hopper environment. You can use this model to train your robot to hop by feeding it training data with appropriate formats.\\n4. After training your robot using this model, it will be able to perform a hop based on the learned behavior from the training data.\\n<<>>: from transformers import AutoModel\\nmodel = AutoModel.from_pretrained('edbeeching/decision-transformers-gym-hopper-medium')\\n# Use the model to train your robot to hop\\n\"", "answer_id": "TMuUFCTaSNsuqmLq7BMyfY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 728, "text": "<<>>: Reinforcement Learning Robotics\\n<<>>: model_utils.load_model('model_utils.VC1_BASE_NAME')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'model_utils' from vc_models.models.vit.\\n2. Use the 'load_model' function from model_utils to load the 'facebook/vc1-large' model. This model is trained on a wide range of visual data and can be used to understand and analyze the scene in real-time.\\n3. The robot's camera system can be used to capture images and feed them into the model to obtain an understanding of the current context and activities.\\n4. The model's output can then be used to take appropriate actions based on the elderly's current activities, such as providing assistance or offering companionship.\\n<<>>: from vc_models.models.vit import model_utils\\nmodel, embd_size, model_transforms, model_info = model_utils.load_model(model_utils.VC1_BASE_NAME)\\nimg = capture_elderly_activity_image() # Function to capture an image from the robot's camera system\\ntransformed_img = model_transforms(img)\\nunderstood_elderly_activity = model(transformed_img)\\n\"", "answer_id": "WbzMb4c82o2qzNpaGgnc29", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 729, "text": "\\n1. Import the necessary classes from the transformers package, including AutoTokenizer and AutoModel.\\n2. Load the pre-trained model 'microsoft/codebert-base' using the from_pretrained method of the AutoModel class.\\n3. Load the tokenizer using the from_pretrained method of the AutoTokenizer class.\\n4. Tokenize the input text that describes the source code requirements using the tokenizer's encode method.\\n5. Pass the tokenized input to the model's forward method to generate features.\\n6. These features can then be used to train a recommendation model or perform other tasks like code search and clustering.\\n### Code:\\n```python\\nfrom transformers import AutoTokenizer, AutoModel\\ntokenizer = AutoTokenizer.from_pretrained('microsoft/codebert-base')\\nmodel = AutoModel.from_pretrained('microsoft/codebert-base')\\ntext = \\\"Write a function to find the factorial of a number.\\\"\\ninputs = tokenizer.encode(text, return_tensors='pt')\\noutputs = model(**inputs)\\nfeatures = outputs.last_hidden_state\\n```\"", "answer_id": "TuQ4TLhh5E6VZxDnUECPYt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 730, "text": "<<>>: Multimodal Feature Extraction\\n<<>>: AutoModel.from_pretrained('microsoft/unixcoder-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries: AutoTokenizer and AutoModel from transformers.\\n2. Load the tokenizer and the pre-trained model 'microsoft/unixcoder-base'. The model is designed to extract features from code and comments.\\n3. Tokenize the source code and process it with the model, which will generate features related to programming constructs and comments.\\n4. These features can be used for further data analysis, such as sentiment analysis or other machine learning tasks.\\n<<>>: from transformers import AutoTokenizer, AutoModel\\ntokenizer = AutoTokenizer.from_pretrained('microsoft/unixcoder-base')\\nmodel = AutoModel.from_pretrained('microsoft/unixcoder-base')\\ntokenized_input = tokenizer(source_code, return_tensors='pt')\\nfeatures = model(**tokenized_input).last_hidden_state\\n\"", "answer_id": "SscdexQpd2AjqPnvyQphk5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 731, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', vae='AutoencoderKL.from_pretrained(stabilityai/sd-vae-ft-mse)')\\n<<>>: Hugging Face\\n<<>>:1. Import AutoencoderKL from the diffusers.models module and StableDiffusionPipeline from the diffusers module.\\n2. Load the pre-trained model 'CompVis/stable-diffusion-v1-4' using the AutoencoderKL.from_pretrained function.\\n3. Load the fine-tuned VAE model 'stabilityai/sd-vae-ft-mse' for text-to-image generation.\\n4. Create a pipeline object by combining the two models together using the StableDiffusionPipeline.from_pretrained function.\\n5. The pipeline object can be used to generate images for the characters in the given text.\\n<<>>: from diffusers.models import AutoencoderKL\\nfrom diffusers import StableDiffusionPipeline\\nmodel = 'CompVis/stable-diffusion-v1-4'\\nvae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse')\\npipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)\\ntext_story = \\\"Once upon a time in a magical forest, there lived a brave knight named Arthur.\\\"\\ngenerated_image = pipe.generate(text_story)\"", "answer_id": "jsSmWLuMFpsHHfgJnCHgjP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 732, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text-generation', model='microsoft/git-base-coco')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-generation model, specifying the model 'microsoft/git-base-coco' to be loaded. This model has been trained on a large dataset of images with associated text labels and can be used for generating meaningful descriptions of images.\\n3. Load the image data from a file or acquire it in real-time from a camera.\\n4. Use the created model to generate a textual description of the park landscape image you designed.\\n<<>>: from transformers import pipeline\\nfrom PIL import Image\\nimage_generator = pipeline('text-generation', model='microsoft/git-base-coco')\\nimage = Image.open('park_image.jpg')\\n# replace 'park_image.jpg' with path to your image\\ndescription = image_generator(image)\"", "answer_id": "DqYQYugqEUqcw8wFsXNsgp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 733, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-base')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'Image' and 'Pix2StructForConditionalGeneration' from transformers.\\n2. Load the pre-trained model 'google/pix2struct-base' using the Pix2StructForConditionalGeneration.from_pretrained() method.\\n3. For each image in your database, convert it into an RGB image and pass it to the model.\\n4. The model will generate a textual description for the image, which you can store alongside the image in your database.\\n<<>>: from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor\\nfrom PIL import Image\\nmodel = Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-base')\\nprocessor = Pix2StructProcessor.from_pretrained('google/pix2struct-base')\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with the path to your image\\ntext_description = model.generate(images=image)\\n\"", "answer_id": "27at9UBRqbZa6wrUvQaHdc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 734, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: LayoutLMv2ForQuestionAnswering.from_pretrained('dperales/layoutlmv2-base-uncased_finetuned_docvqa')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries: LayoutLMv2Processor and LayoutLMv2ForQuestionAnswering from transformers, and a tokenizer.\\n2. Load the pre-trained model 'dperales/layoutlmv2-base-uncased_finetuned_docvqa'. This model is designed to answer questions based on a document's content.\\n3. Process and tokenize the question and document text using the LayoutLMv2Processor.\\n4. Use the LayoutLMv2ForQuestionAnswering model to predict answers to customer questions based on the document contents.\\n5. Return the model-generated answers to the customer's questions.\\n<<>>: from transformers import LayoutLMv2Processor, LayoutLMv2ForQuestionAnswering\\nfrom transformers import AutoTokenizer\\nimport torch\\nprocessor = LayoutLMv2Processor.from_pretrained('dperales/layoutlmv2-base-uncased_finetuned_docvqa', apply_ocr=False)\\nmodel = LayoutLMv2ForQuestionAnswering.from_pretrained('dperales/layoutlmv2-base-uncased_finetuned_docvqa')\\ntokenizer = AutoTokenizer.from_pretrained('dperales/layoutlmv2-base-uncased_finetuned_docvqa')\\ndef answer_question(question, context):\\n inputs = processor(question, context, return_tensors=\\\"pt\\\")\\n outputs = model(**inputs)\\n start_index = torch.argmax(outputs.start_logits)\\n end_index = torch.argmax(outputs.end_logits)\\n tokens = inputs[\\\"input_ids\\\"][0][start_index:end_index + 1]\\n answer = tokenizer.decode(tokens)\\n return answer\\n\"", "answer_id": "PWt4TJzRRD7LaHnv7agZgm", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 735, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/roberta-base-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We first import the required classes from the transformers library: AutoModelForQuestionAnswering and AutoTokenizer.\\n2. We then use the from_pretrained method to load the pre-trained model 'deepset/roberta-base-squad2' and the associated tokenizer. This model has been trained on SQuAD v2 dataset and is suitable for extracting answers from text, such as insurance policy documents.\\n3. With the model and tokenizer loaded, we can use them to create a question-answering pipeline to extract relevant information from the text of the insurance policy document.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel_name = 'deepset/roberta-base-squad2'\\nmodel = AutoModelForQuestionAnswering.from_pretrained(model_name)\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nnlp = pipeline('question-answering', model=model, tokenizer=tokenizer)\\nQA_input = {\\n 'question': 'What is the maximum coverage amount?',\\n 'context': 'Insurance policy document text goes here...'\\n}\\nresult = nlp(QA_input)\\n\"", "answer_id": "ZxKSWuriReqVuJHwg2hHZG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 736, "text": "1. Import the necessary packages, which include PIL (Python Imaging Library) and NumPy for image processing, and huggingface_hub for the transformers package.\\n2. Use the HEDdetector and the pre-trained model 'Asteroids-1.0' to detect and estimate the distance between the camera and the objects in the image.\\n3. Load the image data from a file or acquire it in real-time from the drone's camera feed.\\n4. Run the pre-trained model on the input image to get the object detection and distance estimation results.\\n5. Visualize the output to see the detected objects and their estimated distances from the camera.\\n<<>>: \\nfrom PIL import Image\\nimport numpy as np\\nfrom transformers import AutoImageProcessor, CLIPModel\\nfrom tqdm import tqdm\\nmodel = CLIPModel.from_pretrained('openai/clip-vit-large-patch14')\\nprocessor = AutoImageProcessor.from_pretrained('openai/clip-vit-large-patch14')\\nimage = Image.open('drone_image.jpg')\\nimage = np.array(image)\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n# Post-processing to detect objects and estimate distances\\n# ...\\n# Visualizing the output\\n# ...\\n\"", "answer_id": "bCwQZamXzN4zamVz6DXcGA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 737, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-depth')\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary packages and modules, including diffusers, transformers, PIL, numpy, and torch.\\n2. Load the depth estimation model 'lllyasviel/sd-controlnet-depth' using the from_pretrained method of the ControlNetModel class.\\n3. Preprocess the input image by resizing it to the required dimensions and converting it to a tensor.\\n4. Pass the preprocessed image through the depth estimation model, which will generate a depth map image representing the estimated depths of various elements in the architectural design.\\n5. Convert the depth map image into a 2D format, and save or display it for further analysis or design modifications.\\n<<>>: from transformers import pipeline\\nfrom diffusers import ControlNetModel\\nfrom PIL import Image\\nimport numpy as np\\nimport torch\\nmodel = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-depth', torch_dtype=torch.float16)\\nimage = Image.open('arch_design_image.jpg')\\n# replace 'arch_design_image.jpg' with path to your image\\ncontrol_image = np.array(image)\\ncontrol_image = control_image.transpose(2, 0, 1).astype(np.float)\\ndepth_map = model(control_image)\\ndepth_image = np.array(depth_map).transpose(2, 0, 1).astype(np.float)\\ndepth_image = Image.fromarray(depth_image)\\ndepth_image.save('depth_estimation_output.png')\"", "answer_id": "RDUNtjBakAT9Bcqeq2qRj9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 738, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', 'martinezomg/vit-base-patch16-224-diabetic-retinopathy')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'martinezomg/vit-base-patch16-224-diabetic-retinopathy'.\\n3. The loaded model will be used for image classification, which is a task where the model makes a prediction for a given image.\\n4. The model will predict whether the image is diabetic retinopathy or not, based on the features it was trained on.\\n5. We will provide the path to the image ('eye.jpg') as input to the model for classification.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', 'martinezomg/vit-base-patch16-224-diabetic-retinopathy')\\nimage_path = 'eye.jpg'\\nresult = image_classifier(image_path)\\n\"", "answer_id": "jSTfKnW7NE7PfX5nNEYP9o", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 739, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTModel.from_pretrained('facebook/dino-vits8')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including ViTFeatureExtractor and ViTModel from transformers, Image from PIL, and requests for handling image data.\\n2. Create a feature extractor using the ViTFeatureExtractor.from_pretrained('facebook/dino-vits8') method, which prepares the image data for the ViT model.\\n3. Load the pre-trained Vision Transformer (ViT) model using the ViTModel.from_pretrained('facebook/dino-vits8') method.\\n4. Use the feature extractor to process the input image and return tensors ready to be fed into the model.\\n5. Pass the processed image through the model to obtain the output, which can be used to recognize objects in the image.\\n<<>>: from transformers import ViTFeatureExtractor, ViTModel\\nfrom PIL import Image\\nimport requests\\nurl = 'https://example.com/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vits8')\\nmodel = ViTModel.from_pretrained('facebook/dino-vits8')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlast_hidden_states = outputs.last_hidden_state\"", "answer_id": "TpfzpJ2PYEnuagC9GGcfLo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 740, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ConvNextForImageClassification.from_pretrained('facebook/convnext-large-224')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes ConvNextFeatureExtractor and ConvNextForImageClassification for image classification.\\n2. Load the pre-trained model 'facebook/convnext-large-224'. This model has been trained for image classification tasks and is capable of recognizing different dog breeds based on image features.\\n3. Create a function to take an uploaded image and preprocess it using the ConvNextFeatureExtractor.from_pretrained method. This function will convert the image data into a format that can be input into the classification model.\\n4. Classify the image using the pre-trained ConvNextForImageClassification model. The model will produce a prediction indicating the breed of the dog in the image.\\n<<>>: from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification\\nimport torch\\nfrom PIL import Image\\nimage = Image.open('uploaded_image.jpg')\\n# replace 'uploaded_image.jpg' with path to the uploaded image\\nfeature_extractor = ConvNextFeatureExtractor.from_pretrained('facebook/convnext-large-224')\\nmodel = ConvNextForImageClassification.from_pretrained('facebook/convnext-large-224')\\ninputs = feature_extractor(image, return_tensors='pt')\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\npredicted_label = logits.argmax(-1).item()\\nprint(model.config.id2label[predicted_label])\"", "answer_id": "EdgUU76UB36xsW7MUEf5Tv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 741, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required pipeline function from the transformers package.\\n2. Use the pipeline function to create an image classification model with the specified 'laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup' model.\\n3. Use the created classifier to classify the input image according to the species you are interested in. The classifier will return a list of probabilities associated with each class (species).\\n4. Select the species with the highest probability as the final classification result.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup')\\nimage_path = 'path/to/image.jpg'\\n# replace 'path/to/image' with the path to your image\\nclass_names = ['species_1', 'species_2', 'species_3'] # replace with your desired species\\nresults = image_classifier(image_path, class_names)\\nclassification = max(results, key=results.get)\\n\"", "answer_id": "aj9rx5V5v746ZxLbkp3BeF", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 742, "text": "<<>>: Computer Vision Object Detection\\n<<>>: DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers and PIL packages. This includes DetrForObjectDetection for the object detection model and Image for processing image data.\\n2. We then use the from_pretrained method of the DetrForObjectDetection class to load the pre-trained model 'facebook/detr-resnet-50'. This model has been trained for object detection tasks, which is exactly what we need for detecting objects in warehouses.\\n3. We load the image data from a file, or it can be acquired in real-time from a camera in the warehouse.\\n4. This model can then be used to analyze the image and identify the objects within the warehouse.\\n<<>>: from transformers import DetrForObjectDetection\\nfrom PIL import Image\\nimage = Image.open('warehouse_image.jpg')\\n# replace 'warehouse_image.jpg' with path to your image\\nmodel = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "5C6rH4kyGGiEtX4tdMrjdx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 743, "text": "<<>>: Computer Vision Object Detection\\n<<>>: DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101-dc5')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import required libraries like torch, transformers, PIL, and requests.\\n2. Use the DetrFeatureExtractor and DetrForObjectDetection classes from the transformers library to load the pre-trained model 'facebook/detr-resnet-101-dc5'. This model is designed for objects detection tasks in images.\\n3. Load the image data from a file or through an API, such as requests.\\n4. Use the feature_extractor to convert the images into the required input format and then process the images using the DetrForObjectDetection model.\\n5. The outputs from the model can be used to analyze the detected objects and their locations in the images.\\n<<>>: import torch\\nfrom transformers import DetrFeatureExtractor, DetrForObjectDetection\\nfrom PIL import Image\\nimport requests\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101-dc5')\\nmodel = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101-dc5')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\"", "answer_id": "KNJhVv83KvhgUwj85jxfTj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 744, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-plane-detection')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, we import the YOLO class from the ultralyticsplus package. \\n2. We then create an instance of the YOLO class with the pre-trained model 'keremberke/yolov8m-plane-detection', which is specifically trained to detect airplanes.\\n3. We can adjust the model's parameters, such as confidence threshold, intersection over union (IoU) threshold, and maximum number of detections, to suit your application requirements.\\n4. Using the model's predict() method, we can obtain detected plane boxes for the drone surveillance system.\\n5. We can visualize the detected planes using the render_result() function.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-plane-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'your_drone_surveillance_image'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "c3uTtqpSzWBqfmrkmnmMFb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 745, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers and PIL packages. This includes YolosForObjectDetection for the object detection model and Image for processing image data.\\n2. We then use the from_pretrained method of the YolosForObjectDetection class to load the pre-trained model 'hustvl/yolos-tiny'. This model has been trained for object detection tasks, which is exactly what we need for monitoring apartment corridors.\\n3. We load the image data from a file, or it can be acquired in real-time from the IoT device's camera.\\n4. This model can then be used to analyze an image and detect objects in it. If anything abnormal is detected, we can trigger an alert.\\n<<>>: from transformers import YolosForObjectDetection\\nfrom PIL import Image\\nimage = Image.open('corridor_image.jpg')\\n# replace 'corridor_image.jpg' with path to your image\\nmodel = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "N9jxLQva8468V8CFejx9q8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 746, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-large-cityscapes-semantic')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import necessary libraries such as requests, torch, PIL, and transformers.\\n2. Load the pre-trained Mask2Former model 'facebook/mask2former-swin-large-cityscapes-semantic' for universal segmentation.\\n3. Acquire an image (from a dataset or real-time drone camera feed) to process.\\n4. Process the image using the AutoImageProcessor and the Mask2Former model.\\n5. Extract predicted semantic map and masks_logits from the model output.\\n6. Post-process the semantic map and use it to generate a semantic segmentation image.\\n7. Visualize the segmentation result using an image editing library or display the image.\\n<<>>: \\nimport requests\\nimport torch\\nfrom PIL import Image\\nfrom transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation\\nprocessor = AutoImageProcessor.from_pretrained('facebook/mask2former-swin-large-cityscapes-semantic')\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained('facebook/mask2former-swin-large-cityscapes-semantic')\\nimage = Image.open(\\\"drone_image.jpg\\\") # Replace with real-time drone image feed\\ninputs = processor(images=image, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\nresult = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_semantic_map = result['segmentation']\\nrender = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]['render']\\nimage = render.images[0]\\nimage.show()\"", "answer_id": "gVQR9WBjc4ZFSPBadK3VZj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 747, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-tiny-coco')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries: MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation from transformers, Image from PIL, and requests.\\n2. Load the pre-trained model 'facebook/maskformer-swin-tiny-coco', which has been trained on the COCO dataset for instance-based image segmentation.\\n3. Load the image data from a URL or a local file.\\n4. Preprocess the image using the MaskFormerFeatureExtractor.\\n5. Run the model on the preprocessed image data to obtain the segmentation result.\\n6. Post-process the segmentation result to generate a semantic map.\\n7. The semantic map can be used to identify different objects in the urban landscape image.\\n<<>>: from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = MaskFormerFeatureExtractor.from_pretrained('facebook/maskformer-swin-tiny-coco')\\nmodel = MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-tiny-coco')\\nurl = 'https://your_image_url_here.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nresult = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_semantic_map = result['segmentation']\"", "answer_id": "jGuGRCQziJTo7syGn24jJy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 748, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: YOLO('keremberke/yolov8m-pcb-defect-segmentation')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the ultralyticsplus and ultralytics libraries.\\n2. Import the YOLO class from ultralyticsplus and render_result function.\\n3. Create a YOLO model with the 'keremberke/yolov8m-pcb-defect-segmentation' model.\\n4. Set model parameters such as 'conf', 'iou', 'agnostic_nms', and 'max_det' according to your requirements.\\n5. Use the predict method of the model to detect defects in a given image.\\n6. Render the results using the render_result function from the ultralyticsplus library.\\n7. View the rendered result to see the defects highlighted on the PCB board image in real-time.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-pcb-defect-segmentation')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'your_pcb_image.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "kba37QVvK4LQ9php45PG2T", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 749, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: YOLO('keremberke/yolov8s-pothole-segmentation')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the YOLO and render_result functions from the ultralyticsplus library.\\n2. Create an instance of the YOLO model with the 'keremberke/yolov8s-pothole-segmentation' pre-trained model for detecting potholes in images.\\n3. Set the model's parameters, such as confidence threshold, IoU threshold, agnostic non-maximum suppression, and maximum detections.\\n4. Pass the drone footage image to the model's predict function to analyze the image for potholes.\\n5. Analyze the model's output results, including bounding boxes and masks for detected potholes.\\n6. Render and display the segmented image with pothole detections and render results.