 Our gaming company is looking for a reinforcement learning solution to implement an artificial agent that can play SoccerTwos proficiently.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Unity ML-Agents Library\', \'functionality\': \'Train and play SoccerTwos\', \'api_name\': \'poca-SoccerTwosv2\', \'api_call\': "mlagents-load-from-hf --repo-id=\'Raiden-1001/poca-SoccerTwosv2\' --local-dir=\'./downloads\'", \'api_arguments\': [\'your_configuration_file_path.yaml\', \'run_id\'], \'python_environment_requirements\': [\'ml-agents\'], \'example_code\': \'mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume\', \'performance\': {\'dataset\': \'SoccerTwos\', \'accuracy\': \'Not provided\'}, \'description\': \'A trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library.\'}', metadata={})]


 Develop a language understanding feature for a chatbot that can help tourists with information on visiting Indonesia.\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 need to analyze images in real-time feeds from different locations for object recognition.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'hustvl/yolos-tiny\', \'api_call\': "YolosForObjectDetection.from_pretrained(\'hustvl/yolos-tiny\')", \'api_arguments\': {\'images\': \'image\', \'return_tensors\': \'pt\'}, \'python_environment_requirements\': [\'transformers\', \'PIL\', \'requests\'], \'example_code\': "from transformers import YolosFeatureExtractor, YolosForObjectDetection\\nfrom PIL import Image\\nimport requests\\nurl = \'http://images.cocodataset.org/val2017/000000039769.jpg\'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = YolosFeatureExtractor.from_pretrained(\'hustvl/yolos-tiny\')\\nmodel = YolosForObjectDetection.from_pretrained(\'hustvl/yolos-tiny\')\\ninputs = feature_extractor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\\nlogits = outputs.logits\\nbboxes = outputs.pred_boxes", \'performance\': {\'dataset\': \'COCO 2017 validation\', \'accuracy\': \'28.7 AP\'}, \'description\': \'YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a bipartite matching loss: one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a no object as class and no bounding box as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.\'}', metadata={})]


 Our travel agency needs to build a chatbot that helps tourists find the best attractions in various destinations. The chatbot should answer questions related to tourist attractions.\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 want to develop a chatbot that can engage with multilingual users. Please help us create a model to encode sentences and understand user input in various languages.\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 need to create a demo for a 3D printing company by generating images of some examples that the business will print.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'gsdf/Counterfeit-V2.5\', \'api_call\': "pipeline(\'text-to-image\', model=\'gsdf/Counterfeit-V2.5\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field\', \'performance\': {\'dataset\': \'EasyNegative\', \'accuracy\': \'Not provided\'}, \'description\': \'Counterfeit-V2.5 is a text-to-image model that generates anime-style images based on text prompts. It has been updated for ease of use and can be used with negative prompts to create high-quality images.\'}', metadata={})]


 I work at an art school and our professor wants to create an AI chatbot that can study an image of a painting and answer questions about it.\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 working on a project to develop an AI-based method to provide answers to questions about charts and diagrams. Please provide the necessary code for using the Pix2Struct model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'google/pix2struct-chartqa-base\', \'api_call\': "Pix2StructForConditionalGeneration.from_pretrained(\'google/pix2struct-chartqa-base\')", \'api_arguments\': [\'t5x_checkpoint_path\', \'pytorch_dump_path\', \'use-large\'], \'python_environment_requirements\': \'transformers\', \'example_code\': \'python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE\', \'performance\': {\'dataset\': \'ChartQA\', \'accuracy\': \'Not provided\'}, \'description\': \'Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The model is pretrained by learning to parse masked screenshots of web pages into simplified HTML. It can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.\'}', metadata={})]


