 We are an analytics company that needs help in extracting data tables from plots and charts efficiently.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Our marketing team wants a video commercial for our product. They are looking for a video with a couple sitting in a cafe and laughing while using our product.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 A company is having an ad campaign with a superhero theme. They want to generate a video of Spiderman surfing to showcase as a social media ad.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-video-synthesis\', \'api_name\': \'damo-vilab/text-to-video-ms-1.7b\', \'api_call\': "DiffusionPipeline.from_pretrained(\'damo-vilab/text-to-video-ms-1.7b\', torch_dtype=torch.float16, variant=fp16)", \'api_arguments\': [\'prompt\', \'num_inference_steps\', \'num_frames\'], \'python_environment_requirements\': [\'pip install git+https://github.com/huggingface/diffusers transformers accelerate\'], \'example_code\': \'pipe = 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_video(video_frames)\', \'performance\': {\'dataset\': \'Webvid\', \'accuracy\': \'Not specified\'}, \'description\': \'A multi-stage text-to-video generation diffusion model that inputs a description text and returns a video that matches the text description. The model consists of three sub-networks: text feature extraction model, text feature-to-video latent space diffusion model, and video latent space to video visual space model. It supports English input only and has a wide range of applications.\'}', metadata={})]


 We are developing a solution where we can visually detect the medical conditions of patients. Can you write code to determine the condition based on the image provided?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Help me to build a visual-question-answering model that takes a question and an image as input and returns an answer based on the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'ivelin/donut-refexp-combined-v1\', \'api_call\': "pipeline(\'visual-question-answering\', model=\'ivelin/donut-refexp-combined-v1\')", \'api_arguments\': \'image, question\', \'python_environment_requirements\': \'transformers\', \'example_code\': "vqa(image=\'path/to/image.jpg\', question=\'What is the color of the object?\')", \'performance\': {\'dataset\': \'ivelin/donut-refexp-combined-v1\', \'accuracy\': \'N/A\'}, \'description\': \'A visual question answering model that takes an image and a question as input and provides an answer based on the visual content of the image and the context of the question.\'}', metadata={})]


 Implement an optical text recognition system for documents that can answer a simple question about the document's content.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'ivelin/donut-refexp-combined-v1\', \'api_call\': "pipeline(\'visual-question-answering\', model=\'ivelin/donut-refexp-combined-v1\')", \'api_arguments\': \'image, question\', \'python_environment_requirements\': \'transformers\', \'example_code\': "vqa(image=\'path/to/image.jpg\', question=\'What is the color of the object?\')", \'performance\': {\'dataset\': \'ivelin/donut-refexp-combined-v1\', \'accuracy\': \'N/A\'}, \'description\': \'A visual question answering model that takes an image and a question as input and provides an answer based on the visual content of the image and the context of the question.\'}', metadata={})]


 Our company deals with insurance claims. We need a smart assistant who can retrieve information from documents, especially invoices, such as total amount, date of invoice, and name of the service provider.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'layoutlm-invoices\', \'api_call\': "AutoModelForDocumentQuestionAnswering.from_pretrained(\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "nlp(question=\'What is the total amount?\', context=\'your_invoice_text\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'Not provided\'}, \'description\': \'A fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens, this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


 We have an AI-powered document management system that needs to answer questions based on the content of a given document.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'vilt-finetuned-vqasi\', \'api_call\': "ViltModel.from_pretrained(\'tufa15nik/vilt-finetuned-vqasi\')", \'api_arguments\': {\'model\': \'tufa15nik/vilt-finetuned-vqasi\', \'tokenizer\': \'tufa15nik/vilt-finetuned-vqasi\'}, \'python_environment_requirements\': {\'transformers\': \'>=4.11.3\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Visual Question Answering model fine-tuned on the VQASI dataset by tufa15nik using the ViLT architecture. The model is designed to answer questions based on the content of an input image.\'}', metadata={})]


 Take a look at this document image and tell me the answer to my question: \"What is the total amount due?\".\n###Input: {\"image_url\": \"https://example.com/document_invoice.jpg\", \"question\": \"What is the total amount due?\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'DialogLED-base-16384\', \'api_call\': "LEDForConditionalGeneration.from_pretrained(\'MingZhong/DialogLED-base-16384\')", \'api_arguments\': \'input_text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'arxiv\', \'accuracy\': \'2109.02492\'}, \'description\': \'DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pre-training task on a large amount of long dialogue data for further training. Here is a base version of DialogLED, the input length is limited to 16,384 in the pre-training phase.\'}', metadata={})]