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8s-pothole-segmentation')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path_to_drone_footage_image.jpg'\\nresults = model.predict(image)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "5swFfCFCusoYBScpcGJ5gN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 750, "text": "\\n<<>>: Computer Vision Image Segmentation\\n<<>>: YOLO('keremberke/yolov8s-pothole-segmentation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the YOLO and render_result functions from the ultralyticsplus library.\\n2. Create a YOLO model instance with the pre-trained 'keremberke/yolov8s-pothole-segmentation' model for pothole segmentation.\\n3. Set up the model's confidence threshold, IoU threshold, agnostic NMS, and maximum detections.\\n4. Provide the image containing the road potholes to the model using the 'predict()' method.\\n5. The results include the detected potholes and their segmentation masks, which can be used for further analysis and road maintenance reporting.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8s-pothole-segmentation')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'https://path_to_image.jpg'\\nresults = model.predict(image)\\nsegmented_potholes = results[0].boxes\\nrendered_image = render_result(model=model, image=image, result=results[0])\\nrendered_image.show()\"", "answer_id": "mqV6KU8ZhE8vQRhgubeLUW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 751, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: StableDiffusionImageVariationPipeline.from_pretrained('lambdalabs/sd-image-variations-diffusers', revision='v2.0')\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries, including StableDiffusionImageVariationPipeline from the diffusers package and Image from PIL.\\n2. Load the pretrained model 'lambdalabs/sd-image-variations-diffusers' using the from_pretrained method of the StableDiffusionImageVariationPipeline class.\\n3. Load the original product image using Image.open().\\n4. Apply the necessary transformations to the image (resizing, normalization, etc.) based on the model's configuration.\\n5. Generate image variations using the model's generate method.\\n6. Save the generated image variations to use in advertising materials.\\n<<>>: from diffusers import StableDiffusionImageVariationPipeline\\nfrom PIL import Image\\noriginal_image = Image.open('path/to/original/image.jpg')\\n# Apply necessary transformations\\ntransforms = {'size': (224, 224), 'shear': (0, 0), 'rotation': (0, 0), 'padding': (0, 0), 'normalization': (True, True)}\\ntransformed_image = original_image.apply(transforms)\\n# Generate image variations\\nmodel = StableDiffusionImageVariationPipeline.from_pretrained('lambdalabs/sd-image-variations-diffusers', revision='v2.0')\\nimage_variations = model.generate(transformed_image, num_inference_steps=20)\\n# Save the generated image variations\\nfor idx, img in enumerate(image_variations):\\n img.save(f'image_variation_{idx}.jpg')\\n\"", "answer_id": "B5bnx4Kde7Lwh8RHSmRxvo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 752, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-openpose')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries, such as diffusers, controlnet_aux, and PIL.\\n2. Load the pre-trained ControlNetModel using the 'lllyasviel/sd-controlnet-openpose' checkpoint.\\n3. Create a StableDiffusionControlNetPipeline by combining the pre-trained ControlNet model with the Stable Diffusion model.\\n4. Set the pipeline's scheduler to UniPCMultistepScheduler using the current timestep value as the base scheduler.\\n5. Provide the input image, which contains the actor's pose, and set the pipe's input to be that image.\\n6. Use the pipe to generate the human pose estimation and save the result as an output image.\\n<<>>: from PIL import Image\\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\\nimport torch\\nfrom controlnet_aux import OpenposeDetector\\nfrom diffusers.utils import load_image\\nopenpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')\\nimage = load_image('path/to/image.png')\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-openpose', torch_dtype=torch.float16),\\n safety_checker=None, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nimage = openpose(image)\\npose_image = image\\npose_image.save('pose_estimation_output.png')\"", "answer_id": "TsYKZqJtFT98uomRQ36xCw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 753, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-depth')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary libraries and classes, including the ControlNetModel class from the diffusers package.\\n2. Load the pre-trained depth estimation model, 'lllyasviel/sd-controlnet-depth', using the from_pretrained() method of the ControlNetModel class.\\n3. Process the input image using a feature extractor to obtain the necessary image inputs for the model.\\n4. Use the depth_estimator model to predict the depth map of the input image.\\n5. Create an image from the depth map using a post-processing component.\\n6. The resulting depth map can be visualized and used for further analysis or processing.\\n<<>>: from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nfrom diffusers.utils import load_image\\nfrom transformers import CLIPTextModel\\nimport numpy as np\\nimport torch\\nmodel = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-depth', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=model, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\ninput_image = load_image('https://your-image-url-here')\\ndepth_map = model.predict(input_image)\\nclip_model = CLIPTextModel.from_pretrained('runwayml/stable-diffusion-v1-5')\\noutput_text = clip_model.generate(depth_map, max_length=16, num_beams=4, return_dict_in_generate=True)\\noutput_image = torch.tensor(output_text['sample'])\\noutput_image = np.array(output_image).transpose(0, 3, 1, 2).astype(np.uint8)\\n\"", "answer_id": "TLmUYP29TvgAxB3eLjicD3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 754, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/control_v11e_sd15_ip2p')\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary libraries and modules, including ControlNetModel from diffusers, StableDiffusionControlNetPipeline from transformers, UniPCMultistepScheduler from transformers, and other required packages.\\n2. Load the pre-trained ControlNet model from Hugging Face using the model checkpoint 'lllyasviel/control_v11e_sd15_ip2p'.\\n3. Create a new pipeline object with the StableDiffusionControlNetPipeline and the loaded ControlNet model.\\n4. Set the scheduler for the pipeline using the UniPCMultistepScheduler.\\n5. Define the number of inference steps during the image generation process.\\n6. Set the input prompt to describe the desired characteristics of the new art piece.\\n7. Use the pipeline with the input prompt and number of inference steps to generate the new image.\\n8. Save the generated image to your digital gallery.\\n<<>>: from diffusers import ControlNetModel\\nfrom transformers import StableDiffusionControlNetPipeline, UniPCMultistepScheduler\\nimport torch\\ncontrolnet = ControlNetModel.from_pretrained('lllyasviel/control_v11e_sd15_ip2p', torch_dtype=torch.float16)\\npipe = StableDiffusionControlNetPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_model_cpu_offload()\\nprompt = \\\"Abstract art with vibrant colors\\\"\\ngenerator = torch.manual_seed(0)\\nnew_image = pipe(prompt, num_inference_steps=30, generator=generator).images[0]\\nnew_image.save('new_art_work.png')\\n\"", "answer_id": "Qb9PaHkcaPHvB4fk5LHM8b", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 755, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('MFawad/sd-class-butterflies-32')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary class from the diffusers package. This includes DDPMPipeline for the unconditional image generation model.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'MFawad/sd-class-butterflies-32'. This model has been trained for unconditional image synthesis tasks involving butterflies.\\n3. This model can then be used to generate butterfly images, which can be used in the butterfly-themed stationery set for children.\\n<<>>: from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained('MFawad/sd-class-butterflies-32')\\nimage = pipeline().images[0]\\nimage.save('generated_butterfly.png')\\n\"", "answer_id": "gqJ9SrVDTyRmoP3sMv4Kb5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 756, "text": "<<>>: Computer Vision Image Classification\\n<<>>: timm.create_model('hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including timm for the image classification model and Image from PIL for image processing.\\n2. Load the pre-trained model 'hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k' using the timm.create_model function. This model has been trained for image classification tasks, which is what we need to detect butterflies in images.\\n3. Read the user's desired theme as a file path.\\n4. Open the image file and process it with the feature extractor.\\n5. Pass the processed image through the classification model.\\n6. The model will generate predictions for the image, which can then be used to suggest pictures of butterflies whenever the user attempts to change their theme.\\n<<>>: from urllib.request import urlopen\\nfrom PIL import Image\\nimport timm\\nimage = Image.open(urlopen('https://path/to/image'))\\nmodel = timm.create_model('hf_hub:timm/vit_tiny_patch16_224.augreg_in21k_ft_in1k', pretrained=True)\\nmodel = model.eval()\\ndata_config = timm.data.resolve_model_data_config(model)\\ntransforms = timm.data.create_transform(**data_config, is_training=False)\\noutput = model(transforms(image).unsqueeze(0))\\n\"", "answer_id": "QeEAQoEkBp2XZHoax4EkdY", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 757, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('pravsels/ddpm-ffhq-vintage-finetuned-vintage-3epochs')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the DDPMPipeline class from the diffusers package, which is required for unconditional image generation.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'pravsels/ddpm-ffhq-vintage-finetuned-vintage-3epochs'. This model has been trained to generate vintage-style images, which is suitable for our promotion poster.\\n3. This model can then be used to generate an image, which can serve as the basis for the promotional poster.\\n<<>>: from diffusers import DDPMPipeline\\nvintage_pipeline = DDPMPipeline.from_pretrained('pravsels/ddpm-ffhq-vintage-finetuned-vintage-3epochs')\\nimage = vintage_pipeline().images[0]\\nimage.save('vintage_image.png')\"", "answer_id": "brsK73nyTKYYYr546Zpocc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 758, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DiffusionPipeline.from_pretrained('myunus1/diffmodels_galaxies_scratchbook', torch_dtype=torch.float16)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install the required Python packages: 'diffusers', 'transformers', 'scipy', and 'torch'.\\n2. Import the necessary classes from the diffusers package, including 'DiffusionPipeline' for the unconditional image generation model.\\n3. Load the pre-trained model 'myunus1/diffmodels_galaxies_scratchbook' using the 'DiffusionPipeline.from_pretrained()' method.\\n4. Set the desired number of inference steps for the generation process.\\n5. Call the 'pipe()' method with the input_ids to generate a batch of images.\\n6. Save the generated image to a file.\\n<<>>: from diffusers import DiffusionPipeline\\nimport torch\\nmodel_id = 'myunus1/diffmodels_galaxies_scratchbook'\\npipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to('cuda')\\nsteps = 20\\ngenerated_images = pipe(input_ids=torch.randint(steves[0], steves[1], (1, 28, 28)), num_inference_steps=steps)\\ngenerated_image_file_name = 'generated_butterfly_image.png'\\ngenerated_image = generated_images.images[0]\\nprint('Writing image to file:', generated_image_file_name)\\ngenerated_image.save(generated_image_file_name)\"", "answer_id": "moHGPWZCuTJDDW5AxhbeFX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 759, "text": "<<>>: Multimodal Video-Text Modeling\\n<<>>: AutoModelForVideoClassification.from_pretrained('lmazzon70/videomae-base-finetuned-kinetics-finetuned-rwf2000-epochs8-batch8-kb')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary class from the transformers package provided by Hugging Face. This includes AutoModelForVideoClassification for video classification models.\\n2. We use the from_pretrained method of the AutoModelForVideoClassification class to load the pre-trained model 'lmazzon70/videomae-base-finetuned-kinetics-finetuned-rwf2000-epochs8-batch8-kb'. This model has been trained on a large dataset (Kinetics-400) and should be able to classify videos into various categories based on their content.\\n3. The loaded model can then be used to analyze video input and generate predictions for the content and category of the video.\\n<<>>: from transformers import AutoModelForVideoClassification\\nvideo_classifier = AutoModelForVideoClassification.from_pretrained('lmazzon70/videomae-base-finetuned-kinetics-finetuned-rwf2000-epochs8-batch8-kb')\\ncategory_prediction = video_classifier(video_input)\\n\"", "answer_id": "WtH3BDcN7Hfz8B7N6mTPtK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 760, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-kinetics')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Start by importing the necessary libraries and classes, such as VideoMAEImageProcessor and VideoMAEForVideoClassification from the transformers package, numpy for numerical processing, and Torch for PyTorch functionality.\\n2. Load the pre-trained video classification model 'MCG-NJU/videomae-small-finetuned-kinetics' using VideoMAEForVideoClassification.from_pretrained().\\n3. Create a VideoMAEImageProcessor instance, which will be used to preprocess the input video.\\n4. Preprocess the video frames, converting them into the correct format and tensor type expected by the model.\\n5. Pass the preprocessed video frames to the model and obtain the classification results.\\n6. Determine the type of sports being played in the video based on the classification results.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = load_video_clip() # Replace `load_video_clip` with a function to load your video clip.\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-small-finetuned-kinetics')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-small-finetuned-kinetics')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nsports_type = model.config.id2label[predicted_class_idx]\\n\"", "answer_id": "neHpGaZZ4RgMi5wyUVJfNj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 761, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library: VideoMAEFeatureExtractor and VideoMAEForPreTraining.\\n2. Load the VideoMAE model ('MCG-NJU/videomae-base-short-ssv2') using the VideoMAEForPreTraining.from_pretrained() method.\\n3. To process videos, we first need to extract features from the video using the VideoMAEFeatureExtractor.from_pretrained() method, which returns a VideoMAEFeatureExtractor object.\\n4. Pass the video to the feature extractor and obtain the input tensors.\\n5. With the input tensors, we can now use the VideoMAE model to analyze the video and classify the exercise being performed.\\n6. The output logits from the model can be used to determine the exercise category.\\n<<>>: from transformers import VideoMAEFeatureExtractor, VideoMAEForPreTraining\\nimport numpy as np\\nfeature_extractor = VideoMAEFeatureExtractor.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\nmodel = VideoMAEForPreTraining.from_pretrained('MCG-NJU/videomae-base-short-ssv2')\\nvideo = list(np.random.randn(16, 3, 224, 224)) # Replace with actual video data\\ninputs = feature_extractor(video, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\n\"", "answer_id": "5NQYftWw2vCtPJSk2uzv6W", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 762, "text": "<<>>: Computer Vision Video Classification\\n<<>>: TimesformerForVideoClassification.from_pretrained('fcakyon/timesformer-large-finetuned-k400')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries and classes, such as AutoImageProcessor, TimesformerForVideoClassification, numpy, and torch.\\n2. Load the pre-trained Timesformer model 'fcakyon/timesformer-large-finetuned-k400' which has been fine-tuned on the Kinetics-400 dataset for video classification tasks.\\n3. Process the video frames, which could be loaded from a file or acquired in real-time from a camera.\\n4. Pass the video frames to the processor, which in turn prepares the video inputs for the model.\\n5. Perform video classification using the Timesformer model, and obtain the predicted class index as the output.\\n6. Obtain the human-readable label for the predicted class using the model's config and print it out.\\n<<>>: from transformers import AutoImageProcessor, TimesformerForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 448, 448))\\nprocessor = AutoImageProcessor.from_pretrained('fcakyon/timesformer-large-finetuned-k400')\\nmodel = TimesformerForVideoClassification.from_pretrained('fcakyon/timesformer-large-finetuned-k400')\\ninputs = processor(images=video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "oWxafhrx9z3pLFZ8g9Y3WJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 763, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: ChineseCLIPModel.from_pretrained('OFA-Sys/chinese-clip-vit-large-patch14-336px')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, including PIL for image processing, requests for handling image URLs, and ChineseCLIPProcessor and ChineseCLIPModel from transformers.\\n2. Load the pre-trained ChineseCLIPModel using the 'OFA-Sys/chinese-clip-vit-large-patch14-336px' checkpoint.\\n3. Load the ChineseCLIPProcessor from the same checkpoint.\\n4. Open the image URL and convert it to RGB format using the PIL library.\\n5. Define the caption texts that need to be analyzed, such as \\\"\\u5ba2\\u751f\\u5d50\\u6bb5\\u4e86\\u751f\\u5b50\\u6bb5\\u6dd4\\u8fdb\\u7684\\u65c5\\\" for a cat or \\\"\\u5ba2\\u751f\\u5d50\\u6bb5\\u4e86\\u751f\\u5b50\\u6bb5\\u6dd4\\u8fdb\\u7684\\u65c5\\\" for a dog.\\n6. Create a list of the image and caption texts to analyze.\\n7. Process the image and caption texts using the ChineseCLIPProcessor.\\n8. Compute image and text features using the pre-trained ChineseCLIPModel.\\n9. Calculate the similarity score between image and text features.\\n10. Analyze the similarity scores to determine if the image includes a cat or a dog.\\n<<>>: from PIL import Image\\nimport requests\\nfrom transformers import ChineseCLIPProcessor, ChineseCLIPModel\\nmodel = ChineseCLIPModel.from_pretrained('OFA-Sys/chinese-clip-vit-large-patch14-336px')\\nprocessor = ChineseCLIPProcessor.from_pretrained('OFA-Sys/chinese-clip-vit-large-patch14-336px')\\nurl = 'https://your-image-url.jpg'\\n# replace the URL with the actual image URL\\nimage = Image.open(requests.get(url, stream=True).raw).convert('RGB')\\ncaption_texts = [\\\"\\u5ba2\\u751f\\u5d50\\u6bb5\\u4e86\\u751f\\u5b50\\u6bb5\\u6dd4\\u8fdb\\u7684\\u65c5\\\", \\\"\\u5ba2\\u751f\\u5d50\\u6bb5\\u4e86\\u751f\\u5b50\\u6bb5\\u6dd4\\u8fdb\\u7684\\u65c5\\\"]\\ninputs = processor(images=image, return_tensors='pt')\\nimage_features = model.get_image_features(**inputs)\\nimage_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\\ninputs = processor(text=caption_texts, padding=True, return_tensors='pt')\\ntext_features = model.get_text_features(**inputs)\\ntext_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\\ncosine_scores = (text_features * image_features).softmax(1).cpu().tolist()\\n# Analyze the scores to determine if the image includes a cat or a dog\\n\"", "answer_id": "4YWFs8irPkSbXkqiFstgKE", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 764, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='finiteautomata/bertweet-base-sentiment-analysis')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library 'pipeline' from the transformers package.\\n2. Use the 'pipeline' function to load the 'finiteautomata/bertweet-base-sentiment-analysis' model, which is a BERTweet model trained specifically for sentiment analysis tasks.\\n3. This model can be used to analyze sentiments in text, in this case, customer reviews.\\n4. The results can be used to understand the general sentiment of the reviews and guide business decisions accordingly.\\n5. The model is portable, so it can be used on mobile devices or in other remote locations where Internet connectivity is not available.\\n<<>>: from transformers import pipeline\\nsentiment_analyzer = pipeline('text-classification', model='finiteautomata/bertweet-base-sentiment-analysis')\\nreview_text = \\\"I love this product! It's incredibly versatile and easy to use.\\\"\\nresult = sentiment_analyzer(review_text)\\n\"", "answer_id": "Q3UMPoggLwF7K8RKYqzBMX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 765, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include 'pipeline' from transformers.\\n2. Instantiate the sentiment analysis model using the 'pipeline' function and the specified model 'bhadresh-savani/distilbert-base-uncased-emotion'. This model is trained on the IEMOCAP dataset and can classify text into 6 Ekman emotions (anger, disgust, fear, joy, sadness, and surprise) and a neutral class.\\n3. Pass the summary generated by the language model to the sentiment analysis model.\\n4. The model will return the sentiment scores for each emotion, which can be used to evaluate the overall sentiment of the review.\\n<<>>: from transformers import pipeline\\nsummary = \\\"The book was a fascinating journey through time and space, and the author's deep knowledge and passion for the subject shined through in every page.\\\"\\nnlp = pipeline('sentiment-analysis', model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)\\nsentiment_scores = nlp(summary)\\n\"", "answer_id": "Ub4n8q2EcunaTyaGNDnJ4A", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 766, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='cardiffnlp/twitter-xlm-roberta-base-sentiment')\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary library 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the pre-trained model 'cardiffnlp/twitter-xlm-roberta-base-sentiment', which is trained for sentiment analysis on tweets in English, Dutch, and German.\\n3. The loaded model will be used for sentiment analysis, which is a task where the model predicts the sentiment (positive or negative) of a given text.\\n4. Apply the sentiment analysis model to the movie reviews, to find out if they are positive or negative.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('sentiment-analysis', model='cardiffnlp/twitter-xlm-roberta-base-sentiment')\\nreview = \\\"I absolutely loved this movie! The characters were well-developed and the plot kept me engaged from start to finish.\\\"\\nresult = classifier(review)\"", "answer_id": "3qVUuAGbvmdCHdXfJWWTTG", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 767, "text": "To classify the emotion of a given text, you can use the \\\"emotion-english\\\" model from the distilbert-base-uncased-distilled-emotion package. This model has been fine-tuned on an English subset of the IEMO dataset and can classify text into seven emotions: anger, disgust, fear, joy, neutral, sadness, and surprise.\\nTo load this model, you can use the following code:\\n```python\\nfrom transformers import DistilBertTokenizer, DistilWav2Vec2Model, DistilRobertaModel, DistilEmotionModel\\ntokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\\nmodel = DistilWav2Vec2Model.from_pretrained('distilwav2vec2-base-uncased')\\nemotion_model = DistilEmotionModel.from_pretrained('distilbert-base-uncased-distilled-emotion')\\n```\\nAfter loading the required modules, you can use the `tokenizer` to tokenize and encode the input text.\\nNext, use the `model` to generate embeddings for the input text.\\nFinally, use the `emotion_model` to predict the emotion of the text, by passing the generated embeddings.\\n```python\\ntext = \\\"I am so happy to be finally taking a vacation.\\\"\\ntokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\\nmodel = DistilWav2Vec2Model.from_pretrained('distilwav2vec2-base-uncased')\\nemotion_model = DistilEmotionModel.from_pretrained('distilbert-base-uncased-distilled-emotion')\\ninputs = tokenizer(text, return_tensors='pt')\\noutputs = model(**inputs)\\nemotion_predictions = emotion_model(inputs['hidden_states'][0], outputs.logits.detach(), outputs.logits.numpy(), top_k=len(text.split()))\\n``\\nFor more information, please refer to the `emotion-english` model's documentation.\"", "answer_id": "FiVhhYFEKzzsZJzJTRBjFZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 768, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoTokenizer.from_pretrained('bert-base-uncased')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary components from the transformers library, including AutoTokenizer and AutoConfig.\\n2. We then use the from_pretrained method of the AutoTokenizer class to load the pre-trained tokenizer 'bert-base-uncased'.\\n3. We use the from_pretrained method of the AutoConfig class to load the pre-trained configuration 'potatobunny/results-yelp'.\\n4. We create a model using the tokenizer and configuration, which is capable of sentiment analysis on restaurant reviews.\\n5. The model can then be used to classify the sentiment of the reviews as either positive or negative.\\n<<>>: from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification\\ntokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\\nconfig = AutoConfig.from_pretrained('potatobunny/results-yelp')\\nmodel = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', config=config)\\n\"", "answer_id": "hBozKgxeGgSk9ReLAtka7H", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 769, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='joeddav/distilbert-base-uncased-go-emotions-student')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text classification model for emotion detection.\\n3. Load the model 'joeddav/distilbert-base-uncased-go-emotions-student', which is a student version of the emotion classification model described in the GoEmotions dataset.\\n4. Once the model is loaded, it can be used to classify user responses into different emotional categories, like happiness, sadness, anger, etc.\\n5. Using this emotion classification model, we can build a chatbot that can understand and respond to the user's emotions.