 Create a personalized online ad campaign for our new clothing line. The ad should generate a video showing a man wearing a stylish suit while walking in the city.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 We need to generate a short video showing Spider-Man water skiing in redshift style based on a textual prompt.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video Generation\', \'api_name\': \'redshift-man-skiing\', \'api_call\': "TuneAVideoPipeline.from_pretrained(\'nitrosocke/redshift-diffusion\', unet=UNet3DConditionModel.from_pretrained(\'Tune-A-Video-library/redshift-man-skiing\', subfolder=\'unet\', torch_dtype=torch.float16), torch_dtype=torch.float16)", \'api_arguments\': {\'prompt\': \'string\', \'video_length\': \'int\', \'height\': \'int\', \'width\': \'int\', \'num_inference_steps\': \'int\', \'guidance_scale\': \'float\'}, \'python_environment_requirements\': [\'torch\', \'tuneavideo\'], \'example_code\': "from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline\\nfrom tuneavideo.models.unet import UNet3DConditionModel\\nfrom tuneavideo.util import save_videos_grid\\nimport torch\\npretrained_model_path = nitrosocke/redshift-diffusion\\nunet_model_path = Tune-A-Video-library/redshift-man-skiing\\nunet = UNet3DConditionModel.from_pretrained(unet_model_path, subfolder=\'unet\', torch_dtype=torch.float16).to(\'cuda\')\\npipe = TuneAVideoPipeline.from_pretrained(pretrained_model_path, unet=unet, torch_dtype=torch.float16).to(cuda)\\npipe.enable_xformers_memory_efficient_attention()\\nprompt = (redshift style) spider man is skiing\\nvideo = pipe(prompt, video_length=8, height=512, width=512, num_inference_steps=50, guidance_scale=7.5).videos\\nsave_videos_grid(video, f./{prompt}.gif)", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': "Tune-A-Video - Redshift is a text-to-video generation model based on the nitrosocke/redshift-diffusion model. It generates videos based on textual prompts, such as \'a man is skiing\' or \'(redshift style) spider man is skiing\'."}', metadata={})]


 Our company develops travel guides in Polish language. We are now planning to use artificial intelligence for quick captioning of Polish images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'azwierzc/vilt-b32-finetuned-vqa-pl\', \'api_call\': "pipeline(\'visual-question-answering\', model=\'azwierzc/vilt-b32-finetuned-vqa-pl\')", \'api_arguments\': {\'image\': \'path_to_image\', \'question\': \'question_text\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Visual Question Answering model fine-tuned on the Polish language.\'}', metadata={})]


 A user has difficulty in visualizing images and needs help answering questions about an image. We need a model to answer questions based on the image provided.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 A real estate company wants to extract information from a scan of a property listing. They need a solution to recognize and retrieve critical information from the OCR of the scan.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'kha-white/manga-ocr-base\', \'api_call\': "pipeline(\'ocr\', model=\'kha-white/manga-ocr-base\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'manga109s\', \'accuracy\': \'\'}, \'description\': \'Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses Vision Encoder Decoder framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: both vertical and horizontal text, text with furigana, text overlaid on images, wide variety of fonts and font styles, and low quality images.\'}', metadata={})]


 We want to utilize the machine learning model for predicting molecular properties in the drug discovery domain.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions Prediction\', \'api_name\': \'bibekbehera/autotrain-numeric_prediction-40376105019\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'bibekbehera/autotrain-data-numeric_prediction\', \'accuracy\': {\'Loss\': 0.152, \'R2\': 0.659, \'MSE\': 0.023, \'MAE\': 0.062, \'RMSLE\': 0.105}}, \'description\': \'A tabular regression model trained with AutoTrain to predict carbon emissions based on input features.\'}', metadata={})]


 We need to estimate the depth of a scene in an image using a pretrained model. Can you please suggest a way?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Depth Estimation\', \'api_name\': \'glpn-kitti\', \'api_call\': "GLPNForDepthEstimation.from_pretrained(\'vinvino02/glpn-kitti\')", \'api_arguments\': \'images, return_tensors\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation\\nimport torch\\nimport numpy as np\\nfrom PIL import Image\\nimport requests\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = GLPNFeatureExtractor.from_pretrained(vinvino02/glpn-kitti)\\nmodel = GLPNForDepthEstimation.from_pretrained(vinvino02/glpn-kitti)\\ninputs = feature_extractor(images=image, return_tensors=pt)\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\nprediction = torch.nn.functional.interpolate(\\n predicted_depth.unsqueeze(1),\\n size=image.size[::-1],\\n mode=bicubic,\\n align_corners=False,\\n)\\noutput = prediction.squeeze().cpu().numpy()\\nformatted = (output * 255 / np.max(output)).astype(uint8)\\ndepth = Image.fromarray(formatted)\', \'performance\': {\'dataset\': \'KITTI\', \'accuracy\': \'Not provided\'}, \'description\': \'Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.\'}', metadata={})]