 I am designing a parking spot detector for a car parking management solution. I want the model to estimage the depth of the car park.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 We are researching multi-view 3D scanning, and we would like to improve our depth estimation with a pretrained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 I am building an app to detect objects in an image from a URL. How can I experiment with object detection models using transformers?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'fcakyon/yolov5s-v7.0\', \'api_call\': "yolov5.load(\'fcakyon/yolov5s-v7.0\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic\': False, \'multi_label\': False, \'max_det\': 1000, \'img\': \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\', \'size\': 640, \'augment\': True}, \'python_environment_requirements\': \'pip install -U yolov5\', \'example_code\': "import yolov5\\nmodel = yolov5.load(\'fcakyon/yolov5s-v7.0\')\\nmodel.conf = 0.25\\nmodel.iou = 0.45\\nmodel.agnostic = False\\nmodel.multi_label = False\\nmodel.max_det = 1000\\nimg = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model(img)\\nresults = model(img, size=640)\\nresults = model(img, augment=True)\\npredictions = results.pred[0]\\nboxes = predictions[:, :4]\\nscores = predictions[:, 4]\\ncategories = predictions[:, 5]\\nresults.show()\\nresults.save(save_dir=\'results/\')", \'performance\': {\'dataset\': \'detection-datasets/coco\', \'accuracy\': None}, \'description\': \'Yolov5s-v7.0 is an object detection model trained on the COCO dataset. It can detect objects in images and return their bounding boxes, scores, and categories.\'}', metadata={})]


 I have a picture of my backyard, but I am not sure if a cat sneaked in. Can you help me find out if it is a cat in the picture?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Summarization\', \'api_name\': \'distilbart-cnn-12-6-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/distilbart-cnn-12-6-samsum\')", \'api_arguments\': {\'model\': \'philschmid/distilbart-cnn-12-6-samsum\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/distilbart-cnn-12-6-samsum)\\nconversation = \'\'\'Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? \\nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \\nJeff: ok.\\nJeff: and how can I get started? \\nJeff: where can I find documentation? \\nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face <br />\\n\'\'\'\\nsummarizer(conversation)", \'performance\': {\'dataset\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 41.09, \'ROUGE-2\': 20.746, \'ROUGE-L\': 31.595, \'ROUGE-LSUM\': 38.339}}, \'description\': \'This model is a DistilBART-based text summarization model trained on the SAMsum dataset. It can be used to generate summaries of conversational text.\'}', metadata={})]


 Our hospital needs to analyze digital blood samples in order to detect and count platelets, red blood cells, and white blood cells.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Blood Cell Detection\', \'api_name\': \'keremberke/yolov8n-blood-cell-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-blood-cell-detection\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': \'ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "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://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()", \'performance\': {\'dataset\': \'blood-cell-object-detection\', \'accuracy\': 0.893}, \'description\': \'This model detects blood cells in images, specifically Platelets, RBC, and WBC. It is based on the YOLOv8 architecture and trained on the blood-cell-object-detection dataset.\'}', metadata={})]


 Develop a script that extracts a table from a given document and then visualize the results.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are now working on an architectural image generation project to generate different images of architecture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are designing a book cover where the book is about love and roses. We have the idea of \"A head full of roses.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Help me generate a realistic bedroom interior image that can be used as reference for a 3D model being created for a virtual reality game.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'microsoft/git-base-coco\', \'api_call\': "pipeline(\'text-generation\', model=\'microsoft/git-base-coco\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'See the model hub for fine-tuned versions on a task that interests you.\', \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Refer to the paper for evaluation results.\'}, \'description\': \'GIT (short for GenerativeImage2Text) model, base-sized version, fine-tuned on COCO. It was introduced in the paper GIT: A Generative Image-to-text Transformer for Vision and Language by Wang et al. and first released in this repository. The model is a Transformer decoder conditioned on both CLIP image tokens and text tokens. It can be used for tasks like image and video captioning, visual question answering (VQA) on images and videos, and even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text).\'}', metadata={})]