\\n<<>>: from transformers import pipeline\\nemotion_classifier = pipeline('text-classification', model='joeddav/distilbert-base-uncased-go-emotions-student')\\nuser_response = \\\"I'm really happy today!\\\"\\nemotion_category = emotion_classifier(user_response)\"", "answer_id": "T62tny2pn5VsRJFCiLE4Av", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 770, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: pipeline('ner', model='dslim/bert-base-NER-uncased')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary library, 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model, 'dslim/bert-base-NER-uncased', which is designed for Named Entity Recognition (NER) tasks.\\n3. The loaded model will be used to identify and classify dates, organizations, and names within the provided text.\\n4. After the model is applied, the text will be processed to extract the named entities and return them in a structured format.\\n<<>>: from transformers import pipeline\\ntext = \\\"On September 1st, John Doe, a French startup founder, attends a conference in Berlin.\\\"\\nner_pipeline = pipeline('ner', model='dslim/bert-base-NER-uncased')\\nentities = ner_pipeline(text)\\nprint(entities)\"", "answer_id": "NtbnJnrv2GkGpUybutUkps", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 771, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\n<<>>: Transformers\\n<<>>:1. Import the necessary transformers library classes and functions, including AutoTokenizer, AutoModelForTokenClassification, and pipeline.\\n2. Create a tokenizer and token classification model using the 'Davlan/distilbert-base-multilingual-cased-ner-hrl' model. This model is pretrained for named entity recognition (NER) tasks and can identify various entities, including person names, locations, organizations, and other miscellaneous entities.\\n3. Use the pipeline function to create an NER pipeline, which combines the tokenizer and model. This pipeline can be used to extract entities from input text.\\n4. Apply the NER pipeline on news articles to extract important figures, organizations, and locations.\\n<<>>: from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\\ntokenizer = AutoTokenizer.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nmodel = AutoModelForTokenClassification.from_pretrained('Davlan/distilbert-base-multilingual-cased-ner-hrl')\\nner_pipeline = pipeline('ner', model=model, tokenizer=tokenizer)\\nnews_article = \\\"News article text here...\\\"\\nner_results = ner_pipeline(news_article)\\n\"", "answer_id": "JHzoYaBURVjU36xfgEHJ6r", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 772, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: pipeline('token-classification', model='kredor/punctuate-all')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model, which in this case is 'kredor/punctuate-all'.\\n3. The loaded model will be used for predicting punctuation marks in the given text.\\n4. The model can predict punctuation in the text provided and help the writer to improve the quality of his written draft.\\n<<>>: from transformers import pipeline\\npunctuator = pipeline('token-classification', model='kredor/punctuate-all')\\npunctuated_text = punctuator(writer_draft)\\n\"", "answer_id": "UkDhdiJNqkWom2qRjDXuTQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 773, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: SequenceTagger.load('flair/upos-english')\\n<<>>: Flair\\n<<>>:1. Import the necessary libraries, which are 'Sentence' and 'SequenceTagger' from the Flair package.\\n2. Load the pre-trained part-of-speech (POS) tagging model 'flair/upos-english'.\\n3. Create a Sentence object containing the text you want to analyze.\\n4. Use the 'predict()' method on the loaded model to identify the POS tags for each token in the Sentence object.\\n5. Iterate through the identified POS tags to extract the relevant information.\\n<<>>: from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\ntagger = SequenceTagger.load('flair/upos-english')\\ntext = \\\"This is a collection of texts we want to analyze for their part-of-speech tags.\\\"\\nsentence = Sentence(text)\\ntagger.predict(sentence)\\nfor entity in sentence.get_spans('pos'):\\n print(entity)\"", "answer_id": "KmvHaijrpq9efGP52sf5eS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 774, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wtq')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers and pandas packages. This includes BartForConditionalGeneration for the pre-trained model and DataFrame for processing tabular data.\\n2. We then use the from_pretrained method of the BartForConditionalGeneration class to load the pre-trained model 'microsoft/tapex-large-finetuned-wtq'. This model has been fine-tuned for table question answering tasks, which is exactly what we need for analyzing data stored in structured tables.\\n3. We prepare our table as a DataFrame object and provide a question we want to ask the model.\\n4. The model can then be used to analyze the table and generate an answer based on the question provided.\\n<<>>: from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-large-finetuned-wtq')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-large-finetuned-wtq')\\ntable = pd.DataFrame.from_dict(data) # Replace data with your table data\\nquestion = \\\"What is the highest salary?\\\"\\nencoding = tokenizer(table=table, query=question, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nanswer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\\n\"", "answer_id": "2zdQSBUnGhrgftSuDKg9we", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 775, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: TapasForQuestionAnswering.from_pretrained('google/tapas-large-finetuned-wikisql-supervised')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package. This includes TapasForQuestionAnswering for the table question answering model.\\n2. We then use the from_pretrained method of the TapasForQuestionAnswering class to load the pre-trained model 'google/tapas-large-finetuned-wikisql-supervised'. This model has been trained for table question answering tasks and can understand tables and answer questions related to the data in the table.\\n3. We can then pass the table in CSV format and the query you have to the model, which will process the data and return an answer to the query.\\n<<>>: from transformers import TapasForQuestionAnswering, TapasTokenizer\\nimport pandas as pd\\nmodel = TapasForQuestionAnswering.from_pretrained('google/tapas-large-finetuned-wikisql-supervised')\\ntokenizer = TapasTokenizer.from_pretrained('google/tapas-large-finetuned-wikisql-supervised')\\ntable = pd.read_csv('table.csv')\\ninputs = tokenizer(table=table, queries=query, padding='max_length', return_tensors='pt')\\noutputs = model(**inputs)\\npredicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach(), outputs.logits_aggregation.detach())\"", "answer_id": "FTxEqrg4YgixE43sZ2wSw9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 776, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('deepset/roberta-base-squad2')\\n<<>>: Transformers\\n<<>>:1. Import the required libraries, such as AutoModelForQuestionAnswering and AutoTokenizer from the transformers package.\\n2. Load the pre-trained model 'deepset/roberta-base-squad2' using the AutoModelForQuestionAnswering.from_pretrained method. This model is trained on the SQuAD 2.0 dataset for question answering.\\n3. Load the tokenizer for the model using AutoTokenizer.from_pretrained.\\n4. Define a function that takes a question and a context as input and returns the answer extracted from the context.\\n5. Tokenize the input question and context using the tokenizer.\\n6. Feed the tokenized input into the model and obtain the start and end indices of the answer in the input sequence.\\n7. Extract the answer from the tokenized input between the start and end indices.\\n<<>>: from transformers import AutoModelForQuestionAnswering, AutoTokenizer\\nmodel_name = 'deepset/roberta-base-squad2'\\nmodel = AutoModelForQuestionAnswering.from_pretrained(model_name)\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\ndef get_answer(question, context):\\n input_qa = tokenizer(question, context, return_tensors='pt')\\n output = model(**input_qa)\\n answer_start = torch.argmax(output.start_logits)\\n answer_end = torch.argmax(output.end_logits)\\n answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_qa['input_ids'][0][answer_start:answer_end]))\\n return answer\\nquestion = \\\"What is the main topic of the text?\\\"\\ncontext = \\\"The example text is about renewable energy sources, which are an environmentally friendly alternative to non-renewable sources like coal, oil, and natural gas.\\\"\\nanswer = get_answer(question, context)\\nprint(answer)\"", "answer_id": "hpzFsacMPDDSwXAjuR8RVh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 777, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='bigwiz83/sapbert-from-pubmedbert-squad2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a question-answering pipeline using the model 'bigwiz83/sapbert-from-pubmedbert-squad2', which is based on the SapBERT architecture specifically fine-tuned for the biomedical domain and derived from the PubMedBERT model.\\n3. You can then use this pipeline to query the model with the latest research articles and their findings on COVID-19.\\n4. The model will provide the answers to the questions based on the content of the research articles.\\n5. This will assist the healthcare professional in getting quick answers to their COVID-19 related questions.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='bigwiz83/sapbert-from-pubmedbert-squad2')\\nresult = qa_pipeline({'context': 'covid-19-research-article', 'question': 'What are the latest treatment methods?'})\\n\"", "answer_id": "Wa8zadAdLphd3sQTRAafRA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 778, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a visual question answering model that is capable of understanding images and answering questions based on the content of the image.\\n3. Specify the model 'microsoft/git-base-vqav2' to be loaded. This model has been trained on a large dataset of images and corresponding questions and answers, making it suitable for answering questions about a given recipe image.\\n4. The created model can be used to analyze a recipe image and answer questions posed by users based on the information available in the image.\\n<<>>: from transformers import pipeline\\nvqa_pipeline = pipeline('visual-question-answering', model='microsoft/git-base-vqav2')\\nanswer = vqa_pipeline(question=\\\"What is the main ingredient in the recipe?\\\", image_path=\\\"path/to/recipe_image.jpg\\\")\\n\"", "answer_id": "QMPwrxZkwSf4k87W4BnKpB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 779, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForTokenClassification.from_pretrained('DataIntelligenceTeam/eurocorpV4')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, which are 'AutoModelForTokenClassification' and 'AutoTokenizer'.\\n2. Use the 'from_pretrained' method to load the tokenizer and the model 'DataIntelligenceTeam/eurocorpV4'. This model is fine-tuned on the sroie dataset and is suitable for extracting answers from legal documents.\\n3. Tokenize the input text (legal document) using the tokenizer, and then use the model to extract answers for the given questions.\\n4. The output will be the most likely token(s) corresponding to the answer.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained('DataIntelligenceTeam/eurocorpV4')\\nmodel = AutoModelForTokenClassification.from_pretrained('DataIntelligenceTeam/eurocorpV4')\\ninput_text = \\\"Legal document text...\\\"\\nquestions = [\\\"What is the contract duration?\\\", \\\"Who are the parties involved?\\\"]\\nfor question in questions:\\n inputs = tokenizer(question, input_text, return_tensors='pt')\\n outputs = model(**inputs)\\n answer_index = outputs.logits.argmax().item()\\n answer = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0][answer_index])[0]\\n print(f\\\"Question: {question}\\\\nAnswer: {answer}\\\\n\\\")\"", "answer_id": "AkyB9JcdGoaboUKZXCgfAj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 780, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='sileod/deberta-v3-base-tasksource-nli')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the 'pipeline' function from the transformers library provided by Hugging Face.\\n2. Use the 'pipeline' function to load the model, which in this case is 'sileod/deberta-v3-base-tasksource-nli'. This model has been trained for zero-shot classification tasks, which is perfect for categorizing customer inquiries.\\n3. The loaded model will be used to classify the input text into one of the provided categories: 'sales', 'technical support', or 'billing'.\\n4. Provide the categories to the model and it will predict which category the customer inquiry falls under.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='sileod/deberta-v3-base-tasksource-nli')\\nquery = \\\"I am experiencing difficulty with the installation process of your software.\\\"\\ncategories = ['sales', 'technical support', 'billing']\\nresult = classifier(query, categories)\\nprint(result)\"", "answer_id": "PM3pjKL3tK4fekxfKrpSJi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 781, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Use the pipeline function to create a zero-shot classification model, which can classify text into predefined categories without requiring additional training data.\\n3. Specify the 'cross-encoder/nli-deberta-v3-xsmall' model to be loaded. This model is capable of classifying text into a number of categories based on semantic similarity.\\n4. The created classifier can be used to classify news articles into various categories like 'Politics', 'Sports', 'Technology', 'Business', and 'Entertainment'.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='cross-encoder/nli-deberta-v3-xsmall')\\nnews_categories = ['politics', 'sports', 'technology', 'business', 'entertainment']\\nclassification_result = classifier(news_article, candidate_labels=news_categories)\"", "answer_id": "9rAnQePdxBxdB3oVLybyK3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 782, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: classifier(model, candidate_labels, candidate_texts, hypothesis_template=hypothesis_template)\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, including the pipeline function from the transformers package.\\n2. Use the pipeline function to create a classifier with the 'Sahajtomar/German_Zeroshot'.\\n3. Set the hypothesis_template parameter to a German zero-shot classification template like 'In deisem geht es um {}.'\\n4. Use the classifier to classify German news articles by providing the model with the candidate_labels, candidate_texts, and hypothesis_template.\\n5. The classifier will then predict the most suitable category for each article based on the content.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='Sahajtomar/German_Zeroshot')\\nhypothesis_template = 'In deisem geht es um {}.'\\nresult = classifier(news_article, candidate_labels=['crime', 'tragedy', 'theft'] , candidate_texts=['Ein Selbstmord', 'Ein trag\\u00f6rdiger Selbstmord', 'Ein Diebstahl'], hypothesis_template=hypothesis_template)\\n\"", "answer_id": "FFWwDzj8NkwUAsGxxYpWXw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 783, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_fr_to_en', model='Helsinki-NLP/opus-mt-fr-en')\\n<<>>: Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a translation model by specifying the model's name 'Helsinki-NLP/opus-mt-fr-en', which is trained to translate French text to English.\\n3. Use the created translation model to translate the given French document into English by passing the French text as input.\\n4. The translated English text will be the output.\\n<<>>: from transformers import pipeline\\ntranslation_model = pipeline('translation_fr_to_en', model='Helsinki-NLP/opus-mt-fr-en')\\nfrench_text = \\\"Bonjour, comment \\u00e7a va?\\\"\\ntranslated_text = translation_model(french_text)\\nenglish_text = translated_text[0]['translation_text']\\n\"", "answer_id": "6izg6yAaw7ejPr2eazGyLK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 784, "text": "<<>>: Natural Language Processing Translation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-ru-en')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes and functions from the transformers library provided by Hugging Face.\\n2. Use the from_pretrained method to load the 'Helsinki-NLP/opus-mt-ru-en' model, which has been trained for translating Russian text to English.\\n3. With the created model, you can now translate Russian documents into English as needed for the business collaboration project.\\n4. Just feed the Russian text to the model, and it will return the translated English text.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-ru-en')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('Helsinki-NLP/opus-mt-ru-en')\\ntext = 'Russian text to be translated'\\ninputs = tokenizer.encode(text, return_tensors='pt')\\noutputs = model.generate(inputs)\\ntranslated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\"", "answer_id": "KoSmk5iLEkMLSyJigtbeHk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 785, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-newsroom')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import 'pipeline' from the transformers package.\\n2. Create a summarization pipeline using 'summarization' as the task and 'google/pegasus-newsroom' as the model.\\n3. This model is trained for summarizing news articles and is fine-tuned on a dataset of news articles from the Economist English Weekly. It performs well on various summarization tasks and can generate concise summaries of length 40.\\n4. Use the pipeline to generate a summary of the news article by passing the text of the article as input.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-newsroom')\\narticle_text = \\\"Long news article text here...\\\"\\nsummary = summarizer(article_text, max_length=40, min_length=30)\\n\"", "answer_id": "4A4S73SV6JgAm8EPBw6upv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 786, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-pubmed')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers package provided by Hugging Face.\\n2. Use the pipeline function to create a summarization model that can abridge the scientific article.\\n3. Specify the model 'google/pegasus-pubmed' to be loaded. This model is trained on biomedical publications and is suitable for summarizing scientific articles.\\n4. Provide the input text, which should include all the important points from the article.\\n5. The model will generate a concise summary of the content.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-pubmed')\\ninput_text = \\\"Insert your article's text here\\\"\\nsummary = summarizer(input_text, max_length=100, min_length=30, do_sample=False)\\nprint(summary[0]['summary_text'])\"", "answer_id": "ZdxkyEUbCgtQ2sLPFkEM7Y", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 787, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('summarization', model='lidiya/bart-large-xsum-samsum')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the 'pipeline' function from the transformers library.\\n2. Create a summarizer using the 'lidiya/bart-large-xsum-samsum' model, which is specifically designed for summarizing conversations.\\n3. Pass the conversation text to the summarizer and obtain the summarized text.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='lidiya/bart-large-xsum-samsum')\\nconversation = '''Hannah: Hey, do you have Betty's number?\\\\nAmanda: Lemme check\\\\nAmanda: Sorry, can't find it.\\\\nAmanda: Ask Larry\\\\nAmanda: He called her last time we were at the park together\\\\nHannah: I don't know him well\\\\nAmanda: Don't be shy, he's very nice\\\\nHannah: If you say so..\\\\nHannah: I'd rather you texted him\\\\nAmanda: Just text him \\ud83d\\ude42\\\\nHannah: Urgh.. Alright\\\\nHannah: Bye\\\\nAmanda: Bye bye\\\\n'''\\nsummary = summarizer(conversation)[0]['summary_text']\\n\"", "answer_id": "6HGWLeR7vDpTRVv6632vPp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 788, "text": "YouTube to remove videos containing false claims about approved vaccines, terminate anti-vaccine influencers under new policy. Tech giants criticized for not countering false health information. US President Biden appeals for social media platforms to address vaccination misinformation. YouTube removes 130,000 videos since implementing Covid vaccine misinformation ban. New policy covers long-approved vaccines, such as measles and hepatitis B. Guidelines based on local health authorities' safety and effectiveness confirmation, WHO support.\"", "answer_id": "N2HAQZAJjsrfnvtbHddZTQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 789, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('allenai/cosmo-xl')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'torch' and 'transformers'.\\n2. Load the pre-trained model 'allenai/cosmo-xl' using the AutoModelForSeq2SeqLM class, which is specialized in generating text-to-text responses.\\n3. Create a function 'generate' that takes a problem description and an optional solution as arguments, and then uses the loaded model to generate a response that can be a potential solution to the given problem.\\n4. The function will use the model's ability to mimic human-like language and reasoning to provide a plausible answer to the given problem.\\n<<>>: import torch\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\ntokenizer = AutoTokenizer.from_pretrained('allenai/cosmo-xl')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('allenai/cosmo-xl').to(device)\\ndef generate(problem, context):\\n if context: problem = '[{0}] {1}'.format(problem, context)\\n inputs = {'input_text': problem}\\n outputs = model.generate(inputs['input_text'], max_length=128, temperature=1.0, top_p=0.95, do_sample=True)\\n solution = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n return solution\\n# Example usage:\\nproblem = \\\"Cosmo is very lonely and doesn't have any friends.\\\"\\ncontext = \\\"Cosmo is an AI Assistant who can help you with daily life problems.\\\"\\nresponse = generate(problem, context)\\nprint(response)\"", "answer_id": "GSU94wYRKDEBJ6nD3etSQN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 790, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='mywateriswet/ShuanBot')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We will import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a conversational pipeline using the pipeline function and specify the model 'mywateriswet/ShuanBot' to be loaded. This model is based on the GPT-2 architecture and is designed for generating human-like responses in a chat-like environment.\\n3. The conversational pipeline can be used to generate responses based on user input. This can be integrated into the personal assistant we're building to enable it to have conversations with people.\\n<<>>: from transformers import pipeline\\nchat_pipeline = pipeline('conversational', model='mywateriswet/ShuanBot')\\nresponse = chat_pipeline('What is the capital of France?')\"", "answer_id": "HLEjbKcwr568U4bHX4EaSi", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 791, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelForCausalLM.from_pretrained('pygmalion-6b')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library, including AutoTokenizer and AutoModelForCausalLM.\\n2. Use the from_pretrained method to load the 'pygmalion-6b' model and tokenizer, which is specifically designed for dialogue generation tasks.\\n3. Define a function called \\\"generate\\\" that accepts a dialogue context, previously generated text, and an optional maximum length for the response.\\n4. Tokenize the input text and generate the response using the model.\\n5. Decode the generated tokens back to text format and return the response.\\n6. The model can refer to external knowledge during conversation by incorporating external IDs in the input text and using the model's self-attention-based structure to gather relevant information from previous messages.\\n7. The created chatbot can use this information to generate high-quality responses to users' questions or gardening tips.\\n<<>>: from transformers import AutoTokenizer, AutoModelForCausalLM\\ntokenizer = AutoTokenizer.from_pretrained('pygmalion-6b')\\nmodel = AutoModelForCausalLM.from_pretrained('pygmalion-6b')\\ndef generate(dialogue_context, previous_message_ids, max_length):\\n input_text = \\\"Gardening Tip: \\\" + dialogue_context + \\\"\\\\n\\\\n\\\"\\n input_ids = tokenizer.encode(input_text, return_tensors='pt')\\n output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, do_sample=True, top_k=10, top_p=0.92, temperature=0.6)\\n response = tokenizer.decode(output[0], skip_special_tokens=True)\\n return response\\n# Example usage:\\nresponse = generate(\\\"What is the best way to water a plant?\\\", [previous_message_ids], max_length=50)\\nprint(response)\"", "answer_id": "JAEVaRtSPasxbn9W5TDdCp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 792, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='distilgpt2')\\n<<>>: Transformers\\n<<>>: 1. We import the necessary functions from the transformers library provided by Hugging Face. This includes the pipeline function for creating a text generation model.\\n2. We then create a text generation model using the 'distilgpt2' model. This model has been pre-trained for generating creative text, which is exactly what we need for helping a friend with a creative paragraph bot.\\n3. With the model created, we can pass a starting text to it, and the model will generate creative and coherent ideas to improve the text.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='distilgpt2')\\ninitial_text = \\\"The paragraph is quite basic and needs improvement.\\\"\\ngenerated_ideas = text_generator(initial_text, max_length=50, num_return_sequences=3, top_p=0.95, top_k=60)\\n\"", "answer_id": "PMfv3DbfQKPdb3aABcyvCB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 793, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5ForConditionalGeneration.from_pretrained('t5-small')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes, T5Tokenizer and T5ForConditionalGeneration, from the transformers library by Hugging Face.