 Our company is working on an autonomous robot and needs a solution to estimate the depth of objects in its environment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Depth Estimation\', \'api_name\': \'glpn-kitti\', \'api_call\': "GLPNForDepthEstimation.from_pretrained(\'vinvino02/glpn-kitti\')", \'api_arguments\': \'images, return_tensors\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation\\nimport torch\\nimport numpy as np\\nfrom PIL import Image\\nimport requests\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = GLPNFeatureExtractor.from_pretrained(vinvino02/glpn-kitti)\\nmodel = GLPNForDepthEstimation.from_pretrained(vinvino02/glpn-kitti)\\ninputs = feature_extractor(images=image, return_tensors=pt)\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n predicted_depth = outputs.predicted_depth\\nprediction = torch.nn.functional.interpolate(\\n predicted_depth.unsqueeze(1),\\n size=image.size[::-1],\\n mode=bicubic,\\n align_corners=False,\\n)\\noutput = prediction.squeeze().cpu().numpy()\\nformatted = (output * 255 / np.max(output)).astype(uint8)\\ndepth = Image.fromarray(formatted)\', \'performance\': {\'dataset\': \'KITTI\', \'accuracy\': \'Not provided\'}, \'description\': \'Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.\'}', metadata={})]


 I want to create a video streaming app for plants, in which plants are categorized by species. I need to find the species of plants in an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'GreeneryScenery/SheepsControlV5\', \'api_call\': "pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV5\')", \'api_arguments\': {\'input_image\': \'path/to/image/file\'}, \'python_environment_requirements\': {\'huggingface_hub\': \'>=0.0.17\', \'transformers\': \'>=4.13.0\', \'torch\': \'>=1.10.0\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'poloclub/diffusiondb\', \'accuracy\': \'Not provided\'}, \'description\': \'SheepsControlV5 is an image-to-image model trained on the poloclub/diffusiondb dataset. It is designed for transforming input images into a different style or representation.\'}', metadata={})]


 Develop an image classifier for distinguishing images of cats and dogs.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 We are building AI glasses that should tell us about things that we are seeing with classifications. We want to use a visual transformer architecture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\', \'api_call\': "pipeline(\'image-classification\', \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\')", \'api_arguments\': {\'model_name\': \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\'}, \'python_environment_requirements\': {\'transformers\': \'4.28.1\', \'pytorch\': \'2.0.0+cu118\', \'datasets\': \'2.11.0\', \'tokenizers\': \'0.13.3\'}, \'example_code\': "from transformers import pipeline\\nimage_classifier = pipeline(\'image-classification\', \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\')\\nresult = image_classifier(\'path/to/image.jpg\')", \'performance\': {\'dataset\': \'None\', \'accuracy\': 0.7744}, \'description\': \'This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It is designed for image classification tasks, specifically for diabetic retinopathy detection.\'}', metadata={})]


 A marketing firm has asked us to build an application to classify social media images into various categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 John is a car enthusiast and he wants to build an application that can recognize the car brand in a picture he takes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'tejas23/autotrain-amx2-1702259725\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': {\'accuracy\': 0.827}}, \'description\': \'Multi-class Classification Model for Carbon Emissions\'}', metadata={})]


 We are building an app to help Counter-Strike: Global Offensive players improve their gameplay. We need to detect players in a live game of CS:GO.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8n-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-csgo-player-detection\')", \'api_arguments\': {\'image\': \'URL or local path to image\'}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8n-csgo-player-detection\')\\n\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\n\\nresults = model.predict(image)\\n\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.844}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players with supported labels: [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