 You feel lonely and need to generate a picture of a butterfly. How would you proceed?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Conversational\', \'api_name\': \'microsoft/GODEL-v1_1-large-seq2seq\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'microsoft/GODEL-v1_1-large-seq2seq\')", \'api_arguments\': {\'instruction\': \'Instruction: given a dialog context, you need to response empathically.\', \'knowledge\': \'\', \'dialog\': [\'Does money buy happiness?\', \'It is a question. Money buys you a lot of things, but not enough to buy happiness.\', \'What is the best way to buy happiness ?\']}, \'python_environment_requirements\': {\'transformers\': \'AutoTokenizer, AutoModelForSeq2SeqLM\'}, \'example_code\': "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained(microsoft/GODEL-v1_1-large-seq2seq)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(microsoft/GODEL-v1_1-large-seq2seq)\\ndef generate(instruction, knowledge, dialog):\\n if knowledge != \'\':\\n knowledge = \'[KNOWLEDGE] \' + knowledge\\n dialog = \' EOS \'.join(dialog)\\n query = f{instruction} [CONTEXT] {dialog} {knowledge}\\n input_ids = tokenizer(f{query}, return_tensors=pt).input_ids\\n outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)\\n output = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n return output\\n\\ninstruction = f\'Instruction: given a dialog context, you need to response empathically.\'\\nknowledge = \'\'\\ndialog = [\\n \'Does money buy happiness?\',\\n \'It is a question. Money buys you a lot of things, but not enough to buy happiness.\',\\n \'What is the best way to buy happiness ?\'\\n]\\nresponse = generate(instruction, knowledge, dialog)\\nprint(response)", \'performance\': {\'dataset\': \'Reddit discussion thread, instruction and knowledge grounded dialogs\', \'accuracy\': \'Not provided\'}, \'description\': \'GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs. The v1.1 model is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs.\'}', metadata={})]


 We need an AI-generated insect image for our biology article about African habitats.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Our client is involved in a sports organization, and they require a solution to classify sports videos efficiently.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'videomae-small-finetuned-ssv2\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-small-finetuned-ssv2\')", \'api_arguments\': {\'model_name\': \'MCG-NJU/videomae-small-finetuned-ssv2\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification\', \'numpy\': \'import numpy as np\', \'torch\': \'import torch\'}, \'example_code\': \'video = 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])\', \'performance\': {\'dataset\': \'Something-Something V2\', \'accuracy\': {\'top-1\': 66.8, \'top-5\': 90.3}}, \'description\': \'VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.\'}', metadata={})]


 We need to create a content filter for images submitted by users in our online community. We want to detect and filter out adult content and offensive images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are an animal rescue organization, and we are looking for a way to automatically identify if images uploaded to our site contain cats, dogs, or birds.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Create a machine learning-based image recognition tool that can identify whether an animal in an image is a cat or a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'zero-shot-object-detection\', \'api_name\': \'google/owlvit-base-patch16\', \'api_call\': "OwlViTForObjectDetection.from_pretrained(\'google/owlvit-base-patch16\')", \'api_arguments\': [\'texts\', \'images\'], \'python_environment_requirements\': [\'requests\', \'PIL\', \'torch\', \'transformers\'], \'example_code\': \'processor = OwlViTProcessor.from_pretrained(google/owlvit-base-patch16)\\nmodel = OwlViTForObjectDetection.from_pretrained(google/owlvit-base-patch16)\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ntexts = [[a photo of a cat, a photo of a dog]]\\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)\', \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not provided\'}, \'description\': \'OWL-ViT is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. OWL-ViT uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features.\'}', metadata={})]


 I want to find out whether a given review is positive or negative. I need an API that can help me with sentiment analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Summarization\', \'api_name\': \'distilbart-cnn-12-6-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/distilbart-cnn-12-6-samsum\')", \'api_arguments\': {\'model\': \'philschmid/distilbart-cnn-12-6-samsum\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/distilbart-cnn-12-6-samsum)\\nconversation = \'\'\'Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? \\nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \\nJeff: ok.\\nJeff: and how can I get started? \\nJeff: where can I find documentation? \\nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face <br />\\n\'\'\'\\nsummarizer(conversation)", \'performance\': {\'dataset\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 41.09, \'ROUGE-2\': 20.746, \'ROUGE-L\': 31.595, \'ROUGE-LSUM\': 38.339}}, \'description\': \'This model is a DistilBART-based text summarization model trained on the SAMsum dataset. It can be used to generate summaries of conversational text.\'}', metadata={})]


 I am a traveler and I want to know the best time to visit a Chinese historical site. Take a look at an image and tell me whether it is a good time to visit the site or not.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 Organizing a special event and want to identify the place where street pictures were taken for invitations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Graph Machine Learning\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'graphormer-base-pcqm4mv1\', \'api_call\': "AutoModel.from_pretrained(\'graphormer-base-pcqm4mv1\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'See the Graph Classification with Transformers tutorial\', \'performance\': {\'dataset\': \'PCQM4M-LSC\', \'accuracy\': \'1st place on the KDD CUP 2021 (quantum prediction track)\'}, \'description\': \'The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSC, and which got 1st place on the KDD CUP 2021 (quantum prediction track). Developed by Microsoft, this model should be used for graph classification tasks or graph representation tasks; the most likely associated task is molecule modeling. It can either be used as such, or finetuned on downstream tasks.\'}', metadata={})]