\\n2. Load the pre-trained T5-Small tokenizer and model.\\n3. Tokenize the input article text with the loaded tokenizer.\\n4. Use the loaded model to generate a summary of the input text.\\n5. Decode the generated tokens back into human-readable text.\\n<<>>: from transformers import T5Tokenizer, T5ForConditionalGeneration\\nmodel = T5ForConditionalGeneration.from_pretrained('t5-small')\\ntokenizer = T5Tokenizer.from_pretrained('t5-small')\\ninput_text = \\\"Your input news article here...\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutput_ids = model.generate(input_ids)\\nsummary = tokenizer.decode(output_ids[0], skip_special_tokens=True)\\n\"", "answer_id": "KZD8PDrZqwJcLcEH2z3nKg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 794, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='Salesforce/codegen-350M-multi')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary 'pipeline' function from the transformers library provided by Hugging Face.\\n2. Create a code generation model using the pipeline function, specifying the model 'Salesforce/codegen-350M-multi'. This model is designed for generating code based on natural language descriptions of programs.\\n3. Pass the incomplete Python code to the model, and it will generate the missing parts of the code to complete the task.\\n4. The generated code can then be executed or further refined as needed.\\n<<>>: from transformers import pipeline\\ncode_generator = pipeline('text-generation', model='Salesforce/codegen-350M-multi')\\nincomplete_code = \\\"def hello_world():\\\"\\ngenerated_code = code_generator(incomplete_code)\\nprint(generated_code)\"", "answer_id": "VAsdgZFSY5ohfXNUfkDkFg", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 795, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='gpt2')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries from the transformers package, which includes the pipeline function for creating a text generation model.\\n2. Use the pipeline function to create a text-generation model based on the GPT-2 architecture.\\n3. Specify the model as 'gpt2' for the generation model.\\n4. Use a prompt related to eco-friendly kitchenware to generate marketing content.\\n5. The generated content can be used as a part of a larger AI copywriter project for creating marketing materials.\\n<<>>: from transformers import pipeline\\ngenerator = pipeline('text-generation', model='gpt2')\\nprompt = \\\"Create marketing content for eco-friendly kitchenware.\\\"\\nresult = generator(prompt, max_length=100, num_return_sequences=1)\\nkitchenware_content = result[0]['generated_text']\\n\"", "answer_id": "enC72NHC7DsCDxWJQhj6sy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 796, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: T5Model.from_pretrained('t5-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes T5Tokenizer and T5Model from the transformers library.\\n2. We create a tokenizer and a model using the T5-small pre-trained model. T5-small is a versatile model that can be used in various NLP tasks.\\n3. We tokenize the input diary text using the tokenizer and convert it into input_ids.\\n4. We use the model to generate a summary of the diary by passing the input_ids to the model with appropriate instructions.\\n5. The generated summary is decoded and returned.\\n<<>>: from transformers import T5Tokenizer, T5Model\\ntokenizer = T5Tokenizer.from_pretrained('t5-small')\\nmodel = T5Model.from_pretrained('t5-small')\\ninput_text = \\\"summarize: I am an astronaut in space, writing my diary every day. Today, I discovered a new planet and explored it. My diary is too long to share with my family, so please summarize it for me.\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutput_ids = model.generate(input_ids, max_length=150)\\nsummary = tokenizer.decode(output_ids[0], skip_special_tokens=True)\"", "answer_id": "8FsTWjBtUpe88wFvBdq6Wy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 797, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the required libraries, AutoTokenizer and AutoModelWithLMHead, from the transformers package.\\n2. Load the pre-trained tokenizer and model using the 'mrm8488/t5-base-finetuned-question-generation-ap' identifier.\\n3. Define the input text paragraphs that you want to use as a source for generating questions.\\n4. Tokenize the input text using the AutoTokenizer and convert it into input_ids.\\n5. Generate questions using the model and the input_ids, and then decode the resulting questions.\\n<<>>: from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')\\nmodel = AutoModelWithLMHead.from_pretrained('mrm8488/t5-base-finetuned-question-generation-ap')\\ninput_text = \\\"\\\"\\\"Your paragraph here...\\\"\\\"\\\"\\ninput_ids = tokenizer(input_text, return_tensors=\\\"pt\\\").input_ids\\ngenerated_output = model.generate(input_ids, max_length=128)\\nquestions = tokenizer.decode(generated_output[0], skip_special_tokens=True)\\n\"", "answer_id": "DrjhyiYz8TduCNSPMAGv78", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 798, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='distilroberta-base')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary pipeline function from the transformers library.\\n2. Create a fill-mask pipeline using the 'distilroberta-base' model. This is a smaller, faster, and lighter version of the RoBERTa-base model trained on the OpenWebTextCorpus, which is designed for masked language modeling tasks like completeing missing words in a given text.\\n3. Provide the incomplete paragraph with a '' placeholder where the missing word(s) should be.\\n4. Call the fill_mask function with the input text and get the results, which will contain the most likely words to fill in the '' token.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='distilroberta-base')\\nincomplete_paragraph = \\\"This novel is about a writer who plans to write some paragraphs but is unable to find the exact word placeholder or missing word.\\\"\\nresults = unmasker(incomplete_paragraph)\\nmissing_word = results[0]['token_str']\"", "answer_id": "FnFUoRNkS4e5VTnRtvnaox", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 799, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='facebook/opt-1.3b')\\n<<>>: Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which is 'facebook/opt-1.3b' in this case.\\n3. The loaded model will be used for text generation, which is a task where the model creates content based on given input.\\n4. Provide a text prompt to the model and it will generate a sentence or set of sentences that can be used to create interactive conversation.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='facebook/opt-1.3b')\\nprompt = \\\"What's your favorite movie?\\\"\\ngenerated_text = text_generator(prompt, max_length=50, do_sample=True, top_k=50, top_p=0.95, temperature=0.8)\\nprint(generated_text[0]['generated_text'])\"", "answer_id": "ZtRxHe4uHHMgtXSAxt22Pp", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 801, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, install the 'sentence-transformers' package using pip.\\n2. Import SentenceTransformer from the sentence_transformers package.\\n3. Initialize the SentenceTransformer model with the pre-trained model 'sentence-transformers/distilbert-base-nli-stsb-mean-tokens'.\\n4. Encode the input sentences using the model's 'encode' method to obtain embeddings representing the sentences.\\n5. Compare the embeddings of the two sentences to determine their similarity. Highly similar sentences will have closely related embeddings, and this information can be used to group news articles based on their content.\\n<<>>: from sentence_transformers import SentenceTransformer\\nsentences = [\\\"This is an example sentence\\\", \\\"Each sentence is converted\\\"]\\nmodel = SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\\nembeddings = model.encode(sentences)\\n\"", "answer_id": "bwg9pYxiQhRSnKQejEmPvN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 802, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='typeform/distilbert-base-uncased-mnli')\\n<<>>: Transformers\\n<<>>:1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a zero-shot classification model, which is capable of classifying text into predefined categories even if it has not been explicitly trained on those categories.\\n3. We specify the model 'typeform/distilbert-base-uncased-mnli' to be loaded. This is a model that has been trained on a large corpus of text and can classify text into several categories based on semantic similarity.\\n4. The created classifier can then be used to analyze customers' reviews and classify them into positive, neutral, or negative categories.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='typeform/distilbert-base-uncased-mnli')\\nresult = classifier(review_text, candidate_labels=['positive', 'neutral', 'negative'])\"", "answer_id": "RbafoEigt54giwusf6wvdW", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 803, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the sentence-transformers library using 'pip install -U sentence-transformers'.\\n2. Import the SentenceTransformer class from the sentence_transformers library.\\n3. Create a SentenceTransformer model by loading the pre-trained model 'sentence-transformers/all-mpnet-base-v2'.\\n4. Encode the provided question and potential answer sentences using the model. This converts the text into embeddings, which are dense vector representations of the text.\\n5. Calculate the cosine similarity between the question and each answer sentence's embedding.\\n6. Rank the answer sentences based on their cosine similarity scores from highest to lowest.\\n7. Return the answer sentences in the sorted order based on their relevance.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nimport numpy as np\\nmodel = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')\\nquestion_embedding = model.encode(question)\\ndeflection_embeddings = model.encode(deflection_sentences)\\nquestion_cosines = cosine_similarity(question_embedding, reflection_embeddings)\\ndeflection_scores = cosine_similarity(question_embedding, reflection_embeddings)\\n# Sort the scores in descending order\\ndef sorted_scores(scores):\\n scores = np.array(scores)\\n sorted_scores = sorted(scores[::-1], reverse=True)\\n return np.concatenate([scores, sorted_scores], axis=1)\\nsorted_scores = sorted_scores(deflection_scores)\\nanswer_sentences = [deflection[i] for i, _ in enumerate(sorted_scores) if i > 0]\\n\"", "answer_id": "SRNSznpHL6o9DMPufKaah7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 804, "text": "<<>>: Audio Text-to-Speech\\n<<>>: AutoModelForCausalLM.from_pretrained('espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804')\\n<<>>: ESPnet\\n<<>>:1. Import the necessary classes from the transformers package. This includes AutoModelForCausalLM for text-to-speech synthesis.\\n2. Use the from_pretrained method of the AutoModelForCausalLM class to load the pre-trained model 'espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804'. This model has been fine-tuned for Japanese text-to-speech tasks.\\n3. Use the loaded model to convert the Japanese text script into spoken Japanese. This can then be used for the audiobook production project.\\n<<>>: from transformers import AutoModelForCausalLM\\ntts_model = AutoModelForCausalLM.from_pretrained('espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804')\\ntext_script = \\\"Japanese text script here...\\\"\\n# Convert text script to spoken Japanese using tts_model\\n\"", "answer_id": "eV4VRNVBYLyuBHKtXKjXo8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 805, "text": "<<>>: Audio Text-to-Speech\\n<<>>: HIFIGAN.from_hparams(source='padmalcom/tts-hifigan-german', savedir=tmpdir_vocoder)\\n<<>>: speechbrain\\n<<>>: 1. Import the necessary libraries and classes like Tacotron2 and HIFIGAN.\\n2. Load the pre-trained Tacotron2 model for the German language from the source 'padmalcom/tts-tacotron2-german'.\\n3. Load the pre-trained HIFIGAN vocoder model for the German language from the source 'padmalcom/tts-hifigan-german'.\\n4. Encode the text with the Tacotron2 model to produce mel spectrograms.\\n5. Use the HIFIGAN model to convert the mel spectrograms into audio waveforms.\\n6. Save the generated waveforms as .wav files for further usage in the promotional videos.\\n<<>>: import torchaudio\\nfrom speechbrain.pretrained import Tacotron2, HIFIGAN\\ntacotron2 = Tacotron2.from_hparams(source='padmalcom/tts-tacotron2-german')\\nhifi_gan = HIFIGAN.from_hparams(source='padmalcom/tts-hifigan-german')\\ntext = \\\"Welche Produkte haben wir f\\u00fcr Deutschsprachige?\\\"\\nmel_output, mel_length, alignment = tacotron2.encode_text(text)\\nwaveforms = hifi_gan.decode_batch(mel_output)\\ntorchaudio.save('sample_voiceover.wav', waveforms.squeeze(1), 22050)\"", "answer_id": "K3DtqebnJ8AjfCkkNzfdJZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 806, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: pyannote.audio\\n<<>>: 1. Install the 'pyannote.audio' library if it's not already installed.\\n2. Import the 'Pipeline' class from the 'pyannote.audio' library.\\n3. Use the 'from_pretrained' method to create a new pipeline object with the 'pyannote/speaker-diarization@2.1' model.\\n4. Process the audio file using the pipeline to obtain speaker diarization results, which will identify and segment the speech of various speakers in the recording.\\n5. Save the diarization results in the RTTM (Rich Transcription Time Marked) format.\\n<<>>: from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained('pyannote/speaker-diarization@2.1', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\ndiarization = pipeline('audio.wav')\\nwith open('audio.rttm', 'w') as rttm:\\n diarization.write_rttm(rttm)\\n\"", "answer_id": "G9snm8bkBWbwzswdyz5aih", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 807, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2Model.from_pretrained('jonatasgrosman/wav2vec2-large-xlsr-53-arabic')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary class from the huggingsound package. This includes SpeechRecognitionModel for the speech recognition model.\\n2. Use the from_pretrained method of the Wav2Vec2Model class to load the pre-trained model 'jonatasgrosman/wav2vec2-large-xlsr-53-arabic'. This model has been fine-tuned for Arabic speech recognition, which is exactly what we need for transcribing Arabic speakers' speech.\\n3. Provide the model with the speech samples, which can be obtained from the audio files or the audio input device.\\n4. The model will then transcribe the Arabic speech to text, which can be processed further or used directly for various tasks.\\n<<>>: from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel('jonatasgrosman/wav2vec2-large-xlsr-53-arabic')\\naudio_paths = ['/path/to/arabic_speech1.mp3', '/path/to/arabic_speech2.wav']\\ntranscriptions = model.transcribe(audio_paths)\\n\"", "answer_id": "hXNqnF3NswWwWfhiq3PkSt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 808, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-xlsr-53-espeak-cv-ft')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers package, including Wav2Vec2Processor for processing audio data and Wav2Vec2ForCTC for the automatic speech recognition model.\\n2. Load the pre-trained model using the from_pretrained method of the Wav2Vec2ForCTC class, with the model name 'facebook/wav2vec2-xlsr-53-espeak-cv-ft'.\\n3. Load the audio file from the conference and use the Wav2Vec2Processor to convert it into a format that can be fed into the model.\\n4. Feed the processed audio into the model and retrieve the predicted transcription as text.\\n<<>>: from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC\\nimport torch\\nprocessor = Wav2Vec2Processor.from_pretrained('facebook/wav2vec2-xlsr-53-espeak-cv-ft')\\nmodel = Wav2Vec2ForCTC.from_pretrained('facebook/wav2vec2-xlsr-53-espeak-cv-ft')\\naudio_data = load_audio_file('audio_file_path.wav')\\n# Replace 'audio_file_path.wav' with the path to your conference audio file\\ninput_values = processor(audio_data, return_tensors='pt').input_values\\nlogits = model(input_values).logits\\npredicted_ids = torch.argmax(logits, dim=-1)\\ntranscription = processor.batch_decode(predicted_ids)\"", "answer_id": "PAUvF5r4cd4En86Uui5PH9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 809, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TT')\\n<<>>: Fairseq\\n<<>>: 1. Import the required libraries and functions from fairseq and huggingface_hub.\\n2. Load the pre-trained model 'facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS' using the load_model_ensemble_and_task_from_hf_hub function.\\n3. The loaded model is a Text-to-Speech model trained on the Hokkien language, and it takes a text input in English and outputs the audio representation of the text.\\n4. Use the model to convert spoken English text to spoken Hokkien audio by passing the text as input to the model.\\n<<>>: from fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech import CodeHiFiGANVocoder\\nfrom fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\\nimport IPython.display as ipd\\ncache_dir = 'facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS'\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub(cache_dir=cache_dir)\\nvocoder_cfg = cfg['model']\\nvocoder = CodeHiFiGANVocoder(models[0], vocoder_cfg)\\ntts_model = VocoderHubInterface(vocoder_cfg, vocoder)\\ntext = \\\"Baek maak jing baa jing\\\"\\nsample = tts_model.get_model_input(text)\\nwav, sr = tts_model.get_prediction(sample)\\nipd.Audio(wav, rate=sr)\\n\"", "answer_id": "c36UwP5BHYwQxuy9p8AN6i", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 810, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create an audio source separation pipeline using the model 'Awais/Audio_Source_Separation'. This model has been trained for audio source separation tasks, which is what we need to separate the speaker sources from the original audio file.\\n3. Feed the audio file into the pipeline, and it will return separated audio sources, representing each speaker's voice separately.\\n4. This can then be used to filter the noise and improve audio quality.\\n<<>>: from transformers import pipeline\\naudio_separator = pipeline('audio-source-separation', model='Awais/Audio_Source_Separation')\\nseparated_sources = audio_separator(audio_file)\\n\"", "answer_id": "KvCavEcZTk8APRsC6LrdAM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 811, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/wav2vec2-base-superb-er')\\n<<>>: PyTorch Transformers\\n<<>>: 1. Import the necessary libraries, including 'pipeline' from transformers and 'librosa' to load the audio file.\\n2. Load the pre-trained emotion recognition model using the 'pipeline' function and specifying the model name 'superb/wav2vec2-base-superb-er'.\\n3. The loaded model will be used for detecting emotions in the given audio file.\\n4. Use the 'classifier' object to predict the emotion class of the audio file. It will return the top emotions along with their probabilities.\\n<<>>: from transformers import pipeline\\nimport librosa\\nclassifier = pipeline('audio-classification', model='superb/wav2vec2-base-superb-er')\\naudio_file = 'path_to_audio_file.wav'\\nwaveform, _ = librosa.load(audio_file, sr=16000)\\nresult = classifier(waveform, top_k=5)\"", "answer_id": "UXeewVdMbszeQuMvdk7owZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 812, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: Model.from_pretrained('pyannote/brouhaha', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the 'pyannote-audio' library.\\n2. Import the Model class from the 'pyannote.audio' package.\\n3. Load the pre-trained 'pyannote/brouhaha' model using the Model.from_pretrained() method.\\n4. Create an instance of the Inference class from the 'pyannote.audio' package.\\n5. For each audio file in the conference call, use the inference to determine voice activity and the user's speech.\\n<<>>: from pyannote.audio import Model, Inference\\nmodel = Model.from_pretrained('pyannote/brouhaha', use_auth_token='ACCESS_TOKEN_GOES_HERE')\\ninference = Inference(model)\\nvoice_activity_detection = inference('audio.wav')\\n# replace 'audio.wav' with the path to your conference call audio file\\n\"", "answer_id": "JhXahYDLrqcSNGe8V4xJm2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 813, "text": "<<>>: Audio Voice Activity Detection\\n<<>>: Pipeline.from_pretrained('philschmid/pyannote-speaker-diarization-endpoint')\\n<<>>: pyannote.audio\\n<<>>: 1. Import the Pipeline class from the pyannote.audio library.\\n2. Load the pre-trained speaker diarization model \\\"philschmid/pyannote-speaker-diarization-endpoint\\\" using the Pipeline.from_pretrained() method.\\n3. Apply the model to the conference call recording's waveform to obtain speaker diarization results in the form of a timeline.\\n4. Iterate through the diarization results and extract speaker turns, durations, and labels to identify the speakers and segments of the conversation they participated in.\\n<<>>: from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained('philschmid/pyannote-speaker-diarization-endpoint')\\ndiarization = pipeline('audio_file.wav') # replace 'audio_file.wav' with path to your conference call recording\\nfor turn, duration, label in diarization:\\n # iterate through diarization results to analyze conversation\\n\"", "answer_id": "Bjcyndio2oV649eeXEL8kD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 814, "text": "<<>>: Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the required libraries: joblib, json, and pandas.\\n2. Load the pre-trained model using joblib.load() with 'model.joblib' as the argument. The model is trained to predict the negative impact on the environment based on input features.\\n3. Load the config.json file containing the list of features to be used as input for the model.\\n4. Read the input data from a CSV file (data.csv) and preprocess it by selecting the required features and renaming the columns as required.\\n5. Pass the preprocessed input data to the model and predict the negative impact on the environment.\\n<<>>: import joblib\\nimport json\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\\n\"", "answer_id": "LrmMDjMQhu8hfNfwKxg92A", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 815, "text": "<<>>: Tabular Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the required libraries, which are 'json', 'joblib', and 'pandas'.\\n2. Load the pre-trained model using joblib.load() and load the configuration file that lists the features to be used in the model.\\n3. Read the dataset from the CSV file using pandas.\\n4. Apply the column selection from the config file to the dataset.\\n5. Pass the selected features to the model and use the 'predict' function to classify the CO2 emissions into high or low emissions categories.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\"", "answer_id": "ADP24QRbU8a4z6to7bd4gU", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 816, "text": "<<>>: Tabular Tabular Classification\\n<<>>: AutoModel.from_pretrained('datadmg/autotrain-test-news-44534112235')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import necessary libraries such as joblib and pandas. This includes joblib for loading the model, and pandas for handling the CSV data.\\n2. We then load the pre-trained model 'datadmg/autotrain-test-news-44534112235', which was trained on news article categorization tasks and can be used for classifying tabular data.\\n3. We load the CSV file containing the data that needs to be classified.\\n4. We process the CSV data using pandas and select the appropriate features listed in the 'config.json' file.\\n5. We use the loaded model to predict the categories of the input data and print the results.\\n<<>>: import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\nconfig = json.load(open('config.json'))\\nfeatures = config['features']\\ndata = pd.read_csv('data.csv')\\ndata = data[features]\\ndata.columns = ['feat_' + str(col) for col in data.columns]\\npredictions = model.predict(data)\"", "answer_id": "EngfpGnHb3idH2iwvJrRvh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 817, "text": "<<>>: Tabular Tabular Regression\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the required libraries joblib and pandas.\\n2. Load the pre-trained carbon emissions model using joblib.\\n3. Read the data from a CSV file containing information about the device to estimate its carbon emissions.\\n4. Extract the required features ('features' in the example code) from the data and preprocess it according to the model's requirements.\\n5. Use the loaded model to predict the carbon emissions for the given device data.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('device_data.csv')\\nfeatures = ['idle_power', 'standby_power', 'active_power']\\ndata = data[features]\\npredictions = model.predict(data)\\n\"", "answer_id": "7KZiSRr7rKK6xf96q7RJUj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 818, "text": "<<>>: Reinforcement Learning\\n<<>>: mlagents-load-from-hf --repo-id='0xid/poca-SoccerTwos' --local-dir='./downloads'\\n<<>>: ML-Agents\\n<<>>: 1. To create an intelligent learning-based soccer player, we will use the pre-trained '0xid/poca-SoccerTwos' model from the ML-Agents library.\\n2. We will download the model using the 'mlagents-load-from-hf' command, which will save the model in a local directory (e.g., 'downloads').\\n3. We can then use this model to make decisions in our virtual reality soccer simulation, making the environment more engaging and challenging for players.\\n<<>>: # Download the model\\n!mlagents-load-from-hf --repo-id='0xid/poca-SoccerTwos' --local-dir='./downloads'\\n# Use the model in your soccer simulation\\n# Additional code to integrate the model into your simulation is required.\"", "answer_id": "3EUBpFWHegVgMEJF4g9HbT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 819, "text": "<<>>: Multimodal Graph Machine Learning\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('janpase97/codeformer-pretrained')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including AutoTokenizer and AutoModelForSeq2SeqLM from the transformers package.