 The security department needs assistance to detect suspicious objects and people using a zero-shot text-conditioned object detection system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Detect Bordered and Borderless tables in documents\', \'api_name\': \'TahaDouaji/detr-doc-table-detection\', \'api_call\': "DetrForObjectDetection.from_pretrained(\'TahaDouaji/detr-doc-table-detection\')", \'api_arguments\': [\'images\', \'return_tensors\', \'threshold\'], \'python_environment_requirements\': [\'transformers\', \'torch\', \'PIL\', \'requests\'], \'example_code\': \'from transformers import DetrImageProcessor, DetrForObjectDetection\\nimport torch\\nfrom PIL import Image\\nimport requests\\nimage = Image.open(IMAGE_PATH)\\nprocessor = DetrImageProcessor.from_pretrained(TahaDouaji/detr-doc-table-detection)\\nmodel = DetrForObjectDetection.from_pretrained(TahaDouaji/detr-doc-table-detection)\\ninputs = processor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\\ntarget_sizes = torch.tensor([image.size[::-1]])\\nresults = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]\\nfor score, label, box in zip(results[scores], results[labels], results[boxes]):\\n box = [round(i, 2) for i in box.tolist()]\\n print(\\n fDetected {model.config.id2label[label.item()]} with confidence \\n f{round(score.item(), 3)} at location {box}\\n )\', \'performance\': {\'dataset\': \'ICDAR2019 Table Dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'detr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents, based on facebook/detr-resnet-50.\'}', metadata={})]


 As a specialist in computer vision, we need to use the OwlViT model to identify objects in an image described by specific text phrases like \"a photo of a cat\" and \"a photo of 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-large-patch14\', \'api_call\': "OwlViTForObjectDetection.from_pretrained(\'google/owlvit-large-patch14\')", \'api_arguments\': {\'model_name\': \'google/owlvit-large-patch14\'}, \'python_environment_requirements\': [\'torch\', \'transformers\', \'PIL\', \'requests\'], \'example_code\': [\'import requests\', \'from PIL import Image\', \'import torch\', \'from transformers import OwlViTProcessor, OwlViTForObjectDetection\', \'processor = OwlViTProcessor.from_pretrained(google/owlvit-large-patch14)\', \'model = OwlViTForObjectDetection.from_pretrained(google/owlvit-large-patch14)\', \'url = http://images.cocodataset.org/val2017/000000039769.jpg\', \'image = Image.open(requests.get(url, stream=True).raw)\', \'texts = [[a photo of a cat, a photo of a dog]\', \'inputs = processor(text=texts, images=image, return_tensors=pt)\', \'outputs = model(**inputs)\', \'target_sizes = torch.Tensor([image.size[::-1]])\', \'results = processor.post_process(outputs=outputs, target_sizes=target_sizes)\', \'i = 0\', \'text = texts[i]\', \'boxes, scores, labels = results[i][boxes], results[i][scores], results[i][labels]\', \'score_threshold = 0.1\', \'for box, score, label in zip(boxes, scores, labels):\', \' box = [round(i, 2) for i in box.tolist()]\', \' if score >= score_threshold:\', \' print(fDetected {text[label]} with confidence {round(score.item(), 3)} at location {box})\'], \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not specified\'}, \'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. It 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. OWL-ViT is trained on publicly available image-caption data and fine-tuned on publicly available object detection datasets such as COCO and OpenImages.\'}', metadata={})]


 A medical research team requests an automated procedure for detecting blood cells in microscopic images of blood samples. Develop a solution to address this need.\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={})]


 Our client has a traffic camera system and wants to detect vehicles in the images captured. Implement a solution.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'License Plate Detection\', \'api_name\': \'keremberke/yolov5s-license-plate\', \'api_call\': "yolov5.load(\'keremberke/yolov5s-license-plate\')", \'api_arguments\': {\'img\': \'image url or path\', \'size\': \'image resize dimensions\', \'augment\': \'optional, test time augmentation\'}, \'python_environment_requirements\': \'pip install -U yolov5\', \'example_code\': [\'import yolov5\', "model = yolov5.load(\'keremberke/yolov5s-license-plate\')", \'model.conf = 0.25\', \'model.iou = 0.45\', \'model.agnostic = False\', \'model.multi_label = False\', \'model.max_det = 1000\', "img = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model(img, size=640)\', \'results = model(img, augment=True)\', \'predictions = results.pred[0]\', \'boxes = predictions[:, :4]\', \'scores = predictions[:, 4]\', \'categories = predictions[:, 5]\', \'results.show()\', "results.save(save_dir=\'results/\')"], \'performance\': {\'dataset\': \'keremberke/license-plate-object-detection\', \'accuracy\': 0.985}, \'description\': \'A YOLOv5 based license plate detection model trained on a custom dataset.\'}', metadata={})]