 Our client wants us to monitor consumer sentiment on their e-commerce platform. We must identify low-rated product reviews in six languages: English, Dutch, German, French, Italian, and Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'bert-base-multilingual-uncased-sentiment\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'nlptown/bert-base-multilingual-uncased-sentiment\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "result = sentiment_pipeline(\'I love this product!\')", \'performance\': {\'dataset\': [{\'language\': \'English\', \'accuracy\': {\'exact\': \'67%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Dutch\', \'accuracy\': {\'exact\': \'57%\', \'off-by-1\': \'93%\'}}, {\'language\': \'German\', \'accuracy\': {\'exact\': \'61%\', \'off-by-1\': \'94%\'}}, {\'language\': \'French\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'94%\'}}, {\'language\': \'Italian\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Spanish\', \'accuracy\': {\'exact\': \'58%\', \'off-by-1\': \'95%\'}}]}, \'description\': \'This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).\'}', metadata={})]


 Write a script for an online forum moderator that will help them to detect gibberish text in a post.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Keras\', \'functionality\': \'anomaly-detection\', \'api_name\': \'keras-io/timeseries-anomaly-detection\', \'api_call\': "TFAutoModelForSequenceClassification.from_pretrained(\'keras-io/timeseries-anomaly-detection\')", \'api_arguments\': {\'optimizer\': {\'name\': \'Adam\', \'learning_rate\': 0.001, \'decay\': 0.0, \'beta_1\': 0.9, \'beta_2\': 0.999, \'epsilon\': 1e-07, \'amsgrad\': False}, \'training_precision\': \'float32\'}, \'python_environment_requirements\': [\'tensorflow\', \'keras\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Numenta Anomaly Benchmark(NAB)\', \'accuracy\': {\'Train Loss\': 0.006, \'Validation Loss\': 0.008}}, \'description\': \'This script demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data. We will use the Numenta Anomaly Benchmark(NAB) dataset. It provides artifical timeseries data containing labeled anomalous periods of behavior. Data are ordered, timestamped, single-valued metrics.\'}', metadata={})]


As a restaurant owner, I want to know if the total revenue for last week met our target revenue.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'ToddGoldfarb/Cadet-Tiny\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'ToddGoldfarb/Cadet-Tiny\', low_cpu_mem_usage=True)", \'api_arguments\': {\'pretrained_model\': \'t5-small\', \'model_max_length\': 512}, \'python_environment_requirements\': {\'torch\': \'\', \'transformers\': \'\', \'colorful\': \'\'}, \'example_code\': "import torch\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nimport colorful as cf\\ncf.use_true_colors()\\ncf.use_style(\'monokai\')\\nclass CadetTinyAgent:\\n def init(self):\\n  print(cf.bold | cf.purple(Waking up Cadet-Tiny...))\\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 = \\ndef observe(self, observation):\\n self.conversation_history = self.conversation_history + observation\\n # The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.\\n if len(self.conversation_history) > 400:\\n self.conversation_history = self.conversation_history[112:]\\ndef set_input(self, situation_narrative=, role_instruction=):\\n input_text = dialogue: \\n if situation_narrative != :\\n input_text = input_text + situation_narrative\\n if role_instruction != :\\n input_text = input_text +  <SEP>  + role_instruction\\n input_text = input_text +  <TURN>  + self.conversation_history\\n # Uncomment the line below to see what is fed to the model.\\n # print(input_text)\\n return input_text\\ndef generate(self, situation_narrative, role_instruction, user_response):\\n user_response = user_response +  <TURN> \\n self.observe(user_response)\\n input_text = self.set_input(situation_narrative, role_instruction)\\n inputs = self.tokenizer([input_text], return_tensors=pt).to(self.device)\\n # I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.\\n outputs = self.model.generate(inputs[input_ids], max_new_tokens=512, temperature=1, top_p=.95,\\n do_sample=True)\\n cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)\\n added_turn = cadet_response +  <TURN> \\n self.observe(added_turn)\\n return cadet_response\\ndef reset_history(self):\\n self.conversation_history = []\\ndef run(self):\\n def get_valid_input(prompt, default):\\n  while True:\\n   user_input = input(prompt)\\n   if user_input in [Y, N, y, n]:\\n    return user_input\\n   if user_input == :\\n    return default\\n  while True:\\n   continue_chat = \\n   # MODIFY THESE STRINGS TO YOUR LIKING :)\\n   situation_narrative = Imagine you are Cadet-Tiny talking to ???.\\n   role_instruction = You are Cadet-Tiny, and you are talking to ???.\\n   self.chat(situation_narrative, role_instruction)\\n   continue_chat = get_valid_input(cf.purple(Start a new conversation with new setup? [Y/N]:), Y)\\n   if continue_chat in [N, n]:\\n    break\\n   print(cf.blue(CT: See you!))\\ndef chat(self, situation_narrative, role_instruction):\\n print(cf.green(\\n  Cadet-Tiny is running! Input [RESET] to reset the conversation history and [END] to end the conversation.))\\n while True:\\n  user_input = input(You: )\\n  if user_input == [RESET]:\\n   self.reset_history()\\n   print(cf.green([Conversation history cleared. Chat with Cadet-Tiny!]))\\n   continue\\n  if user_input == [END]:\\n   break\\n  response = self.generate(situation_narrative, role_instruction, user_input)\\n  print(cf.blue(CT:  + response))\\ndef main():\\n print(cf.bold | cf.blue(LOADING MODEL))\\nCadetTiny = CadetTinyAgent()\\nCadetTiny.run()\\nif name == \'main\':\\n main()", \'performance\': {\'dataset\': \'allenai/soda\', \'accuracy\': \'\'}, \'description\': \'Cadet-Tiny is a very small conversational model trained off of the SODA dataset. Cadet-Tiny is intended for inference at the edge (on something as small as a 2GB RAM Raspberry Pi). Cadet-Tiny is trained off of the t5-small pretrained model from Google, and is, as a result, is about 2% of the size of the Cosmo-3B model.\'}', metadata={})]