\\n2. Load the pre-trained model 'janpase97/codeformer-pretrained' using the AutoModelForSeq2SeqLM.from_pretrained method.\\n3. Create a tokenizer using the AutoTokenizer.from_pretrained method, with the same model name.\\n4. Write a function that accepts a dictionary of image-text pairs and their corresponding performance metrics, and uses the loaded model and tokenizer to generate new combinations of images and headlines.\\n5. Iterate through the dictionary, creating new combinations using the loaded model and tokenizer, and updating the performance metrics for each combination.\\n6. Return the dictionary with the updated performance metrics for each combination of images and headlines.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained('janpase97/codeformer-pretrained')\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('janpase97/codeformer-pretrained')\\ndef generate_combinations(current_combinations, image_dict, text_dict, performance_metrics):\\n new_combinations = []\\n for image, text in current_combinations:\\n inputs = tokenizer(image=image, text=text, return_tensors=\\\"pt\\\")\\n outputs = model.generate(**inputs)\\n performance_metrics[\\\"image\\\" + str(image)] = performance_metrics[\\\"image\\\" + str(image)] + 1\\n performance_metrics[\\\"text\\\" + str(text)] = performance_metrics[\\\"text\\\" + str(text)] + 1\\n new_combinations.append((image, text, outputs))\\n return new_combinations\\ncombinations = generate_combinations([(image1, headline1, output1)], image_dict, text_dict, performance_metrics)\\n\"", "answer_id": "4dVQ2qfEGnB8orCdLN28b5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 820, "text": "<<>>: Reinforcement Learning\\n<<>>: PPO.load_from_hub('araffin/ppo-LunarLander-v2', 'ppo-LunarLander-v2.zip')\\n<<>>: Stable-Baselines3\\n<<>>: 1. First, import the load_from_hub function from the huggingface_sb3 package and the PPO class from the stable_baselines3 package. \\n2. Load the pre-trained model 'araffin/ppo-LunarLander-v2' from the Hugging Face model hub using the load_from_hub function.\\n3. Create an instance of the PPO class using the loaded model.\\n4. You can now use the PPO instance to play the game and test the module by feeding it the relevant input and analyzing its output.\\n<<>>: from huggingface_sb3 import load_from_hub\\nfrom stable_baselines3 import PPO\\nmodel_path = load_from_hub('araffin/ppo-LunarLander-v2', 'ppo-LunarLander-v2.zip')\\nmodel = PPO.load(model_path)\\n\"", "answer_id": "E5ZkSQPFBcGouspSUv84dJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 821, "text": "<<>>: Reinforcement Learning\\n<<>>: mlagents-load-from-hf --repo-id='Raiden-1001/poca-SoccerTwosv2' --local-dir='./downloads'\\n<<>>: Unity ML-Agents Library\\n<<>>:1. Install the 'ml-agents' library in your Python environment.\\n2. Download the pre-trained model 'Raiden-1001/poca-SoccerTwosv2' using the mlagents-load-from-hf command. Place the downloaded model in a local directory (e.g., 'downloads').\\n3. Import the 'ml-agents' library and use the 'mlagents.load_from_hf' function to load the downloaded model.\\n4. You can now use this model to play the SoccerTwos game, incorporating advanced strategies that take advantage of learning during gameplay.\\n<<>>: from mlagents import load_from_hf\\nmodel = load_from_hf('Raiden-1001/poca-SoccerTwosv2', './downloads')\\n\"", "answer_id": "nMzdcHNgreP8eFKf5WsD46", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 822, "text": "<<>>: Reinforcement Learning\\n<<>>: mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n<<>>: ML-Agents\\n<<>>: 1. Install the required libraries, which include unity-ml-agents and deep-reinforcement-learning.\\n2. Use the provided mlagents-load-from-hf command to download the pre-trained model, 'Raiden-1001/poca-Soccerv7.1', which is designed to play SoccerTwos.\\n3. Place the downloaded model in a local directory (e.g., 'downloads').\\n4. Configure the game environment and set up the AI agent using the provided Unity ML-Agents SDK and configuration file.\\n5. Train the AI agent by running the training command with the provided configuration file and run_id.\\n6. Once the AI agent is trained, it can be deployed in the game environment to play SoccerTwos.\\n<<>>: # Execute the following commands in your terminal\\n# Install required libraries\\n!pip install unity-ml-agents deep-reinforcement-learning\\n# Download the pre-trained model\\n!mlagents-load-from-hf --repo-id='Raiden-1001/poca-Soccerv7.1' --local-dir='./downloads'\\n# Configure the game environment and ML agent\\n!mlagents-learn --run-id= --resume\\n# Train the AI agent\\n!mlagents-learn --run-id= --resume --run\\n# Deploy the AI agent in the game environment\\n!mlagents-play --run-id=\\n\"", "answer_id": "KGVmdc9jQ2ZBt4rP4Ygnm7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 823, "text": "<<>>: Natural Language Processing Feature Extraction\\n<<>>: AutoModel.from_pretrained('indobenchmark/indobert-base-p1')\\n<<>>: Hugging Face Transformers\\n<<>>:1. First, import the necessary classes from the transformers package. This includes the BertTokenizer and AutoModel classes.\\n2. Use the from_pretrained method of the BertTokenizer and AutoModel classes to load the pre-trained model 'indobenchmark/indobert-base-p1'.\\n3. The loaded model is designed for the Indonesian language, and it can be used to extract features from text.\\n4. Create a function that takes text input related to Indonesia and processes it with the loaded model. This will generate contextualized representations of the text that can be used for various tasks, such as providing information to tourists.\\n5. Implement the function and use it to extract meaningful features from text related to Indonesia.\\n<<>>: from transformers import BertTokenizer, AutoModel\\ntokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p1')\\nmodel = AutoModel.from_pretrained('indobenchmark/indobert-base-p1')\\ntext = 'Welcome to Indonesia! We have many attractions for you, such as Bali, Borobudur, and Komodo Island. Enjoy your visit.'\\ninputs = tokenizer(text, return_tensors='pt')\\noutputs = model(**inputs)\\nfeatures = outputs.last_hidden_state\\n\"", "answer_id": "4BFc28LtYQrncQxoPYq4s3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 824, "text": "<<>>: Computer Vision Object Detection\\n<<>>: DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which include DetrFeatureExtractor, DetrForObjectDetection, Image, and requests.\\n2. Use the from_pretrained method of the DetrForObjectDetection class to load the pre-trained model 'facebook/detr-resnet-101'. This model has been trained for object detection tasks in images.\\n3. Load the image from the real-time feed using the Image library.\\n4. Extract features from the image using the DetrFeatureExtractor.\\n5. Pass the extracted features to the object detection model, which will analyze the image and return the detected objects and their coordinates.\\n<<>>: from transformers import DetrFeatureExtractor, DetrForObjectDetection\\nimport torch\\nfrom PIL import Image\\nimport requests\\nurl = 'http://path_to_your_image_url'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-101')\\nmodel = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-101')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "WJRN5DDcAQwYGj9SyurJ2m", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 825, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('facebook/blenderbot-1B-distill')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers library provided by Hugging Face.\\n2. We use the from_pretrained method to load the 'facebook/blenderbot-1B-distill' model, which is designed for generating text-to-text responses in a conversational manner.\\n3. We can then use this model to generate responses for user questions about tourist attractions.\\n4. The chatbot can answer questions based on the provided information to provide a personalized experience for users.\\n<<>>: from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\\nmodel = AutoModelForSeq2SeqLM.from_pretrained('facebook/blenderbot-1B-distill')\\ntokenizer = AutoTokenizer.from_pretrained('facebook/blenderbot-1B-distill')\\ninput_text = \\\"Tell me about the best tourist attractions in Paris.\\\"\\ninputs = tokenizer(input_text, return_tensors='pt')\\noutputs = model.generate(inputs['input_ids'])\\ndecoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n\"", "answer_id": "koRUx5HmL2kskAamsSeQA6", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 826, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: EncoderDecoderModel.from_pretrained('microsoft/GODEL-v1_1-base-multilingual-encoder-decoder')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers package, including the EncoderDecoderModel class.\\n2. We then use the from_pretrained method of the EncoderDecoderModel class to load the pre-trained model 'microsoft/GODEL-v1_1-base-multilingual-encoder-decoder'. This model has been trained for multilingual sentence encoding and understanding, which is exactly what we need for developing a chatbot capable of handling multiple languages.\\n3. Once the model is loaded, it can be used to encode sentences in multiple languages and understand user input in various languages.\\n<<>>: from transformers import AutoTokenizer, EncoderDecoderModel\\ntokenizer = AutoTokenizer.from_pretrained('microsoft/GODEL-v1_1-base-multilingual-encoder-decoder')\\nmodel = EncoderDecoderModel.from_pretrained('microsoft/GODEL-v1_1-base-multilingual-encoder-decoder')\"", "answer_id": "cQhoAoVHWG52aQp2b3s36W", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 827, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-cifar10-32')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, we import the necessary class from the diffusers package, which is DDPMPipeline for unconditional image generation.\\n2. We then use the from_pretrained method of the DDPMPipeline class to load the pre-trained model 'google/ddpm-cifar10-32'. This model has been trained for unconditional image synthesis tasks, which is exactly what we need for generating images of 3D printing examples.\\n3. After loading the model, we can generate an image by calling the model and accessing the image in the first position of the resulting tensor.\\n4. Finally, we can save the generated image as a file, which can be used as a demo for the 3D printing company.\\n<<>>: from diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-cifar10-32')\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_image.png')\\n\"", "answer_id": "Af6G3uE5BVJUCWjvoidAiA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 828, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text2text-generation model.\\n3. Specify the model 'salesforce/blip2-opt-6.7b' to be loaded. This is a large language model trained for multimodal tasks, including image-to-text generation.\\n4. The created model can be used to analyze an image of a painting and answer questions about it based on the image's content.\\n5. The professor can provide questions as input, along with the image, and the model will generate a textual response based on the painting's content.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('text2text-generation', model='salesforce/blip2-opt-6.7b')\\nquestion = \\\"What is the main color of the painting?\\\"\\nimage_path = \\\"path/to/painting_image.jpg\\\"\\nresponse = chatbot(question, image_path)\\nprint(response)\"", "answer_id": "CFkvDwq7S5VXtnVmyd5DXS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 829, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-chartqa-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, including Pix2StructForConditionalGeneration and Pix2StructProcessor.\\n2. Load the pre-trained model 'google/pix2struct-chartqa-base' using Pix2StructForConditionalGeneration.from_pretrained().\\n3. Load the associated processor using Pix2StructProcessor.from_pretrained().\\n4. To answer a question about a chart or diagram, provide the image file path and the text of the question to the processor. The processor will then convert the image and question into the required input format for the model.\\n5. Generate an answer using the model and the processed input. The output will be a structured format containing the answer to the question.\\n6. Decode the answer using the processor's decode method, skipping special tokens.\\n<<>>: from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor\\nmodel = Pix2StructForConditionalGeneration.from_pretrained('google/pix2struct-chartqa-base')\\nprocessor = Pix2StructProcessor.from_pretrained('google/pix2struct-chartqa-base')\\nimage_path = 'path/to/image.jpg'\\nquestion = 'What is the total revenue for Q1?'\\ninputs = processor(image=image_path, text=question, return_tensors='pt')\\noutput = model.generate(**inputs)\\nanswer = processor.decode(output[0], skip_special_tokens=True)\"", "answer_id": "U393QrhYc2ZHbPa36h5dHv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 830, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b', torch_dtype=torch.float16, variant=fp16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the necessary classes and functions from the diffusers package.\\n2. Load the pre-trained model 'damo-vilab/text-to-video-ms-1.7b' using the from_pretrained method of the DiffusionPipeline class. The model is designed to generate videos based on given text prompts.\\n3. Set the pipeline's scheduler and enable CPU offloading for the model.\\n4. Generate the video by passing the text prompt \\\"a man walking in the city\\\" to the pipeline with specified number of inference steps.\\n5. Save the generated video to a file.\\n<<>>: import torch\\nfrom diffusers import DiffusionPipeline, DPMSolverMultistepScheduler\\nfrom diffusers.utils import export_to_video\\npipeline = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b', torch_dtype=torch.float16, variant='fp16')\\npipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)\\npipeline.enable_model_cpu_offload()\\nprompt = \\\"a man walking in the city\\\"\\ngenerator = pipeline(prompt, num_inference_steps=25)\\nvideo_frames = generator.frames\\nvideo_path = export_to_video(video_frames)\\n\"", "answer_id": "JXgggjzhHh2zF8YKTKfdtk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 831, "text": "<<>>: Multimodal Text-to-Video\\n<<>>: TuneAVideoPipeline.from_pretrained('nitrosocke/mo-di-diffusion', unet=UNet3DConditionModel.from_pretrained('Tune-A-Video-library/mo-di-bear-guitar', subfolder='unet', torch_dtype=torch.float16), torch_dtype=torch.float16)\\n<<>>: Hugging Face\\n<<>>: 1. Import the required libraries and modules, such as tuneavideo, TuneAirModel, TuneAIDiffusionModel, TuneAVideoPipeline, UNet3DConditionModel, and torch.\\n2. Initialize the TuneAVideoPipeline using the pretrained models and the UNet3DConditionModel, providing the required GPU if available.\\n3. Set the video_length, height, width, num_inference_steps, and guidance_scale as required.\\n4. Call the pipeline with the textual prompt, a video file, and the additional parameters to generate the video.\\n5. Save the generated video in the specified output file.\\n<<>>: import torch\\nfrom tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline\\nfrom tuneavideo.models.unet import UNet3DConditionModel\\nfrom tuneavideo.models.mo_di_py_diffusers import TuneAIDiffusionModel, TuneAVideoPipeline\\nfrom tuneavideo.util import save_videos_grid\\nfrom tuneavideo.util import get_new_video_path\\nfrom tuneavideo.models.mo_di_py_diffusers import TuneAIDiffusionModel\\nfrom tuneavideo.util import save_videos_grid\\nimport re\\ntext = \\\"Spider-Man is water skiing\\\"\\nunet = UNet3DConditionModel.from_pretrained('Tune-A-Video-library/mo-di-bear-guitar', subfolder='unet', torch_dtype=torch.float16).to('cuda')\\npipe = TuneAVideoPipeline.from_pretrained('nitrosocke/mo-di-diffusion', unet=unet, torch_dtype=torch.float16).to('cuda')\\npipe.enable_xformers_memory_efficient_attention()\\nprompt = text\\nvideo = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos\\nsave_videos_grid(video, f'./{prompt}.gif')\\nvideo_path = get_new_video_path('Spider-Man_water_skiing.gif')\\nsave_videos_grid(video, video_path)\"", "answer_id": "UkhgSfsL2mXQzMWCYEYBC9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 832, "text": "<<>>: Multimodal Image-to-Text\\n<<>>: pipeline('text-generation', model='azwierzc/vit_base_patch16_224_pl_v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a text-generation model.\\n3. Specify the model 'azwierzc/vit_base_patch16_224_pl_v2' to be loaded. This model is trained on the Polish language dataset and can be used for generating textual captions for Polish images.\\n4. The created model can be used to generate captions by providing an image as input. This can be useful for creating travel guides with Polish language captions for images.\\n<<>>: from transformers import pipeline\\ncaption_generator = pipeline('text-generation', model='azwierzc/vit_base_patch16_224_pl_v2')\\ncaption = caption_generator(image_path)\\n\"", "answer_id": "XPgMhZBZ5tCFG8iDAD8Bhc", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 833, "text": "<<>>: Multimodal Visual Question Answering\\n<<>>: pipeline('visual-question-answering', model='JosephusCheung/GuanacoVQA')\\n<<>>: Hugging Face\\n<<>>: 1. We first import the necessary pipeline function from the transformers package.\\n2. We create a visual question answering model using the pipeline function and specify the 'visual-question-answering' task along with the model 'JosephusCheung/GuanacoVQA'.\\n3. This model can then be used to answer questions about an image. To use the model, provide the image and a question about the image as inputs to the model.\\n4. The model will analyze the image and the question and provide a response based on the image's content.\\n<<>>: from transformers import pipeline\\nvqa_pipeline = pipeline('visual-question-answering', model='JosephusCheung/GuanacoVQA')\\nresponse = vqa_pipeline(image='path/to/image.jpg', question='What is in the image?')\"", "answer_id": "J2eBHVBjTJYBaTuxmoYbbf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 834, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries and classes, including AutoModelForDocumentQuestionAnswering and AutoTokenizer from the transformers package.\\n2. Load the pre-trained model 'tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa' using the from_pretrained method of the AutoModelForDocumentQuestionAnswering class. This model is designed for extracting information from documents and is fine-tuned on an unknown dataset.\\n3. Load the tokenizer for the model using AutoTokenizer.from_pretrained.\\n4. Use the tokenizer to convert the OCR text from the property listing scan into a format the model can understand.\\n5. Pass the tokenized input to the model and retrieve the answers to the questions related to critical information in the document.\\n<<>>: from transformers import AutoModelForDocumentQuestionAnswering, AutoTokenizer\\nmodel = AutoModelForDocumentQuestionAnswering.from_pretrained('tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\ntokenizer = AutoTokenizer.from_pretrained('tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\nocr_text = \\\"OCR text from property listing scan here...\\\"\\ninputs = tokenizer(ocr_text, questions=\\\"What are the critical pieces of information in this document?\\\", return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "AFHTtCejMra3oeyf4Z5kVK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 835, "text": "<<>>: Multimodal Graph Machine Learning\\n<<>>: AutoModel.from_pretrained('clefourrier/graphormer-base-pcqm4mv2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers library provided by Hugging Face, including the AutoModel class.\\n2. We use the from_pretrained method of the AutoModel class to load the pre-trained Graphormer model 'clefourrier/graphormer-base-pcqm4mv2'. This model has been specifically designed for graph classification tasks and graph representation tasks, which is exactly what we need for predicting molecular properties in the drug discovery domain.\\n3. Before using the model, we may need to preprocess the input data to match the format that the Graphormer model expects.\\n4. Once the model is set up and preprocessed data is fed into it, the Graphormer model can be used to make predictions on molecular properties that are crucial in the drug discovery process.\\n<<>>: from transformers import AutoModel\\ngraphormer = AutoModel.from_pretrained('clefourrier/graphormer-base-pcqm4mv2')\\n# Ensure that your input data matches the Graphormer model's expectations\\n# After preprocessing, feed the data into the graphormer for predicting molecular properties\"", "answer_id": "VCzpA94bk6BTs6yxSAqt6k", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 836, "text": "1. Import the required libraries and pretrained models, such as 'AutoModel' from transformers.\\n2. Use the 'model.config.depth_scale' attribute to adjust the scaling factor for the depth estimation model.\\n3. Load an input image using any image processing library, such as 'torchvision.transforms.Compose([...])'.\\n4. Convert the input image to a tensor and pass it through the pretrained model, which will return the predicted depth map.\\n5. Convert the predicted depth map back to an image using 'torchvision.transforms.ToTensor()'.\\n6. Finally, save the depth map as an output image file.\\n###Code:\\n```python\\nfrom transformers import AutoModel\\nimport torch\\nfrom torchvision import transforms\\nmodel = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221215-112116')\\nmodel.config.depth_scale = 1.5\\ninput_image = 'path/to/image.jpg'\\ntransform = transforms.Compose([\\n transforms.Resize((640, 480)),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\\n])\\ninput_tensor = transform(input_image).unsqueeze(0)\\npredictions = model(input_tensor)\\ndepth_map = predictions.squeeze(0).detach().cpu().numpy()\\noutput_image = transform.from_tensor(depth_map).squeeze().cpu().numpy()\\noutput_image[0].save('depth_map.png')\\n```\"", "answer_id": "LC2te54W7NAV2Vm53CMPxS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 837, "text": "<<>>: Computer Vision Depth Estimation\\n<<>>: AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221215-112116')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library AutoModel from transformers, which provides pre-trained models.\\n2. Load the pre-trained depth estimation model 'sayakpaul/glpn-nyu-finetuned-diode-221215-112116' using the AutoModel.from_pretrained() method. This model is fine-tuned on the diode-subset dataset and provides depth estimation for objects in an image.\\n3. Use the loaded model to estimate the depth of objects in the robot's environment. This can be used to evaluate the distance between the robot and other objects, as well as to determine the robot's surroundings.\\n<<>>: from transformers import AutoModel\\ndepth_estimator = AutoModel.from_pretrained('sayakpaul/glpn-nyu-finetuned-diode-221215-112116')\"", "answer_id": "XCZAnwdPsnPpPofHWLTW3p", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 838, "text": "<<>>: Computer Vision Image Classification\\n<<>>: pipeline('image-classification', model='fxmarty/resnet-tiny-beans')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the pre-trained model 'fxmarty/resnet-tiny-beans', which has been trained for the task of image classification.\\n3. This model can then be used to classify images into different categories based on the species of plants in the image.\\n4. You can provide an image to the model and specify the plant species as the class_names parameter. The model will then return the predicted species for each plant in the image.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('image-classification', model='fxmarty/resnet-tiny-beans')\\nimage_path = 'path/to/plant_image.jpg'\\n# replace 'path/to/plant_image.jpg' with the path to your image\\nresults = classifier(image_path, class_names='species_of_plants')\\n\"", "answer_id": "Qoho7KbWYHgLCnGNUD3ceA", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 839, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ConvNextForImageClassification.from_pretrained('facebook/convnext-base-224')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the required classes, which are 'ConvNextFeatureExtractor' and 'ConvNextForImageClassification' from the transformers library.\\n2. Load the 'facebook/convnext-base-224' model using the 'from_pretrained' method of 'ConvNextForImageClassification'.\\n3. Use the 'ConvNextFeatureExtractor.from_pretrained' method to load the feature extractor for the model.\\n4. Create a dataset with images of cats and dogs split evenly.\\n5. Use the feature extractor to preprocess the images and convert them into a format suitable for the model.\\n6. Classify the images using the model and obtain the logits of the predicted class values.\\n7. Convert the logits to actual classes using the argmax function and return the predicted class.\\n<<>>: from transformers import ConvNextFeatureExtractor, ConvNextForImageClassification\\nimport torch\\nfrom datasets import load_dataset\\ndataset = load_dataset('huggingface/cats-image')\\nimage = dataset['test']['image'][0]\\nfeature_extractor = ConvNextFeatureExtractor.from_pretrained('facebook/convnext-base-224')\\nmodel = ConvNextForImageClassification.from_pretrained('facebook/convnext-base-224')\\ninputs = feature_extractor(image, return_tensors='pt')\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\npredicted_label = logits.argmax(-1).item()\\nprint(model.config.id2label[predicted_label])\"", "answer_id": "J2ZtYnnXnmV3MXRnMQXZBf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 840, "text": "<<>>: Computer Vision Image Classification\\n<<>>: ViTForImageClassification.from_pretrained('google/vit-base-patch16-224-in21k')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers package, such as ViTForImageClassification for the image classification model.\\n2. Load the pre-trained Vision Transformer (ViT) model using the from_pretrained method with the provided model name 'google/vit-base-patch16-224-in21k'. This model has been trained on the ImageNet-21k dataset and can be used for image classification tasks.\\n3. Process the input image using the image transformation function provided by the Hugging Face Transformers library.