 Detect the location of players in an image from a Counter-Strike: Global Offensive (CS:GO) game.\n###Input: image_path = \"path/to/your/csgo_image.jpg\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8n-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-csgo-player-detection\')", \'api_arguments\': {\'image\': \'URL or local path to image\'}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8n-csgo-player-detection\')\\n\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\n\\nresults = model.predict(image)\\n\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.844}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players with supported labels: [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


 Develop a fashion app that segments and identifies clothing items in an uploaded image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'mattmdjaga/segformer_b2_clothes\', \'api_call\': "SegformerForSemanticSegmentation.from_pretrained(\'mattmdjaga/segformer_b2_clothes\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'transformers\', \'PIL\', \'requests\', \'matplotlib\', \'torch\'], \'example_code\': "from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nimport matplotlib.pyplot as plt\\nimport torch.nn as nn\\nextractor = AutoFeatureExtractor.from_pretrained(\'mattmdjaga/segformer_b2_clothes\')\\nmodel = SegformerForSemanticSegmentation.from_pretrained(\'mattmdjaga/segformer_b2_clothes\')\\nurl = \'https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80\'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = extractor(images=image, return_tensors=\'pt\')\\noutputs = model(**inputs)\\nlogits = outputs.logits.cpu()\\nupsampled_logits = nn.functional.interpolate(logits, size=image.size[::-1], mode=\'bilinear\', align_corners=False)\\npred_seg = upsampled_logits.argmax(dim=1)[0]\\nplt.imshow(pred_seg)", \'performance\': {\'dataset\': \'mattmdjaga/human_parsing_dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'SegFormer model fine-tuned on ATR dataset for clothes segmentation.\'}', metadata={})]


 Our team is working on a project to develop autonomous vehicles. We need a model to identify different segments of an image captured from the vehicle's camera.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 A real estate agency needs an application that can transform the floor plan images into simple straight line drawings, simplifying the visualization of the properties.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Lykon/DreamShaper\', \'api_call\': "pipeline(\'text-to-image\', model=Lykon/DreamShaper)", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': \'https://huggingface.co/spaces/Lykon/DreamShaper-webui\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Dream Shaper is a text-to-image model that generates artistic images based on the given input text. Read more about this model here: https://civitai.com/models/4384/dreamshaper\'}', metadata={})]


 I'm working on creating images of various scenes based on their textual descriptions. The models should also consider the actual positions and poses of the objects in the scenes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'gsdf/Counterfeit-V2.5\', \'api_call\': "pipeline(\'text-to-image\', model=\'gsdf/Counterfeit-V2.5\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field\', \'performance\': {\'dataset\': \'EasyNegative\', \'accuracy\': \'Not provided\'}, \'description\': \'Counterfeit-V2.5 is a text-to-image model that generates anime-style images based on text prompts. It has been updated for ease of use and can be used with negative prompts to create high-quality images.\'}', metadata={})]