 We are a sports analysis company that processes data from spreadsheets with game statistics. We need to identify the player who has scored the maximum goals in a given match.\n###Input: \"What player scored the most goals?\", \"Player,Goals\\nA,2\\nB,3\\nC,1\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 A manager in our company requires an aggregated answer of the highest and lowest sales numbers for a given period to evaluate the perfomance.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 A doctor needs to find medical information in a large document. We are building an AI model to help them extract answers to their questions. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are building a news classification system, and for Spanish articles, we have to categorize them into different sections.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Feature Extraction\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'DeepPavlov/rubert-base-cased\', \'api_call\': "AutoModel.from_pretrained(\'DeepPavlov/rubert-base-cased\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Russian part of Wikipedia and news data\', \'accuracy\': \'\'}, \'description\': \'RuBERT (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT[1].\'}', metadata={})]


 In my project, I need to classify news articles into their respective categories, such as technology, sports, and politics, without providing any labeled training data.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'laion/CLIP-ViT-g-14-laion2B-s34B-b88K\', \'api_call\': "pipeline(\'zero-shot-image-classification\', model=\'laion/CLIP-ViT-g-14-laion2B-s34B-b88K\')", \'api_arguments\': {\'image\': \'path/to/image/file\', \'class_names\': \'list_of_class_names\'}, \'python_environment_requirements\': {\'huggingface_hub\': \'0.0.17\', \'transformers\': \'4.11.3\', \'torch\': \'1.9.0\', \'torchvision\': \'0.10.0\'}, \'example_code\': None, \'performance\': {\'dataset\': None, \'accuracy\': None}, \'description\': \'A zero-shot image classification model based on OpenCLIP, which can classify images into various categories without requiring any training data for those categories.\'}', metadata={})]


 I have a Catalan language text and I need to publish it as a blog article in Spanish, so I want to translate my Catalan text into Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 Generate an automatic summarization of an executive's meeting notes without sharing too much details.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face\', \'functionality\': \'Visual Question Answering\', \'api_name\': \'sheldonxxxx/OFA_model_weights\', \'api_call\': "AutoModel.from_pretrained(\'sheldonxxxx/OFA_model_weights\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This is an unoffical mirror of the model weights for use with https://github.com/OFA-Sys/OFA. The original link is too slow when downloading from outside of China.\'}', metadata={})]


 A customer needs to translate a French email they received into Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Translation\', \'api_name\': \'opus-mt-fr-en\', \'api_call\': "pipeline(\'translation_fr_to_en\', model=\'Helsinki-NLP/opus-mt-fr-en\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "translation_pipeline(\'Bonjour, comment ça va?\')", \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.fr.en\': 33.1, \'newsdiscusstest2015-enfr.fr.en\': 38.7, \'newssyscomb2009.fr.en\': 30.3, \'news-test2008.fr.en\': 26.2, \'newstest2009.fr.en\': 30.2, \'newstest2010.fr.en\': 32.2, \'newstest2011.fr.en\': 33.0, \'newstest2012.fr.en\': 32.8, \'newstest2013.fr.en\': 33.9, \'newstest2014-fren.fr.en\': 37.8, \'Tatoeba.fr.en\': 57.5}}}, \'description\': \'Helsinki-NLP/opus-mt-fr-en is a machine translation model trained to translate from French to English. It is based on the Marian NMT framework and trained on the OPUS dataset.\'}', metadata={})]