\\n4. Use the loaded model to classify the transformed image into its respective category.\\n5. The output will provide the class label and confidence score for the given image.\\n<<>>: from transformers import ViTImageProcessor, ViTForImageClassification\\nfrom PIL import Image\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with the path to your image\\nmodel = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224-in21k')\\nprocessor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')\\ninputs = processor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "ARCRYWdDRz3rEFNBLtz2ys", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 841, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='hf-tiny-model-private/tiny-random-CLIPSegModel')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a zero-shot image classification model.\\n3. Specify the model 'hf-tiny-model-private/tiny-random-CLIPSegModel' to be loaded. This model is designed for zero-shot image classification tasks and can be used to classify social media images into various categories.\\n4. The created classifier can be used to classify social media images into categories like 'happy', 'sad', 'angry', 'surprised', etc., based on the visual content of the images.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('zero-shot-image-classification', model='hf-tiny-model-private/tiny-random-CLIPSegModel')\\nimage_path = 'path/to/image.jpg'\\nclass_names = ['happy', 'sad', 'angry', 'surprised', 'other']\\nclassification_result = image_classifier(image_path, class_names)\"", "answer_id": "2uFw8ZfQsoPzr6MQz8JwYk", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 842, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('zero-shot-image-classification', model='Bingsu/clip-vit-base-patch32-ko')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Bingsu/clip-vit-base-patch32-ko'.\\n3. The loaded model will be used for zero-shot image classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. Provide an image file path and a list of candidate car brand names to the model for classification.\\n5. The model will return the most likely car brand based on the image.\\n<<>>: from transformers import pipeline\\nfrom PIL import Image\\nimage_classifier = pipeline('zero-shot-image-classification', model='Bingsu/clip-vit-base-patch32-ko')\\nimage = Image.open('path_to_image.jpg')\\n# replace 'path_to_image.jpg' with the path to your image\\ncar_brands = ['Toyota', 'Honda', 'Ford', 'Chevrolet', 'BMW', 'Mercedes']\\nresult = image_classifier(image, car_brands)\\nbest_brand = result['labels'][0]\"", "answer_id": "aQPPmsC5c6DGNf6q8Vntsf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 843, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8s-csgo-player-detection')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the required Python packages: ultralyticsplus and ultralytics.\\n2. Import the YOLO class from the ultralyticsplus library.\\n3. Create a YOLO object detection model with the pre-trained 'keremberke/yolov8s-csgo-player-detection' model, optimized for detecting Counter-Strike: Global Offensive players.\\n4. Set the model's configuration parameters: confidence, IoU threshold, NMS, and maximum detections.\\n5. Provide the model with an image URL or filepath containing a live game of CS:GO.\\n6. Use the model's predict method to detect players in the image and display the results.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8s-csgo-player-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\n\"", "answer_id": "n3uHSYhK8vmES9aTbAynUV", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 844, "text": "<<>>: Computer Vision Object Detection\\n<<>>: OwlViTForObjectDetection.from_pretrained('google/owlvit-base-patch32')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers and PIL packages. This includes OwlViTForObjectDetection for the text-conditioned object detection model and Image for processing image data.\\n2. We then use the from_pretrained method of the OwlViTForObjectDetection class to load the pre-trained model 'google/owlvit-base-patch32'. This model has been trained for zero-shot text-conditioned object detection tasks.\\n3. We load the image data from a file or a camera.\\n4. We can then use this model to analyze an image and detect objects based on the text conditions given, such as 'a photo of a suspicious object'.\\n5. The detected objects will be presented as a bounding box with their class label, and the analysis can help in security tasks like detecting suspicious objects and people.\\n<<>>: from transformers import OwlViTProcessor, OwlViTForObjectDetection\\nfrom PIL import Image\\nimport requests\\nurl = 'http://path_to_image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nprocessor = OwlViTProcessor.from_pretrained('google/owlvit-base-patch32')\\nmodel = OwlViTForObjectDetection.from_pretrained('google/owlvit-base-patch32')\\ntexts = [\\\"a photo of a suspicious object\\\"]\\ninputs = processor(text=texts, images=image, return_tensors='pt')\\noutputs = model(**inputs)\\ntarget_sizes = torch.Tensor([image.size[::-1]])\\nresults = processor.post_process(outputs=outputs, target_sizes=target_sizes)\"", "answer_id": "jcd5EhiC82cKfXCdjSVVFL", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 845, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: OwlViTForImageClassification.from_pretrained('google/owlvit-base-patch32')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library, such as OwlViTProcessor and OwlViTForImageClassification.\\n2. Use the OwlViTProcessor.from_pretrained method to create an instance of the processor with the specified model checkpoint.\\n3. Use the OwlViTForImageClassification.from_pretrained method to create an instance of the image classification model with the specified model checkpoint.\\n4. Prepare the text descriptions and the image to be classified.\\n5. Use the processor to create the input tensors based on the text and image.\\n6. Use the OwlViTForImageClassification model to make a prediction for the image based on the provided text descriptions.\\n7. Obtain the probabilities and index of the highest prediction.\\n8. Print the predicted class label using the model's config and the predicted index.\\n<<>>: from transformers import OwlViTProcessor, OwlViTForImageClassification\\nimport torch\\nprocessor = OwlViTProcessor.from_pretrained('google/owlvit-base-patch32')\\nmodel = OwlViTForImageClassification.from_pretrained('google/owlvit-base-patch32')\\ntexts = ['a photo of a cat', 'a photo of a dog']\\nimage = \\ninputs = processor(text=texts, images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\"", "answer_id": "Kg2Xa5iYsoVcUFt9L6CmGX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 846, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-blood-cell-detection')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the YOLO class from the ultralyticsplus package, which includes a pre-trained YOLO model for detecting blood cells in microscopic images of blood samples.\\n2. Create an instance of the YOLO model using the 'keremberke/yolov8m-blood-cell-detection' pre-trained model.\\n3. Configure the model's parameters, such as confidence threshold, IoU threshold, and maximum detections.\\n4. Load the microscopic image of the blood sample, which can be in the format of 'path/to/image.jpg' or any other compatible image file.\\n5. Run the model's 'predict()' method on the image to get the detected blood cell bounding boxes.\\n6. Visualize the detected blood cells using the 'render_result()' function.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-blood-cell-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path/to/blood_sample_image.jpg'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\"", "answer_id": "V9hXbGovPjWWurXn87YHmT", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 847, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers and PIL packages. This includes YolosForObjectDetection for the object detection model and Image for processing image data.\\n2. Use the from_pretrained method of the YolosForObjectDetection class to load the pre-trained model 'hustvl/yolos-tiny'. This model has been trained for object detection tasks, which is exactly what we need for detecting vehicles in traffic camera images.\\n3. Load the image data from a file or a real-time camera feed.\\n4. This model can then be used to analyze an image and identify the various objects, including vehicles, in it.\\n<<>>: from transformers import YolosForObjectDetection\\nfrom PIL import Image\\nimage = Image.open('image_path.jpg')\\n# replace 'image_path.jpg' with path to your image\\nmodel = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "MMbE4Xdhfur86tuihoPwTs", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 848, "text": "<<>>: Computer Vision Object Detection\\n<<>>: YOLO('keremberke/yolov8m-csgo-player-detection')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, such as 'YOLO' and 'render_result' from the ultralyticsplus package.\\n2. Load the pre-trained model 'keremberke/yolov8m-csgo-player-detection' using the YOLO function.\\n3. Set the model's detection parameters, such as confidence threshold, IoU threshold, and maximum number of detections.\\n4. Use the 'predict' method of the model to detect players in the provided image path.\\n5. Print the results, which include the bounding boxes of the detected players and their confidence scores.\\n6. Optionally, use the 'render_result' function to visualize the detection results.\\n<<>>: from ultralyticsplus import YOLO, render_result\\nmodel = YOLO('keremberke/yolov8m-csgo-player-detection')\\nmodel.overrides['conf'] = 0.25\\nmodel.overrides['iou'] = 0.45\\nmodel.overrides['agnostic_nms'] = False\\nmodel.overrides['max_det'] = 1000\\nimage = 'path/to/your/csgo_image.jpg'\\nresults = model.predict(image_path)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()\\n\"", "answer_id": "4ZTCmx2nawvq7Gp7BzXKjB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 849, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: SegformerForSemanticSegmentation.from_pretrained('mattmdjaga/segformer_b2_clothes')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import necessary libraries, which include 'AutoFeatureExtractor', 'SegformerForSemanticSegmentation' from transformers, 'Image' from PIL, and 'requests'.\\n2. Load the 'mattmdjaga/segformer_b2_clothes' model using 'SegformerForSemanticSegmentation.from_pretrained()' function.\\n3. Use the 'AutoFeatureExtractor.from_pretrained()' function to load the necessary extractors for this model.\\n4. Open an image using the 'Image.open()' function from the PIL library and process it using the feature extractor.\\n5. Pass the input tensors to the model and obtain segmentation logits.\\n6. Post-process the output to obtain the final segmented image, which can be used to identify clothing items within the image.\\n<<>>: from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = AutoFeatureExtractor.from_pretrained('mattmdjaga/segformer_b2_clothes')\\nmodel = SegformerForSemanticSegmentation.from_pretrained('mattmdjaga/segformer_b2_clothes')\\nurl = 'http://example.com/image.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\\n\"", "answer_id": "DuX4ZZCqTbcgLLmEApNY66", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 850, "text": "<<>>: Computer Vision Image Segmentation\\n<<>>: SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b5-finetuned-ade-640-640')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers, PIL, and requests packages. This includes SegformerForSemanticSegmentation for the image segmentation model and Image for processing image data.\\n2. We then use the from_pretrained method of the SegformerForSemanticSegmentation class to load the pre-trained model 'nvidia/segformer-b5-finetuned-ade-640-640'. This model has been fine-tuned on the ADE20K dataset for semantic segmentation tasks, which is exactly what we need for identifying segments of an image captured by an autonomous vehicle's camera.\\n3. We load the image data from a URL or from a file, if available.\\n4. This model can then be used to analyze an image and segment it into different segments, such as buildings, roads, vehicles, etc.\\n<<>>: from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = SegformerFeatureExtractor.from_pretrained('nvidia/segformer-b5-finetuned-ade-640-640')\\nmodel = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b5-finetuned-ade-640-640')\\nurl = 'http://images.cocodataset.org/val2017/000000039769.jpg'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors='pt')\\noutputs = model(**inputs)\\nlogits = outputs.logits\"", "answer_id": "EwNeYwmiME2kJKcyLZRAbf", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 851, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-mlsd')\\n<<>>: Hugging Face\\n<<>>: 1. We first import the necessary classes from the diffusers package and PIL library. This includes ControlNetModel for the image-to-image transformation model and Image for processing image data.\\n2. We then use the from_pretrained method of the ControlNetModel class to load the pre-trained model 'lllyasviel/sd-controlnet-mlsd'. This model has been trained for tasks like image-to-image transformation, which is exactly what we need for converting floor plan images into simple line drawings.\\n3. We load the floor plan image data from a file or a URL.\\n4. This model can then be used to process the input image, transforming it into a simple line drawing that can be used for visualizing properties.\\n<<>>: from diffusers import ControlNetModel\\nfrom PIL import Image\\nimage = Image.open('floor_plan_image.jpg')\\n# replace 'floor_plan_image.jpg' with path to your floor plan image\\nmodel = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-mlsd')\\n# process the image using the model and save the output as 'simple_line_drawing.png'\\n# replace 'simple_line_drawing.png' with path to save the transformed image\"", "answer_id": "QRq9YraJHeYGyfQhcVcsTt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 852, "text": "<<>>: Multimodal Text-to-Image\\n<<>>: StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', vae='AutoencoderKL.from_pretrained(stabilityai/sd-vae-ft-mse)')\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary classes from the diffusers package, including AutoencoderKL and StableDiffusionPipeline.\\n2. Load the pre-trained model 'CompVis/stable-diffusion-v1-4' using the from_pretrained method of StableDiffusionPipeline.\\n3. Load the fine-tuned VAE 'stabilityai/sd-vae-ft-mse' as the Variational Autoencoder (VAE) using the from_pretrained method of the AutoencoderKL class.\\n4. Pass the VAE as an argument to the stable-diffusion-v1-4 model.\\n5. Now you can use this model to generate images based on textual scene descriptions, taking into account the actual positions and poses of objects in the scene.\\n<<>>: from diffusers.models import AutoencoderKL\\nfrom diffusers import StableDiffusionPipeline\\nmodel = 'CompVis/stable-diffusion-v1-4'\\nvae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse')\\npipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)\\n\"", "answer_id": "CfjPUmjZWKgTwL4fpXzagP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 853, "text": "<<>>: Computer Vision Image-to-Image\\n<<>>: Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries including Swin2SRForConditionalGeneration from the transformers package and the Image module from the PIL package.\\n2. Load the Swin2SR model using the from_pretrained method with the provided model name 'condef/Swin2SR-lightweight-x2-64'. This model is designed for lightweight image super-resolution tasks and can upscale images by a factor of 2.\\n3. Load the low-resolution image using the Image module from the PIL package.\\n4. Pass the low-resolution image through the Swin2SR model to obtain the upscaled image.\\n5. Save the upscaled image to a file or display it as needed.\\n<<>>: from transformers import Swin2SRForConditionalGeneration\\nfrom PIL import Image\\nmodel = Swin2SRForConditionalGeneration.from_pretrained('condef/Swin2SR-lightweight-x2-64')\\nlr_image = Image.open('low_resolution_image.jpg')\\nupscaled_image = model.predict(lr_image)\\nupscaled_image.save('upscaled_image.jpg')\\n\"", "answer_id": "GL6DxLRdPsfG6RMsMaByCn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 854, "text": "<<>>: Computer Vision Unconditional Image Generation\\n<<>>: DDPMPipeline.from_pretrained('google/ddpm-cat-256')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Install the 'diffusers' package to work with the DDPMPipeline.\\n2. Import the DDPMPipeline class from the diffusers package.\\n3. Load the pre-trained 'google/ddpm-cat-256' model with DDPMPipeline.from_pretrained() method, which is a model that generates images of cats.\\n4. Generate an original cat image using the loaded model.\\n5. Save the generated image to 'ddpm_generated_cat_image.png', which can be used for the animal shelter's fundraising event poster.\\n<<>>: !pip install diffusers\\nfrom diffusers import DDPMPipeline\\nddpm = DDPMPipeline.from_pretrained('google/ddpm-cat-256')\\nimage = ddpm().images[0]\\nimage.save('ddpm_generated_cat_image.png')\\n\"", "answer_id": "cncDatgJgrBjLvxNUATguZ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 855, "text": "<<>>: Computer Vision Video Classification\\n<<>>: TimesformerForVideoClassification.from_pretrained('facebook/timesformer-hr-finetuned-k400')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries: AutoImageProcessor, TimesformerForVideoClassification, numpy, and torch.\\n2. Load the pre-trained video classification model 'facebook/timesformer-hr-finetuned-k400' using TimesformerForVideoClassification.from_pretrained().\\n3. Create a video data processor using the AutoImageProcessor class.\\n4. Use the processor to preprocess the video frames and convert them into the appropriate format for the model input.\\n5. Use the model to classify the video into one of the given categories (sports, comedy, or news) by calling its `forward` method with the preprocessed inputs.\\n6. Retrieve the predicted class index and print the corresponding category label.\\n<<>>: from transformers import AutoImageProcessor, TimesformerForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(8, 3, 224, 224))\\nprocessor = AutoImageProcessor.from_pretrained('facebook/timesformer-hr-finetuned-k400')\\nmodel = TimesformerForVideoClassification.from_pretrained('facebook/timesformer-hr-finetuned-k400')\\ninputs = processor(images=video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\\\"Predicted class:\\\", model.config.id2label[predicted_class_idx])\"", "answer_id": "J7fLLMiwCXHe8KzsatZfQz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 856, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers package, which includes VideoMAEImageProcessor and VideoMAEForVideoClassification.\\n2. Load the pre-trained model 'MCG-NJU/videomae-base-finetuned-kinetics' for video classification tasks.\\n3. Create a video preprocessor object using VideoMAEImageProcessor.from_pretrained().\\n4. Use the preprocessor to process a given workout video, returning pixel values.\\n5. Create a VideoMAEForVideoClassification instance using the loaded model.\\n6. Use the instance to classify the workout video by passing the pixel values as input.\\n7. Get the predicted class index and corresponding label from model.config.id2label.\\n8. The resulting classification can be used to offer customized workout plans based on the types of movements in the workout video.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\"", "answer_id": "Ph9PmJkVGxn4ozmJiaJZF7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 857, "text": "<<>>: Computer Vision Video Classification\\n<<>>: VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes VideoMAEImageProcessor for processing the video frames and VideoMAEForVideoClassification for the video classification model.\\n2. We then use the from_pretrained method of the VideoMAEForVideoClassification class to load the pre-trained model 'MCG-NJU/videomae-base-finetuned-kinetics'. This model has been trained for video classification tasks and can be used to analyze CCTV footage for suspicious activities.\\n3. We load the video frames and prepare them using the VideoMAEImageProcessor.\\n4. This model can then be used to analyze the video frames and classify the activity occurring in the video.\\n<<>>: from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nnum_frames = 16\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\nmodel = VideoMAEForVideoClassification.from_pretrained('MCG-NJU/videomae-base-finetuned-kinetics')\\ninputs = processor(video, return_tensors='pt')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint('Predicted class:', model.config.id2label[predicted_class_idx])\"", "answer_id": "Tc5ADbnkytz9NxJonj7Gvw", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 858, "text": "<<>>: Computer Vision Zero-Shot Image Classification\\n<<>>: pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft')\\n<<>>: Hugging Face\\n<<>>:1. Import the necessary pipeline function from the transformers library provided by Hugging Face.\\n2. Create an image classification model using the pipeline function.\\n3. Specify the model 'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft' to be loaded. This model is designed for zero-shot image classification tasks and can classify images into a set of predefined categories.\\n4. Use the created classifier to classify the image of the city park, playground, and lake surrounded by trees and skyscrapers.\\n<<>>: from transformers import pipeline\\nimage_classifier = pipeline('image-classification', model='laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft')\\nclassification_result = image_classifier(image_path_or_url)\\n\"", "answer_id": "4JfYe3okYM9KzKDzW4Ho3M", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 859, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='papluca/xlm-roberta-base-language-detection')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which is 'papluca/xlm-roberta-base-language-detection'.\\n3. This model is designed to classify text into languages, so it will first detect the language of user input before providing a response.\\n4. The model outputs the detected language, and that can be used as a basis for further processing and generating appropriate responses.\\n<<>>: from transformers import pipeline\\nlanguage_detector = pipeline('text-classification', model='papluca/xlm-roberta-base-language-detection')\\nuser_input = 'Hello, how are you?'\\ndetected_language = language_detector(user_input)\\n\"", "answer_id": "CEU3D3zgRTTTquzxbCjW4m", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 860, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='cardiffnlp/twitter-xlm-roberta-base-sentiment')\\n<<>>: Hugging Face\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a sentiment analysis model by loading the 'cardiffnlp/twitter-xlm-roberta-base-sentiment' model.\\n3. This model is designed for sentiment analysis on multilingual texts, and it is trained on a dataset of ~198 million tweets in 8 languages.\\n4. The created sentiment classifier can be used to analyze the sentiment of customer reviews in the dataset.\\n<<>>: from transformers import pipeline\\nsentiment_analyzer = pipeline('sentiment-analysis', model='cardiffnlp/twitter-xlm-roberta-base-sentiment')\\nreview_sentiment = sentiment_analyzer(customer_review_text)\"", "answer_id": "7R4tx6ocRGfzgqwMGDdiR9", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 861, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('af1tang/personaGPT')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library. This includes the AutoTokenizer and AutoModelForCausalLM classes.\\n2. Use the from_pretrained method to load the pre-trained model 'af1tang/personaGPT'. This model is designed for open-domain conversational tasks, which is perfect for collecting movie reviews.\\n3. Define a prompt for the conversation, such as \\\"What are your thoughts on the movie 'The Wolf of Wall Street'?\\\"\\n4. Use the tokenizer to encode the prompt and create input_ids to feed into the model.\\n5. Generate a response from the model and decode the output to obtain a human-readable review.\\n<<>>: from transformers import AutoTokenizer, AutoModelForCausalLM\\ntokenizer = AutoTokenizer.from_pretrained('af1tang/personaGPT')\\nmodel = AutoModelForCausalLM.from_pretrained('af1tang/personaGPT')\\nprompt = \\\"What are your thoughts on the movie The Wolf of Wall Street?\\\"\\ninput_ids = tokenizer.encode(prompt, return_tensors='pt')\\ngenerated_response = model.generate(input_ids, max_length=100, num_return_sequences=1)\\nreview = tokenizer.decode(generated_response[0], skip_special_tokens=True)\\n\"", "answer_id": "MZFuS6onMxJsy2BHasGZfB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 862, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary classes from the transformers library, which are AutoTokenizer and AutoModelForSequenceClassification.\\n2. Load the pre-trained model 'cross-encoder/ms-marco-MiniLM-L-6-v2', which has been trained on the MS Marco Passage Ranking task and can be used for information retrieval.\\n3. Use the tokenizer to tokenize the queries and passages in your dataset, and then convert the tokenized input into tensors using the model's input method.\\n4. Obtain the model's output, which will include scores for each query-passage pair. These scores can be used to rank the search results based on their relevance to the query.\\n5. The search results can be sorted based on their relevance, enhancing the ranking of the search function.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSequenceClassification\\nimport torch\\nmodel = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')\\ntokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L-6-v2')\\nqueries = ['what is the capital of France?', 'Paris is the capital of France.']\\npassages = [\\\"Paris is the largest city in France.\\\", \\\"France is a country in Europe.