 I am a writer, and I have written a long article, but I need to come up with a summary for my article to publish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 Your company has launched a new chatbot service. We need to create conversations with the customers to provide information about our products and answer their queries.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'csebuetnlp/mT5_multilingual_XLSum\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'csebuetnlp/mT5_multilingual_XLSum\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers==4.11.0.dev0\'], \'example_code\': "import re\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nWHITESPACE_HANDLER = lambda k: re.sub(\'\\\\s+\', \' \', re.sub(\'\\\\n+\', \' \', k.strip()))\\narticle_text = Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people\'s scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs spill over into misinformation about vaccines in general. The new policy covers long-approved vaccines, such as those against measles or hepatitis B. We\'re expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO, the post said, referring to the World Health Organization.\\nmodel_name = csebuetnlp/mT5_multilingual_XLSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\\ninput_ids = tokenizer(\\n [WHITESPACE_HANDLER(article_text)],\\n return_tensors=pt,\\n padding=max_length,\\n truncation=True,\\n max_length=512\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n max_length=84,\\n no_repeat_ngram_size=2,\\n num_beams=4\\n)[0]\\nsummary = tokenizer.decode(\\n output_ids,\\n skip_special_tokens=True,\\n clean_up_tokenization_spaces=False\\n)\\nprint(summary)", \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 36.5, \'ROUGE-2\': 13.934, \'ROUGE-L\': 28.988, \'ROUGE-LSUM\': 28.996, \'loss\': 2.067, \'gen_len\': 26.973}}, \'description\': \'This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. It is a multilingual abstractive summarization model that supports text-to-text generation for 43 languages.\'}', metadata={})]


 We are a travel company trying to offer personalized experiences to our customers. We want to implement a conversational AI to help users get information about travel destinations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Write a tweet on the topic of \"The Future of AI in Education\".\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'distilbert-base-uncased-finetuned-sst-2-english\', \'api_call\': "DistilBertForSequenceClassification.from_pretrained(\'distilbert-base-uncased-finetuned-sst-2-english\')", \'api_arguments\': [\'inputs\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': "import torch\\nfrom transformers import DistilBertTokenizer, DistilBertForSequenceClassification\\ntokenizer = DistilBertTokenizer.from_pretrained(\'distilbert-base-uncased\')\\nmodel = DistilBertForSequenceClassification.from_pretrained(\'distilbert-base-uncased-finetuned-sst-2-english\')\\ninputs = tokenizer(\'Hello, my dog is cute\', return_tensors=\'pt\')\\nwith torch.no_grad():\\n    logits = model(**inputs).logits\\npredicted_class_id = logits.argmax().item()\\nmodel.config.id2label[predicted_class_id]", \'performance\': {\'dataset\': \'glue\', \'accuracy\': 0.911}, \'description\': \'This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. It reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). This model can be used for topic classification.\'}', metadata={})]


 Create a poem about nature for a school assignment.\n###Input: Once upon a time, in a land of greenery and beauty,\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'table-question-answering-tapas\', \'api_call\': "pipeline(\'table-question-answering\', model=\'Meena/table-question-answering-tapas\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'This model can be loaded on the Inference API on-demand.\', \'performance\': {\'dataset\': [{\'name\': \'SQA (Sequential Question Answering by Microsoft)\', \'accuracy\': None}, {\'name\': \'WTQ (Wiki Table Questions by Stanford University)\', \'accuracy\': None}, {\'name\': \'WikiSQL (by Salesforce)\', \'accuracy\': None}]}, \'description\': \'TAPAS, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refuted by the contents of a table. It is a BERT-based model specifically designed (and pre-trained) for answering questions about tabular data. TAPAS uses relative position embeddings and has 7 token types that encode tabular structure. It is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising millions of tables from English Wikipedia and corresponding texts.\'}', metadata={})]