\\\"]\\nfeatures = tokenizer([queries] * len(passages), passages, padding=True, truncation=True, return_tensors='pt')\\nmodel.eval()\\nwith torch.no_grad():\\n scores = model(**features).logits\\n sorted_passages = sorted(zip(queries, passages), key=lambda x: scores[x[0]])[::-1]\\n\"", "answer_id": "WEgH6A7GdAyLuwCzj5a4AQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 863, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base', return_all_scores=True)\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library.\\n2. Use the pipeline function to create a text classification model with the 'j-hartmann/emotion-english-distilroberta-base' model, which is designed to recognize emotions in English text.\\n3. The created classifier can be used to classify user input emotions, which can help the AI chatbot to respond more appropriately and empathically.\\n<<>>: from transformers import pipeline\\nemotion_classifier = pipeline('text-classification', model='j-hartmann/emotion-english-distilroberta-base', return_all_scores=True)\\nuser_input = \\\"I'm feeling really stressed out today.\\\"\\nemotion_results = emotion_classifier(user_input)\"", "answer_id": "6NssPqZLmxzC7hJoHrfYix", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 864, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModelForTokenClassification.from_pretrained('Jean-Baptiste/roberta-large-ner-english')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes AutoModelForTokenClassification for the token classification model and AutoTokenizer for tokenizing the text input.\\n2. We then use the from_pretrained method to load the pre-trained model 'Jean-Baptiste/roberta-large-ner-english'. This model has been trained for named entity recognition tasks, which is what we need to extract people, organizations, and locations from the given text.\\n3. We tokenize the input text using the AutoTokenizer and convert it into a usable format for the model.\\n4. We pass the tokenized input to the model and process the results to extract the named entities.\\n<<>>: from transformers import AutoModelForTokenClassification, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained('Jean-Baptiste/roberta-large-ner-english')\\nmodel = AutoModelForTokenClassification.from_pretrained('Jean-Baptiste/roberta-large-ner-english')\\ntext = \\\"Hello, my name is John Doe, and I work at Microsoft. Tomorrow, I'll be going to a conference in San Francisco.\\\"\\ninputs = tokenizer(text, return_tensors='pt')\\noutputs = model(**inputs)\\n\"", "answer_id": "Czym3f2AFwkoW97HoLaC2n", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 865, "text": "<<>>: Natural Language Processing Token Classification\\n<<>>: AutoModel.from_pretrained('ckiplab/bert-base-chinese-pos')\\n<<>>: Transformers\\n<<>>:1. Import the required libraries from the transformers package. This includes the BertTokenizerFast and AutoModel modules.\\n2. Load the pre-trained model 'ckiplab/bert-base-chinese-pos', which is designed to perform part-of-speech tagging on Chinese text.\\n3. Create a tokenizer using the BertTokenizerFast.from_pretrained method, specifying the pre-trained model 'bert-base-chinese'.\\n4. Instantiate the part-of-speech tagging model using the AutoModel.from_pretrained method.\\n5. Tokenize the input text and feed it into the model to perform part-of-speech tagging.\\n<<>>: from transformers import BertTokenizerFast, AutoModel\\ntokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')\\nmodel = AutoModel.from_pretrained('ckiplab/bert-base-chinese-pos')\\ninput_text = \\\"\\u4f60\\u597d\\uff0c\\u6211\\u662f\\u4e00\\u540d\\u7a0b\\u5e8f\\u5458\\uff01\\\"\\ntokens = tokenizer.tokenize(input_text)\\noutputs = model(**tokens)\\n\"", "answer_id": "TaQqGxmwbU68sX5LvFgZno", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 866, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: pipeline('table-question-answering', model='google/tapas-small-finetuned-sqa')\\n<<>>: Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'google/tapas-small-finetuned-sqa'.\\n3. The loaded model will be used for table question answering, which is a task where the model answers questions based on tabular data provided in a specific table format.\\n4. Provide the table data containing the salesperson performance in different regions for each month.\\n5. Use the loaded model to answer questions about the table data based on the provided question.\\n<<>>: from transformers import pipeline\\ntable_qa = pipeline('table-question-answering', model='google/tapas-small-finetuned-sqa')\\ntable_data = {'Month': ['January', 'February', 'March'],\\n 'Salesperson': ['Alice', 'Bob', 'Carol'],\\n 'Regions': [['NY', 8000], ['NY', 7000], ['CA', 9000]]}\\nquestion = 'How much did Alice sell in March?'\\nanswer = table_qa(question=question, table=table_data)\"", "answer_id": "XxXvfjxaSng4LChgR26Efh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 867, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: pipeline('table-question-answering', model='dsba-lab/koreapas-finetuned-korwikitq')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary library, which is 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'dsba-lab/koreapas-finetuned-korwikitq'.\\n3. The loaded model will be used for table question answering, which is a task where the model answers questions based on the information provided in a tabular format.\\n4. Provide the table data related to the Korean stock market, and ask the model a question to obtain the accurate information from the table.\\n<<>>: from transformers import pipeline\\ntable_qa = pipeline('table-question-answering', model='dsba-lab/koreapas-finetuned-korwikitq')\\ntable = {'Date': ['2021-01-01', '2021-01-02', '2021-01-03'],\\n 'Symbol': ['SPY', 'MSFT', 'GOOG'],\\n 'Value': [100, 105, 110]}\\nquestion = 'What was the highest stock value during the given period?'\\nresult = table_qa(question=question, table=table)\\nprint(result['answer'])\"", "answer_id": "438PggN86xRCWnjRYBJ5R2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 868, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: BartForConditionalGeneration.from_pretrained('microsoft/tapex-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, such as TapexTokenizer and BartForConditionalGeneration from transformers, and pandas.\\n2. Load the tokenizer and model using the 'microsoft/tapex-base' pre-trained model.\\n3. Create a pandas DataFrame from your data table containing employees' names, titles, departments, and hire dates.\\n4. Define a query to find employees with the title of \\\"Software Engineer\\\" hired in 2020.\\n5. Tokenize the table and query using the tokenizer.\\n6. Generate the answer using the model by calling the generate function.\\n7. Decode the generated output to obtain a human-readable result set.\\n<<>>:from transformers import TapexTokenizer, BartForConditionalGeneration\\nimport pandas as pd\\ntokenizer = TapexTokenizer.from_pretrained('microsoft/tapex-base')\\nmodel = BartForConditionalGeneration.from_pretrained('microsoft/tapex-base')\\ndata = {\\n 'Name': ['John Doe', 'Jane Smith', 'Alice Johnson', 'Bob White', 'Jeff Johnson'],\\n 'Title': ['Software Engineer', 'Software Engineer', 'Data Scientist', 'Data Analyst', 'Project Manager'],\\n 'Department': ['IT', 'Data Science', 'Marketing', 'Operations', 'Sales'],\\n 'Hire Date': [\\\"2019-12-15\\\", \\\"2020-01-30\\\", \\\"2019-09-01\\\", \\\"2018-11-28\\\", \\\"2020-04-20\\\"]\\n}\\ntable = pd.DataFrame.from_dict(data)\\nquery = \\\"Employees with the title of Software Engineer hired in 2020\\\"\\nencoding = tokenizer(table=table, query=query, return_tensors='pt')\\noutputs = model.generate(**encoding)\\nresult = tokenizer.batch_decode(outputs, skip_special_tokens=True)\\nprint(result)\"", "answer_id": "PHxEZkMjj6fGcS7tpPXHNS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 869, "text": "<<>>: Multimodal Document Question Answer\\n<<>>: pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a question-answering pipeline using the pre-trained model 'tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa'. This model is specifically designed for extracting information from documents.\\n3. We can then use this pipeline to ask questions about the document in French, and it will return the relevant answers.\\n4. You can provide the context (the document text) and the question as input to the pipeline to get the answer.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='tiennvcs/layoutlmv2-large-uncased-finetuned-infovqa')\\ncontext = \\\"\\\"\\nquestion = \\\"\\\"\\nanswer = qa_pipeline({\\\"context\\\": context, \\\"question\\\": question})\\n\"", "answer_id": "nymhYEpsK89Qxho5r8SjQt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 870, "text": "<<>>: Natural Language Processing Table Question Answering\\n<<>>: pipeline('table-question-answering', model='google/tapas-large-finetuned-wtq')\\n<<>>: Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We use the pipeline function to create a table question answering model, which is capable of understanding and answering questions related to a table.\\n3. We specify the model 'google/tapas-large-finetuned-wtq' to be loaded. This model has been trained on a large corpus of text and can answer questions based on that context.\\n4. We create a table with the coffee types and their respective caffeine levels.\\n5. The model can then be used to determine the caffeine levels in each cup of coffee on the menu by answering questions based on the table.\\n<<>>: from transformers import pipeline\\ntable_qa = pipeline('table-question-answering', model='google/tapas-large-finetuned-wtq')\\ntable = {\\n 'Coffee type': ['Flat White', 'Long Black', 'Cappuccino', 'Espresso'],\\n 'Caffeine level': [40, 30, 120, 80]\\n}\\nquestions = ['What is the caffeine level in a Flat White?', 'What is the caffeine level in an Espresso?']\\nfor question in questions:\\n result = table_qa(question=question, table=table)\\n print(f'{question}: {result[\\\"answer\\\"]}')\\n\"", "answer_id": "koxzGE9JhJdoFzr9pRCxwJ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 871, "text": "1. Import the required libraries for processing the document's text and extracting the answers.\\n2. Load the model using the 'AutoModelForTokenClassification.from_pretrained' method, which is pretrained for question answering tasks and can extract answers from a given context.\\n3. Use the 'AutoTokenizer.from_pretrained' method to load the tokenizer for the model.\\n4. Tokenize the text from the product manual and pass the resulting tokens to the model.\\n5. The model will then predict the answers from the input text.\\n6. Process the output to retrieve the most probable answers to the questions you want to ask.\\n7. Repeat the process for each question you want to ask or extract answers for.\\n### Code:\\n```python\\nfrom transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained('deepset/roberta-base-squad2')\\ntokenizer = AutoTokenizer.from_pretrained('deepset/roberta-base-squad2')\\ninput_text = \\\"Your product manual text here...\\\"\\ntokens = tokenizer(input_text, return_tensors='pt')\\noutput = model(**tokens)\\nanswers = tokenizer.convert_ids_to_tokens(output.logits.argmax(dim=-1).squeeze().tolist())\\n```\"", "answer_id": "SNbJPGMUrZp8Y7yatc5kRQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 872, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='distilbert-base-cased-distilled-squad')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model.\\n3. Specify the model 'distilbert-base-cased-distilled-squad' to be loaded. This model has been fine-tuned on the SQuAD dataset and can answer questions based on a given context.\\n4. The created question-answering model can be used to answer questions from users of your app based on the provided context.\\n5. To use the model, simply provide the context and question as input to the model, and it will return the answer.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='distilbert-base-cased-distilled-squad')\\ncontext = \\\"This app provides a personal assistant that can answer questions based on a given text.\\\"\\nquestion = \\\"How does this app work?\\\"\\nresult = qa_pipeline({'context': context, 'question': question})\\nanswer = result['answer']\"", "answer_id": "oYr8ULb4Dih2eeUD7KX5bb", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 873, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: pipeline('question-answering', model='monologg/koelectra-small-v2-distilled-korquad-384')\\n<<>>: Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a question-answering model, which is capable of extracting answers from a given context based on the input questions.\\n3. Specify the model 'monologg/koelectra-small-v2-distilled-korquad-384' to be loaded. This is a Korean question answering model based on the mongoliath framework, which is trained on the KorQuAD dataset.\\n4. The created question-answering pipeline can be used to extract answers from the Korean newspaper article based on the reader's questions.\\n<<>>: from transformers import pipeline\\nqa_pipeline = pipeline('question-answering', model='monologg/koelectra-small-v2-distilled-korquad-384')\\nanswer = qa_pipeline(question='', context='')\"", "answer_id": "VQTuXRnhr8KKPkjpyYxXTd", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 874, "text": "<<>>: Natural Language Processing Question Answering\\n<<>>: AutoModelForQuestionAnswering.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary transformers library provided by Hugging Face.\\n2. Create a model for question-answering using the 'valhalla/longformer-base-4096-finetuned-squadv1' pre-trained model. This model is specifically designed for answering questions from long documents.\\n3. When a team member has an essential question, use the model to fetch the answer from the long document.\\n4. The model will help in finding the answer to the question quickly, thus reducing the time spent searching through the document and increasing productivity.\\n<<>>: from transformers import AutoTokenizer, AutoModelForQuestionAnswering\\ntokenizer = AutoTokenizer.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\nmodel = AutoModelForQuestionAnswering.from_pretrained('valhalla/longformer-base-4096-finetuned-squadv1')\\n# Use the tokenizer and model to find answers from the given document for essential questions.\"", "answer_id": "mDoWTPwkqTqixEbxdnVWKS", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 875, "text": "<<>>: Natural Language Processing Zero-Shot Classification\\n<<>>: pipeline('zero-shot-classification', model='xlm-roberta-large')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'xlm-roberta-large'.\\n3. The loaded model will be used for zero-shot classification, which is a task where the model makes a prediction for an example that might not match any of the examples in the training data.\\n4. Provide the text (in German) and a list of candidate labels in German as well.\\n5. The model will classify the given text into one of the candidate labels based on the semantic similarity.\\n<<>>: from transformers import pipeline\\nclassifier = pipeline('zero-shot-classification', model='xlm-roberta-large')\\ntext = \\\"Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU\\\"\\ncandidate_labels = ['politics', 'economy', 'entertainment', 'environment', 'technology']\\nresult = classifier(text, candidate_labels)\"", "answer_id": "YnhELagZ6urYYEkzqfNFu5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 876, "text": "<<>>: Natural Language Processing Text Classification\\n<<>>: pipeline('sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a sentiment analysis model specifically designed to classify opinions positively or negatively.\\n3. Specify the model 'nlptown/bert-base-multilingual-uncased-sentiment' to be loaded. This model is fine-tuned for sentiment analysis tasks and can classify text reviews as either positive or negative.\\n4. Pass the user review to the sentiment analyzer to obtain the sentiment score (positive or negative).\\n<<>>: from transformers import pipeline\\nsentiment_analyzer = pipeline('sentiment-analysis', model='nlptown/bert-base-multilingual-uncased-sentiment')\\nreview = \\\"The movie 'Inception' is an exceptional piece of cinematic art. The storyline is thought-provoking and keeps the audience engaged till the end. The special effects are breathtaking and complement the plot perfectly.\\\"\\nsentiment_score = sentiment_analyzer(review)\"", "answer_id": "XciBhzRuRVjiN4hGyyfbXB", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 877, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: pipeline('translation_en_to_fr', model='facebook/nllb-200-distilled-600M')\\n<<>>: Hugging Face Transformers\\n<<>>:1. We will use the Hugging Face Transformers library to create a translation pipeline.\\n2. Specify the model 'facebook/nllb-200-distilled-600M' which is trained to translate text from English to French.\\n3. Create the translation pipeline using the model, which takes the English text as input and outputs the French translation.\\n4. Use the translation pipeline to translate the book details and recommend books in French to cater to a wider audience.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_fr', model='facebook/nllb-200-distilled-600M')\\nbook_details = 'Book title in English goes here...'\\nfrench_translation = translator(book_details, max_length=100, num_return_sequences=1)\\nfrench_book_details = french_translation[0]['translation_text']\\n\"", "answer_id": "VuoZ4uJcjEUam8h5nXYtLx", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 878, "text": "\\u00e7a va?\\\"", "answer_id": "gvGfVWQqWwDk23hndUGi6B", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 879, "text": "Nous communiquons des informations sur les produits sur le web. Traduisez l'anglais \u00e0 la page suivante:\\nhttps://translate.google.com/translate?sl=en&tl=fr&js=y&prev=_t&hl=en&ie=UTF-8&u=https%3A%2F%2Famazon.com%2Fgp%2Fcustomer-reviews%2Fref%2Fcm_cr_getr_d_rvw%2F10129390011%2F%2F&edit-text=", "answer_id": "iSu4jBVKbswGzTau2hJ8n7", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 880, "text": "\\\"Stockholm is Sweden's capital and largest city. It has a rich history and offers many cultural and historical attractions.\\\"\"", "answer_id": "UZA9wtfSrduenDXHbZGJ9F", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 881, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_fr', model='facebook/nllb-200-distilled-600M')\\n<<>>: PyTorch Transformers\\n<<>>: 1. We first import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a translation pipeline using the 'translation_en_to_fr' setting and specifying the pre-trained model 'facebook/nllb-200-distilled-600M'. This model is designed for translating English text to French text.\\n3. We then provide our English story as input to the translation pipeline, and it will generate the translated French text.\\n4. You can then use the translated text to share your story with your French-speaking friends.\\n<<>>: from transformers import pipeline\\ntranslation_pipeline = pipeline('translation_en_to_fr', model='facebook/nllb-200-distilled-600M')\\nenglish_story = \\\"The adventures of a superhero who saves the day from evil villains.\\\"\\ntranslated_text = translation_pipeline(english_story)\\n\"", "answer_id": "nduQcs6Ma2JpKgGGF7i5Fn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 882, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: pipeline('summarization', model='google/pegasus-newsroom')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Create a summarization pipeline using the 'summarization' task and specifying the pre-trained 'google/pegasus-newsroom' model. This model is designed for summarization tasks on news articles.\\n3. Read the news article content using a suitable API or by passing a text string to the summarizer.\\n4. The pipeline will return a summarized version of the article contents, which can be used for various applications such as news aggregation sites, news feeds, or even just to provide a quick overview of the article.\\n<<>>: from transformers import pipeline\\nsummarizer = pipeline('summarization', model='google/pegasus-newsroom')\\narticle_content = \\\"Long news article text here...\\\"\\nsummary = summarizer(article_content, max_length=100, min_length=25, do_sample=False)[0]['summary_text']\\n\"", "answer_id": "km4QsTn2ymwcEb9VRQdKk8", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 883, "text": "<<>>: Natural Language Processing Summarization\\n<<>>: AutoModelWithLMHead.from_pretrained('SEBIS/code_trans_t5_base_code_documentation_generation_python')\\n<<>>: Transformers\\n<<>>:1. Import the necessary libraries, which include AutoTokenizer and AutoModelWithLMHead from the transformers package.\\n2. Load the pre-trained model 'SEBIS/code_trans_t5_base_code_documentation_generation_python' using the from_pretrained method of the AutoModelWithLMHead class. This model is specifically designed for generating code documentation from Python function inputs.\\n3. Tokenize the input Python function using the AutoTokenizer's tokenize method.\\n4. Use the model to generate code documentation for the input Python function.\\n5. Decode the generated tokens back into human-readable text for the code documentation.\\n<<>>: from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained('SEBIS/code_trans_t5_base_code_documentation_generation_python')\\nmodel = AutoModelWithLMHead.from_pretrained('SEBIS/code_trans_t5_base_code_documentation_generation_python')\\ninput_text = \\\"def e ( message ) : print( message )\\\"\\ninput_tokens = tokenizer.tokenize(input_text)\\ndocumentation_tokens = model.generate(input_tokens)\\ndocumentation = tokenizer.decode(documentation_tokens[0], skip_special_tokens=True)\"", "answer_id": "9rRnSxTm6iYTk2dVnS2b8y", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 884, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('conversational', model='hyunwoongko/blenderbot-9B')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a conversational model.\\n3. Specify the model 'hyunwoongko/blenderbot-9B' to be loaded. This model is designed for open-domain conversations and can engage users by discussing a wide range of topics.\\n4. Use the created conversational model to generate responses based on user input.\\n5. The chatbot can be integrated into your platform to engage users and keep them interested.\\n<<>>: from transformers import pipeline\\nchatbot = pipeline('conversational', model='hyunwoongko/blenderbot-9B')\\nuser_input = \\\"Tell me an interesting fact about the universe.\\\"\\nresponse = chatbot(user_input)\\n\"", "answer_id": "RhNY5BBvRmqXhs3DzdiiqM", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 885, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: AutoModelForSeq2SeqLM.from_pretrained('ToddGoldfarb/Cadet-Tiny', low_cpu_mem_usage=True)\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers package provided by Hugging Face, including AutoTokenizer and AutoModelForSeq2SeqLM.\\n2. We create a CadetTinyAgent class that encapsulates the conversation logic and implements the interface required for the AutoModelForSeq2SeqLM model.\\n3. We instantiate the CadetTinyAgent class and load the pre-trained model 'ToddGoldfarb/Cadet-Tiny'.\\n4. We provide an instruction, knowledge, and a dialog as input to the CadetTinyAgent, representing the situation and the desired response.\\n5. The agent uses the given instruction and knowledge to generate a suitable response to the customer's complaint, based on the information it has learned.\\n<<>>: from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nimport torch\\nclass CadetTinyAgent:\\n def __init__(self):\\n self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\\n self.tokenizer = AutoTokenizer.from_pretrained('t5-small', model_max_length=512)\\n self.model = AutoModelForSeq2SeqLM.from_pretrained('ToddGoldfarb/Cadet-Tiny', low_cpu_mem_usage=True).to(self.device)\\n self.conversation_history = \\\"\\\"\\ninstruction = \\\"Instruction: How can I respond to a customer complaint about late delivery?\\\"\\nknowledge = \\\"Cadet-Tiny is a small model that can help you respond to customer complaints.\\\"\\ndialog = [\\\"Customer: My package is late. What's going on?\\\", \\\"Support: \\\"] \\ninput_text = f\\\"{instruction} {knowledge} {dialog[0]}\\\"\\ninput_ids = tokenizer(input_text, return_tensors='pt').input_ids\\noutput_ids = self.model.generate(input_ids, max_length=128, no_repeat_ngram_size=2)\\nresponse = tokenizer.decode(output_ids[0], skip_special_tokens=True)\\nprint(response)\\n\"", "answer_id": "ZvLU6UjqQHgx3tffxa4hBy", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 886, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: pipeline('text-generation', model='gpt2-large')\\n<<>>: Transformers\\n<<>>: 1. Use the transformers library, which is provided by Hugging Face, to access the pre-trained GPT-2 language model.\\n2. Create a text generation pipeline using the 'gpt2-large' model, which is capable of generating text based on a given prompt.\\n3. Provide the game studio with a starting prompt like \\\"In a world filled with action and adventure,\\\" and use the GPT-2 model to generate a hint or setting suggestion for their new action game.\\n<<>>: from transformers import pipeline, set_seed\\ngenerator = pipeline('text-generation', model='gpt2-large')\\nset_seed(42)\\ngame_hint = generator(\\\"In a world filled with action and adventure,\\\", max_length=100, num_return_sequences=1)[0]['generated_text']\\n\"", "answer_id": "83MX6QBxU9wMs4mujTytMD", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 887, "text": "<<>>: Natural Language Processing Conversational\\n<<>>: pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the model, which in this case is 'Zixtrauce/BDBot4Epoch'.\\n3. The loaded model is designed for text generation tasks, specifically conversational tasks, which is perfect for generating human-like responses for customer questions.\\n4. You can provide the customer's question as input to the model, and it will generate a relevant and context-appropriate response, allowing your chatbot to provide excellent customer support.\\n<<>>: from transformers import pipeline\\ntext_generator = pipeline('text-generation', model='Zixtrauce/BDBot4Epoch')\\ncustomer_question = \\\"How do I reset my password?\\\"\\nresponse = text_generator(customer_question, max_length=50, do_sample=True)\\nprint(response[0]['generated_text'])\"", "answer_id": "Nz4rxFt4H7zMoc4D9JXUB3", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 888, "text": "\\\"Ich habe ein Termin beim Arzt m\\u00fcrn fr\\u00fchlich.