 We want to code a loading spinner to display when our system is undergoing maintenance. Can you generate the code for us?\n###Input: <noinput>\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 I am an English teacher. What kind of creative writing exercise could I give to my students that they could write a story using the words \"moon\", \"rabbit\", \"forest\", and \"magic\"?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 Our team needs to develop a tool that can translate articles from English to German in real-time.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Denoising Diffusion Probabilistic Models (DDPM)\', \'api_name\': \'google/ddpm-bedroom-256\', \'api_call\': "DDPMPipeline.from_pretrained(\'google/ddpm-bedroom-256\')", \'api_arguments\': \'None\', \'python_environment_requirements\': \'diffusers\', \'example_code\': \'!pip install diffusers\\nfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\\nmodel_id = google/ddpm-bedroom-256\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save(ddpm_generated_image.png)\', \'performance\': {\'dataset\': \'CIFAR10\', \'accuracy\': {\'Inception score\': 9.46, \'FID score\': 3.17}}, \'description\': \'We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.\'}', metadata={})]


 Design an efficient search engine that retrieves most relevant articles based on a pool of long text documents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'google/bigbird-pegasus-large-arxiv\', \'api_call\': "BigBirdPegasusForConditionalGeneration.from_pretrained(\'google/bigbird-pegasus-large-arxiv\')", \'api_arguments\': [\'attention_type\', \'block_size\', \'num_random_blocks\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained(google/bigbird-pegasus-large-arxiv)\\nmodel = BigBirdPegasusForConditionalGeneration.from_pretrained(google/bigbird-pegasus-large-arxiv)\\ntext = Replace me by any text you\'d like.\\ninputs = tokenizer(text, return_tensors=\'pt\')\\nprediction = model.generate(**inputs)\\nprediction = tokenizer.batch_decode(prediction)", \'performance\': {\'dataset\': \'scientific_papers\', \'accuracy\': {\'ROUGE-1\': 36.028, \'ROUGE-2\': 13.417, \'ROUGE-L\': 21.961, \'ROUGE-LSUM\': 29.648}}, \'description\': "BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. Moreover, BigBird comes along with a theoretical understanding of the capabilities of a complete transformer that the sparse model can handle. BigBird was introduced in this paper and first released in this repository. BigBird relies on block sparse attention instead of normal attention (i.e. BERT\'s attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. It has achieved SOTA on various tasks involving very long sequences such as long documents summarization, question-answering with long contexts."}', metadata={})]


 Our designer hired me to create an application to automatically translate colors from a design. Usually, our designer likes to work with English, to get the color in Italian.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 I am building an AI to create in work report, this model should be able to complete the sentence for the report.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


 A language learning application wants to generate fill-in-the-blank questions for learners. We need to mask sentences with a keyword to create these questions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 Our client has a medical report and we are trying to assist him in finding relevant information.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Denoising Diffusion Probabilistic Models (DDPM)\', \'api_name\': \'google/ddpm-bedroom-256\', \'api_call\': "DDPMPipeline.from_pretrained(\'google/ddpm-bedroom-256\')", \'api_arguments\': \'None\', \'python_environment_requirements\': \'diffusers\', \'example_code\': \'!pip install diffusers\\nfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\\nmodel_id = google/ddpm-bedroom-256\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save(ddpm_generated_image.png)\', \'performance\': {\'dataset\': \'CIFAR10\', \'accuracy\': {\'Inception score\': 9.46, \'FID score\': 3.17}}, \'description\': \'We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.\'}', metadata={})]


 I am a data scientist and need to find which among given documents is similar, provide me the example code.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Summarization\', \'api_name\': \'distilbart-cnn-12-6-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/distilbart-cnn-12-6-samsum\')", \'api_arguments\': {\'model\': \'philschmid/distilbart-cnn-12-6-samsum\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/distilbart-cnn-12-6-samsum)\\nconversation = \'\'\'Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? \\nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \\nJeff: ok.\\nJeff: and how can I get started? \\nJeff: where can I find documentation? \\nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face <br />\\n\'\'\'\\nsummarizer(conversation)", \'performance\': {\'dataset\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 41.09, \'ROUGE-2\': 20.746, \'ROUGE-L\': 31.595, \'ROUGE-LSUM\': 38.339}}, \'description\': \'This model is a DistilBART-based text summarization model trained on the SAMsum dataset. It can be used to generate summaries of conversational text.\'}', metadata={})]


 My company wants to analyze and cluster different movie scripts. I need a suggestion to embed the movie dialogue text into dense vector representations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Classification\', \'api_name\': \'MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary\')", \'api_arguments\': {\'premise\': \'string\', \'hypothesis\': \'string\'}, \'python_environment_requirements\': {\'transformers\': \'>=4.13\', \'torch\': \'any\'}, \'example_code\': \'from transformers import AutoTokenizer, AutoModelForSequenceClassification\\nimport torch\\ndevice = torch.device(cuda) if torch.cuda.is_available() else torch.device(cpu)\\nmodel_name = MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_name)\\npremise = I first thought that I liked the movie, but upon second thought it was actually disappointing.\\nhypothesis = The movie was good.\\ninput = tokenizer(premise, hypothesis, truncation=True, return_tensors=pt)\\noutput = model(input[input_ids].to(device))\\nprediction = torch.softmax(output[logits][0], -1).tolist()\\nlabel_names = [entailment, not_entailment]\\nprediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}\\nprint(prediction)\', \'performance\': {\'dataset\': {\'mnli-m-2c\': {\'accuracy\': 0.925}, \'mnli-mm-2c\': {\'accuracy\': 0.922}, \'fever-nli-2c\': {\'accuracy\': 0.892}, \'anli-all-2c\': {\'accuracy\': 0.676}, \'anli-r3-2c\': {\'accuracy\': 0.665}, \'lingnli-2c\': {\'accuracy\': 0.888}}}, \'description\': \'This model was trained on 782 357 hypothesis-premise pairs from 4 NLI datasets: MultiNLI, Fever-NLI, LingNLI and ANLI. The base model is DeBERTa-v3-xsmall from Microsoft. The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective.\'}', metadata={})]


 Analyze how characters in a book are connected and if they share any similarity based on their conversation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'facebook/mask2former-swin-large-coco-panoptic\', \'api_call\': "Mask2FormerForUniversalSegmentation.from_pretrained(\'facebook/mask2former-swin-large-coco-panoptic\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'requests\', \'torch\', \'PIL\', \'transformers\'], \'example_code\': \'processor = AutoImageProcessor.from_pretrained(facebook/mask2former-swin-large-coco-panoptic)\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained(facebook/mask2former-swin-large-coco-panoptic)\\nurl = http://images.cocodataset.org/val2017/000000039769.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)\\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]\', \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not provided\'}, \'description\': \'Mask2Former model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency.\'}', metadata={})]


 We are a company specializing in building automation systems for homes. We'd like to convert our written rules into an audio format for user interaction purposes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 We are developing a virtual assistant. One of its major functionalities is to convert written text to speech.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'videomae-small-finetuned-ssv2\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-small-finetuned-ssv2\')", \'api_arguments\': {\'model_name\': \'MCG-NJU/videomae-small-finetuned-ssv2\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification\', \'numpy\': \'import numpy as np\', \'torch\': \'import torch\'}, \'example_code\': \'video = 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])\', \'performance\': {\'dataset\': \'Something-Something V2\', \'accuracy\': {\'top-1\': 66.8, \'top-5\': 90.3}}, \'description\': \'VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.\'}', metadata={})]


 I have a recorded meeting between multiple participants. Identify when two or more people are speaking simultaneously.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'pyannote.audio\', \'functionality\': \'overlapped-speech-detection\', \'api_name\': \'pyannote/overlapped-speech-detection\', \'api_call\': "pipeline.from_pretrained(\'pyannote/overlapped-speech-detection\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote.audio 2.1\'], \'example_code\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/overlapped-speech-detection, use_auth_token=ACCESS_TOKEN_GOES_HERE)\\noutput = pipeline(audio.wav)\\nfor speech in output.get_timeline().support():\\n  # two or more speakers are active between speech.start and speech.end\\n  ...\', \'performance\': {\'dataset\': \'ami\', \'accuracy\': None}, \'description\': \'Automatic overlapped speech detection using pyannote.audio framework. The model detects when two or more speakers are active in an audio file.\'}', metadata={})]


 I have a collection of audio recordings from interviews that I need transcribed into text. How can I achieve this using automatic speech recognition?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Text-to-Text Generation\', \'api_name\': \'optimum/t5-small\', \'api_call\': "ORTModelForSeq2SeqLM.from_pretrained(\'optimum/t5-small\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'optimum.onnxruntime\'], \'example_code\': \'from transformers import AutoTokenizer, pipeline\\nfrom optimum.onnxruntime import ORTModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained(optimum/t5-small)\\nmodel = ORTModelForSeq2SeqLM.from_pretrained(optimum/t5-small)\\ntranslator = pipeline(translation_en_to_fr, model=model, tokenizer=tokenizer)\\nresults = translator(My name is Eustache and I have a pet raccoon)\\nprint(results)\', \'performance\': {\'dataset\': \'c4\', \'accuracy\': \'N/A\'}, \'description\': \'T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. It can be used for translation, text-to-text generation, and summarization.\'}', metadata={})]