\\\"\"", "answer_id": "HoUtht7rAYD4a7isE5P9EN", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 889, "text": "<<>>: Natural Language Processing Text2Text Generation\\n<<>>: HappyTextToText('T5', 'vennify/t5-base-grammar-correction')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary HappyTextToText and TTSettings classes for creating a grammar correction tool.\\n2. Instantiate the model by providing the model name 'T5' and the pre-trained grammar correction model 'vennify/t5-base-grammar-correction' to the HappyTextToText class.\\n3. Set up the model parameters such as num_beams and min_length using TTSettings.\\n4. Finally, provide your text to correct grammar using the 'generate' method of the created model instance.\\n<<>>: from happytext2text.tt.happytexttotext import HappyTextToText\\nfrom happytext2text.tt.settingstommy import TTSettings\\nhappy_tt = HappyTextToText('T5', 'vennify/t5-base-grammar-correction')\\nargs = TTSettings(num_beams=5, min_length=1)\\ninput_text = \\\"This sentences has has bads grammar.\\\"\\nresult = happy_tt.generate(input_text, args=args)\\ncorrected_text = result.text\"", "answer_id": "RgKx3VcMCojxo8jnvypqdv", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 890, "text": "<<>>: Natural Language Processing Translation\\n<<>>: pipeline('translation_en_to_de', model='Helsinki-NLP/opus-mt-en-de')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the pipeline function from the transformers library provided by Hugging Face.\\n2. Use the pipeline function to create a translation model.\\n3. Specify the model 'Helsinki-NLP/opus-mt-en-de' to be loaded. This model is trained to translate text from English to German.\\n4. Use the created translator to translate the given English text into German.\\n<<>>: from transformers import pipeline\\ntranslator = pipeline('translation_en_to_de', model='Helsinki-NLP/opus-mt-en-de')\\ngerman_text = translator(english_text)\\n\"", "answer_id": "2ziAJQgpZo2DPQ626xi8io", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 891, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: pipeline('fill-mask', model='albert-base-v2')\\n<<>>: Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. We create a fill-mask pipeline using the 'albert-base-v2' model, which is designed for masked language modeling tasks.\\n3. We pass the text containing the masked words to the pipeline, and the model will suggest suitable words for the masked positions.\\n4. The most likely words for the given context will be returned, ranked by probability.\\n<<>>: from transformers import pipeline\\nunmasker = pipeline('fill-mask', model='albert-base-v2')\\nresult = unmasker(\\\"The is a large, slow-moving reptile native to the southeastern United States.\\\")\\n\"", "answer_id": "77GnYbqCnxLXRRQu6gSkDr", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 892, "text": "<<>>: Natural Language Processing Text Generation\\n<<>>: AutoModelForCausalLM.from_pretrained('bigscience/bloomz-560m')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary classes from the transformers library provided by Hugging Face. This includes AutoModelForCausalLM for text generation and AutoTokenizer for tokenization.\\n2. Load the pre-trained model 'bigscience/bloomz-560m' using the from_pretrained method of the AutoModelForCausalLM class. This model is trained on a large corpus of Chinese text and can be used for generating Chinese text responses.\\n3. Tokenize the input text, which is the English sentence you want to translate into Chinese, using the AutoTokenizer.\\n4. Use the model to generate a response in Chinese by feeding the tokenized input into the model.generate() method.\\n5. Decode the generated tokens back into a string using the tokenizer's decode() method, which returns the translated Chinese text.\\n<<>>: from transformers import AutoModelForCausalLM, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained('bigscience/bloomz-560m')\\nmodel = AutoModelForCausalLM.from_pretrained('bigscience/bloomz-560m')\\ninput_text = \\\"\\u8fd9\\u662f\\u4e00\\u4e2a\\u793a\\u4f8b\\u7684\\u57ce\\u5e02\\u5728\\u54ea\\u91cc\\u5de5\\u4f5c\\uff1f\\\"\\ninput_tokens = tokenizer.encode(input_text, return_tensors='pt')\\noutput = model.generate(input_tokens)\\ntranslated_text = tokenizer.decode(output[0])\\n\"", "answer_id": "WQRSymnkcdY9DGhGhQfCTn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 893, "text": "<<>>: Natural Language Processing Fill-Mask\\n<<>>: DebertaModel.from_pretrained('microsoft/deberta-base')\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function from transformers to load the fill-mask model, which in this case is 'microsoft/deberta-base'.\\n3. The loaded fill-mask model can be used to predict and fill in the blanks in sentences by providing it with a sentence containing a masked token (e.g. [MASK]).\\n4. The model is trained on a wide range of data and can handle contexts better, making it an ideal choice for filling in the blanks in sentences.\\n<<>>: from transformers import pipeline\\nfill_mask = pipeline('fill-mask', model='microsoft/deberta-base')\\nsentence_with_blank = 'The quick brown fox jumps over the [MASK] dog.'\\nfilled_sentence = fill_mask(sentence_with_blank)\\n\"", "answer_id": "KXJFx9pwiejazhidihrG5b", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 894, "text": "<<>>: Natural Language Processing Sentence Similarity\\n<<>>: SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Install and import the sentence-transformers library.\\n2. Create a SentenceTransformer model by specifying the model name 'sentence-transformers/distilbert-base-nli-stsb-mean-tokens'.\\n3. Encode the two input sentences using the created model to obtain their embeddings.\\n4. Compute the cosine similarity between the two sentence embeddings to determine the level of similarity between the sentences.\\n5. Use the similarity score to determine the similarity between the two sentences and use that to evaluate the plagiarism detection task.\\n<<>>: from sentence_transformers import SentenceTransformer\\nfrom sklearn.metrics.pairwise import cosine_similarity\\nmodel = SentenceTransformer('sentence-transformers/distilbert-base-nli-stsb-mean-tokens')\\nsentence1 = \\\"This is an example sentence.\\\"\\nsentence2 = \\\"This is a sample sentence.\\\"\\nembeddings = model.encode([sentence1, sentence2])\\nsimilarity = cosine_similarity(embeddings[0].reshape(1, -1), embeddings[1].reshape(1, -1))\\nprint(f\\\"Similarity Score: {similarity[0][0]}\\\")\"", "answer_id": "NuC6N3TeQGiqhztGQAiWog", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 895, "text": "<<>>: Audio Text-to-Speech\\n<<>>: run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n<<>>: ESPnet\\n<<>>:1. First, ensure that you have the necessary software installed: transformers, huggingface_hub, and ESPnet.\\n2. Download the mio/amadeus model using the provided command: run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus.\\n3. Once the model has been downloaded, you can convert the input text into speech by feeding the text into the ESPnet API.\\n4. The API will return the generated speech, which can then be saved or played back as required.\\n<<>>: # Assuming the necessary software is already installed\\n# run.sh is the script provided by ESPnet to download and run the model\\n# Execute the following command in the terminal\\n./run.sh --skip_data_prep false --skip_train true --download_model mio/amadeus\\n# Replace The sun was shining brightly, and the birds were singing sweetly with your text to convert\\n\"", "answer_id": "m64ivEHifRH6Sbzn2a8Ya2", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 896, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-small')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'WhisperProcessor' and 'WhisperForConditionalGeneration' from transformers.\\n2. Load the Whisper ASR model and processor using 'WhisperForConditionalGeneration.from_pretrained()' and 'WhisperProcessor.from_pretrained()', respectively, with the model name 'openai/whisper-small'.\\n3. Prepare the audio data by converting it into a suitable format for processing by the model.\\n4. Use the loaded model to transcribe the audio file by generating a sequence of predicted tokens.\\n5. Decode the predicted token sequence to obtain the final transcription.\\n6. Analyze the transcription to determine the sentiment of the audio file (positive, negative, or neutral).\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-small')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-small')\\naudio_data = ... # Load the audio data\\ninput_features = processor(audio_data, return_tensors='pt').input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\\n# Analyze the transcription to determine the sentiment\\n# (positive, negative, or neutral).\\n\"", "answer_id": "YnmMufPZRiEjubVaS4hA37", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 897, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: SpeechRecognitionModel('jonatasgrosman/wav2vec2-large-xlsr-53-japanese')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the necessary library, which is 'SpeechRecognitionModel' from the huggingsound module.\\n2. Use the 'SpeechRecognitionModel' function to load the model, which in this case is 'jonatasgrosman/wav2vec2-large-xlsr-53-japanese'.\\n3. Provide the model with the audio file of the interview.\\n4. The model will then transcribe the audio file into text, which can be used for further analysis and quote finding in your article.\\n<<>>: from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel('jonatasgrosman/wav2vec2-large-xlsr-53-japanese')\\naudio_paths = ['/path/to/interview_audio.mp3']\\n# Replace '/path/to/interview_audio.mp3' with the path to your audio file\\ntranscriptions = model.transcribe(audio_paths)\"", "answer_id": "enqckw7aGXVZJZjCahkVd5", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 898, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the necessary classes from the transformers library provided by Hugging Face. This includes the WhisperProcessor for pre-processing audio data and the WhisperForConditionalGeneration for the automatic speech recognition model.\\n2. We then load the pre-trained model 'openai/whisper-large' using the from_pretrained method.\\n3. We use the WhisperProcessor to process the audio file and convert it into a format that the model can understand.\\n4. The model can then be used to automatically transcribe the speech in the audio file, which can be later used for podcast editing purposes.\\n<<>>: from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\nprocessor = WhisperProcessor.from_pretrained('openai/whisper-large')\\nmodel = WhisperForConditionalGeneration.from_pretrained('openai/whisper-large')\\naudio_file_path = 'path/to/audio/file.wav'\\nsample = load_dataset[0][\\\"audio\\\"]\\n# Replace with the actual audio sample\\ninput_features = processor(sample[\\\"array\\\"], sampling_rate=sample[\\\"sampling_rate\\\"], return_tensors=\\\"pt\\\").input_features\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_deduplicate_and_reset_indentation(predicted_ids)\\n\"", "answer_id": "AcLCNe2B4s5ynd6754ZDqt", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 899, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: WaveformEnhancement.from_hparams('speechbrain/mtl-mimic-voicebank', 'pretrained_models/mtl-mimic-voicebank')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the speechbrain package. This includes the WaveformEnhancement class for speech enhancement.\\n2. We then use the from_hparams method of the WaveformEnhancement class to load the pre-trained model 'speechbrain/mtl-mimic-voicebank'.\\n3. This model can be used to enhance speech by filtering out background noise, making it easier for the robot to understand voice commands.\\n<<>>: import torch\\nfrom speechbrain.pretrained import WaveformEnhancement\\nenhance_model = WaveformEnhancement.from_hparams(\\n source='speechbrain/mtl-mimic-voicebank',\\n savedir='pretrained_models/mtl-mimic-voicebank',\\n)\\nenhanced_waveform = enhance_model.enhance_file('voicebank/sample.wav')\\n\"", "answer_id": "BfkuaNzSCicq8nRwXNN2ig", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 900, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: WaveformEnhancement.from_hparams('speechbrain/mtl-mimic-voicebank', 'pretrained_models/mtl-mimic-voicebank')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the necessary libraries, which are 'WaveformEnhancement' from speechbrain.pretrained and 'torchaudio'.\\n2. Use the 'WaveformEnhancement.from_hparams' function to load the pre-trained speech enhancement model 'speechbrain/mtl-mimic-voicebank'.\\n3. The loaded model will enhance the audio by reducing background noise and improving speech intelligibility for low-quality recordings.\\n4. Save the enhanced audio file and use it as an input for the recommendation engine.\\n5. Recommend the enhanced audio to users seeking high-quality content on the podcast platform.\\n<<>>: import torchaudio\\nfrom speechbrain.pretrained import WaveformEnhancement\\nenhance_model = WaveformEnhancement.from_hparams(\\n source='speechbrain/mtl-mimic-voicebank',\\n savedir='pretrained_models/mtl-mimic-voicebank',\\n)\\n# Replace 'your_audio_file.wav' with the path to a low-quality recording\\nenhanced = enhance_model.enhance_file('your_audio_file.wav')\\ntorchaudio.save('enhanced.wav', enhanced.unsqueeze(0).cpu(), 16000)\\n\"", "answer_id": "BZ4cquRED6vespakwjHzpn", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 901, "text": "<<>>: Audio Text-to-Speech\\n<<>>: load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur')\\n<<>>: Fairseq\\n<<>>:1. Import the required libraries for loading the model from the Hugging Face hub and the torch library for handling tensors.\\n2. Load the speech-to-speech translation model from the Hugging Face hub using the load_model_ensemble_and_task_from_hf_hub() function, specifying the model name 'facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur'.\\n3. The loaded model is a speech-to-speech translation model that can translate between English, Spanish, French, and Italian languages.\\n4. Synthesize speech by providing the input text and generating the waveform using the model and task.\\n5. Save the generated waveform to an audio file with the desired filename.\\n<<>>: from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nimport torch\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub('facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur')\\nmodel = models[0].cpu()\\ncfg['task'].cpu = True\\ngenerator = task.build_generator([model], cfg)\\ntext = \\\"Text in the desired language (English, Spanish, French, or Italian)\\\"\\nsample = S2THubInterface.get_model_input(task, text)\\nwav, rate = S2THubInterface.get_prediction(task, model, generator, sample)\\nwith open(\\\"output_file.wav\\\", \\\"wb\\\") as f:\\n f.write(wav.to(\\\"float\\\"))\\n\"", "answer_id": "4YqoANo7oGjBvizxsuHraz", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 902, "text": "<<>>: Audio Audio-to-Audio\\n<<>>: separator.from_hparams(source='speechbrain/sepformer-whamr', savedir='pretrained_models/sepformer-whamr')\\n<<>>: SpeechBrain\\n<<>>: 1. First, we need to install the SpeechBrain library by running `pip install speechbrain`.\\n2. Import the SepformerSeparation module from the speechbrain.pretrained package.\\n3. Load the pretrained Sepformer model using the separator.from_hparams() function, specifying the source as 'speechbrain/sepformer-whamr' and the savedir as 'pretrained_models/sepformer-whamr'.\\n4. To separate the voices in the single channel audio recording, call the separate_file() method on the model with the path to the audio file as argument.\\n5. The method returns the separated audio sources, which can then be saved as separate files using the torchaudio library.\\n<<>>: from speechbrain.pretrained import SepformerSeparation as separator\\nimport torchaudio\\nmodel = separator.from_hparams(source='speechbrain/sepformer-whamr', savedir='pretrained_models/sepformer-whamr')\\nest_sources = model.separate_file(path='input_audio_file.wav')\\ntorchaudio.save('source1hat.wav', est_sources[:, :, 0].detach().cpu(), 8000)\\ntorchaudio.save('source2hat.wav', est_sources[:, :, 1].detach().cpu(), 8000)\\n\"", "answer_id": "KgTbt8bWEqmyQiFSNdfuvP", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 903, "text": "<<>>: Audio Audio Classification\\n<<>>: EmotionModel.from_pretrained('audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We first import the necessary classes from the transformers package. This includes the RegressionHead and EmotionModel classes.\\n2. We then use the from_pretrained method of the EmotionModel class to load the pre-trained model 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'. This model has been trained to classify emotions in spoken language, which can be helpful for analyzing emotional speech in public speaking.\\n3. This model can then be used to analyze audio files containing people's public speaking, classify the emotions in their speech, and provide feedback on emotional aspects of their presentations.\\n<<>>: from transformers import RegressionHead, EmotionModel\\nemotion_model = EmotionModel.from_pretrained('audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim')\\n# replace 'audio_file_path' with the path to the audio file\\nemotions = emotion_model(torchaudio.load('audio_file_path'))\"", "answer_id": "iJ59KLbHJSTZTatSefn2sQ", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 904, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/hubert-base-superb-er')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create an audio-classification model, which can identify emotions from spoken words by analyzing the speaker's voice.\\n3. We specify the model 'superb/hubert-base-superb-er' to be loaded. This is a pre-trained model for emotion recognition, which has been trained on 16kHz sampled speech audio and can predict a wide range of emotions such as anger, disgust, fear, happiness, neutral, and sadness.\\n4. The created classifier can be used to process audio files taken during client meetings or interactions to understand their emotional state and provide insights to improve customer service.\\n<<>>: from transformers import pipeline\\nemotion_classifier = pipeline('audio-classification', model='superb/hubert-base-superb-er')\\nemotion_prediction = emotion_classifier(client_audio_file_path)\\n\"", "answer_id": "VwW5PJosQbpgLjf3RsDLFo", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 905, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='superb/hubert-large-superb-er')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import necessary libraries, which are 'pipeline' from transformers.\\n2. Use the 'pipeline' function to load the 'superb/hubert-large-superb-er' model for audio classification.\\n3. The loaded model will classify emotions in audio files, in this case, the audio recorded while children brush their teeth.\\n4. The model will return the top emotions detected in the audio.\\n5. This can help us better understand the emotional state of children while they brush their teeth, which can guide the design of our AI-based toothbrushes.\\n<<>>: from transformers import pipeline\\nemotion_classifier = pipeline('audio-classification', model='superb/hubert-large-superb-er')\\n# Load the audio file\\n# ...\\nresult = emotion_classifier(audio_file)\\n\"", "answer_id": "edkXPfWx9NEP3QQCK4RNvh", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 906, "text": "<<>>: Audio Audio Classification\\n<<>>: pipeline('audio-classification', model='MIT/ast-finetuned-audioset-10-10-0.4593')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. We import the pipeline function from the transformers library provided by Hugging Face.\\n2. The pipeline function is used to create an audio classification model, which can identify different types of sounds based on their characteristics.\\n3. We specify the model 'MIT/ast-finetuned-audioset-10-10-0.4593', which is a pretrained Audio Spectrogram Transformer (AST) fine-tuned on AudioSet.\\n4. This model can be used to analyze environmental noise and classify it into different categories, such as music, speech, and ambient noise.\\n5. The output can be used to determine whether a given environment is suitable for communication, taking into account the noise level and other acoustic characteristics.\\n<<>>: from transformers import pipeline\\naudio_classifier = pipeline('audio-classification', model='MIT/ast-finetuned-audioset-10-10-0.4593')\\nnoise_level = audio_classifier(audio_file_path)\\n\"", "answer_id": "M4hvLFC7n8Bun6NmoxjUCK", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 907, "text": "<<>>: Audio Classification\\n<<>>: pipeline('audio-classification', model='MIT/ast-finetuned-speech-detector-v2')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. First, import the 'pipeline' function from the transformers library.\\n2. Then, create an audio classifier by loading the pre-trained model 'MIT/ast-finetuned-speech-detector-v2'. This model has been fine-tuned for speech detection tasks.\\n3. Use the audio classifier to analyze the provided audio clip and determine if it contains speech.\\n<<>>: from transformers import pipeline\\naudio_classifier = pipeline('audio-classification', model='MIT/ast-finetuned-speech-detector-v2')\\nresult = audio_classifier(audio_clip)\\n\"", "answer_id": "FGpan4XnxZyGiTZEkw99cj", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 908, "text": "<<>>: Tabular Tabular Classification\\n<<>>: load_model(cached_download(hf_hub_url('danupurnomo/d33_digits', 'd33_digits.joblib')))\\n<<>>: Hugging Face Transformers\\n<<>>:1. Import the necessary libraries, including huggingface_hub, joblib, and pandas.\\n2. Load the pretrained model 'danupurnomo/d33_digits' using the cached_download function from huggingface_hub and the load_model function from joblib.\\n3. Read your tabular data using pandas and select the relevant features.\\n4. Use the loaded model to predict the category of each digit in the input data.\\n<<>>: from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nREPO_ID = 'danupurnomo/d33_digits'\\nFILENAME = 'd33_digits.joblib'\\nmodel = joblib.load(cached_download(hf_hub_url(REPO_ID, FILENAME)))\\ndata = pd.read_csv('your_data.csv')\\n# replace 'your_data.csv' with path to your input data\\npredictions = model.predict(data)\\n\"", "answer_id": "dtxkGTurQCBqnvVEFXQ49j", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 909, "text": "<<>>: Audio Automatic Speech Recognition\\n<<>>: pipeline('audio-to-transcript', model='facebook/wav2vec2-base-960h')\\n<<>>: Hugging Face Transformers\\n<<>>: 1. Import the pipeline function from Hugging Face Transformers.\\n2. Create an automatic speech recognition (ASR) pipeline using 'audio-to-transcript' and the pre-trained model 'facebook/wav2vec2-base-960h'.\\n3. This model is trained for converting speech to text and can handle multiple speakers at once.\\n4. Feed the audio feed into the pipeline, which will generate a transcript with speakers' turns separated.\\n5. The resulting transcript can then be used for further analysis or visualization.\\n<<>>: from transformers import pipeline\\nmodel = pipeline('audio-to-transcript', model='facebook/wav2vec2-base-960h')\\ntranscript = model(audio_feed)\\n\"", "answer_id": "fcVsdFemihprpXaYv68EQX", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 910, "text": "<<>>: Tabular Classification\\n<<>>: joblib.load('model.joblib')\\n<<>>: Joblib\\n<<>>: 1. Import the necessary libraries, including joblib and pandas.\\n2. Load the pre-trained model using the joblib.load function.\\n3. Read the input data as a CSV file using pandas.read_csv.\\n4. Process the input data according to the model's configuration, ensuring that the features are in the correct order and have the appropriate naming convention.\\n5. Use the loaded model to make predictions on the processed data to determine if carbon emissions will be high or not.\\n<<>>: import joblib\\nimport pandas as pd\\nmodel = joblib.load('model.joblib')\\ndata = pd.read_csv('data.csv')\\n# ensure the columns are named appropriately and in the correct order\\nprocessed_data = data[['column1', 'column2', 'column3']]\\npredictions = model.predict(processed_data)\\n\"", "answer_id": "m9PdJdpGciYqLmTutAju9o", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}} {"question_id": 911, "text": "<<>>: Tabular Tabular Regression\\n<<>>: RandomForestRegressor(max_depth=10, n_estimators=50, random_state=59)\\n<<>>: Scikit-learn\\n<<>>: 1. Import the necessary libraries (scikit-learn and pandas) to load the RandomForestRegressor model and handle data manipulation.\\n2. Load the US power grid dataset (load_dataset) and split it into training and testing data (X_train, X_test, y_train, y_test).\\n3. Create a feature matrix containing the selected features (X) and a binary target variable indicating whether electricity is consumed (energy) or not (no_energy).\\n4. Load the RandomForestRegressor model with the specified parameters.\\n5. Fit the model on the training data.\\n6. Make predictions using the trained model on the test data.\\n7. The model can be used to predict electricity consumption based on historical data.\\n<<>>: from sklearn.datasets import fetch_california_power_load\\nfrom sklearn.model_selection import train_test_split\\nfrom sklearn.metrics import mean_squared_error\\nfrom sklearn.ensemble import RandomForestRegressor\\nfrom sklearn.preprocessing import StandardScaler\\nfrom sklearn.pipeline import Pipeline\\ndata = fetch_california_power_load()\\nX, y, energy, no_energy = data.data, data.target, data.energy, data.no_energy\\nX_train, X_test, y_train, y_test = train_test_split(X, y, energy=energy, no_energy=no_energy, test_size=0.2, random_state=42)\\nscaler = StandardScaler()\\nX_train_scaled = scaler.fit_transform(X_train)\\nX_test_scaled = scaler.transform(X_test)\\nregressor = RandomForestRegressor(max_depth=10, n_estimators=50, random_state=59)\\nregressor.fit(X_train_scaled, y_train)\\ny_pred = regressor.predict(X_test_scaled)\\nmse = mean_squared_error(y_test, y_pred)\\nrmse = np.sqrt(mse)\\nprint(f\\\"rmse: {rmse:.2f}\\\")\"", "answer_id": "HgjxSfMh3UDW4LwxuiZXQa", "model_id": "zshot-llama-FT-hf-may20", "metadata": {}}