 Develop a new product that uses machine learning to enhance the quality of low-resolution images by upscaling them to twice their size.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image Upscaling\', \'api_name\': \'stabilityai/sd-x2-latent-upscaler\', \'api_call\': \'StableDiffusionLatentUpscalePipeline.from_pretrained(stabilityai/sd-x2-latent-upscaler, torch_dtype=torch.float16)\', \'api_arguments\': {\'prompt\': \'text prompt\', \'image\': \'low resolution latents\', \'num_inference_steps\': 20, \'guidance_scale\': 0, \'generator\': \'torch generator\'}, \'python_environment_requirements\': [\'git+https://github.com/huggingface/diffusers.git\', \'transformers\', \'accelerate\', \'scipy\', \'safetensors\'], \'example_code\': \'from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline\\nimport torch\\npipeline = StableDiffusionPipeline.from_pretrained(CompVis/stable-diffusion-v1-4, torch_dtype=torch.float16)\\npipeline.to(cuda)\\nupscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(stabilityai/sd-x2-latent-upscaler, torch_dtype=torch.float16)\\nupscaler.to(cuda)\\nprompt = a photo of an astronaut high resolution, unreal engine, ultra realistic\\ngenerator = torch.manual_seed(33)\\nlow_res_latents = pipeline(prompt, generator=generator, output_type=latent).images\\nupscaled_image = upscaler(prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator).images[0]\\nupscaled_image.save(astronaut_1024.png)\', \'performance\': {\'dataset\': \'LAION-2B\', \'accuracy\': \'Not specified\'}, \'description\': "Stable Diffusion x2 latent upscaler is a diffusion-based upscaler model developed by Katherine Crowson in collaboration with Stability AI. It is designed to upscale Stable Diffusion\'s latent denoised image embeddings, allowing for fast text-to-image and upscaling pipelines. The model was trained on a high-resolution subset of the LAION-2B dataset and works with all Stable Diffusion checkpoints."}', metadata={})]


 An animal shelter needs an original cat image for a fundraising event poster. Generate the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Unconditional Image Generation\', \'api_name\': \'google/ddpm-cat-256\', \'api_call\': "DDPMPipeline.from_pretrained(\'google/ddpm-cat-256\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': \'!pip install diffusers\\nfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\\nmodel_id = google/ddpm-cat-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\': \'Denoising Diffusion Probabilistic Models (DDPM) is a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. It can generate high-quality images using discrete noise schedulers such as scheduling_ddpm, scheduling_ddim, and scheduling_pndm. The model is trained on the unconditional CIFAR10 dataset and 256x256 LSUN, obtaining an Inception score of 9.46 and a state-of-the-art FID score of 3.17.\'}', metadata={})]


 Develop a video content recommendation engine that can understand and generate multiple categories, such as sports, comedy, and news, based on the videos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'ImRma/Brucelee\', \'api_call\': "pipeline(\'text-to-video\', model=\'ImRma/Brucelee\')", \'api_arguments\': [\'your_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Hugging Face model for converting Persian and English text into video.\'}', metadata={})]


 Our customer is a fitness platform. We need to analyze workout videos for offering customized workout plans.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 We are a security company and we need a video classification model to analyze CCTV footage for suspicious activities.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 Design a model to classify the following image: a city park with a playground and a lake, surrounded by trees and skyscrapers.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Keras\', \'functionality\': \'Binary Classification\', \'api_name\': \'TF_Decision_Trees\', \'api_call\': \'TF_Decision_Trees(input_features, target)\', \'api_arguments\': [\'input_features\', \'target\'], \'python_environment_requirements\': [\'tensorflow >= 7.0\'], \'example_code\': \'https://github.com/tdubon/TF-GB-Forest/blob/c0cf4c7e3e29d819b996cfe4eecc1f2728115e52/TFDecisionTrees_Final.ipynb\', \'performance\': {\'dataset\': \'Census-Income Data Set\', \'accuracy\': 96.57}, \'description\': "Use TensorFlow\'s Gradient Boosted Trees model in binary classification of structured data. Build a decision forests model by specifying the input feature usage. Implement a custom Binary Target encoder as a Keras Preprocessing layer to encode the categorical features with respect to their target value co-occurrences, and then use the encoded features to build a decision forests model. The model is trained on the US Census Income Dataset containing approximately 300k instances with 41 numerical and categorical variables. The task is to determine whether a person makes over 50k a year."}', metadata={})]


 We are integrating a chatbot into our system. We want the chatbot to first detect the language of user input before providing a response.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 We have a dataset with customer reviews of our financial service app, and we'd like to analyze their sentiment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'sentiment_analysis_generic_dataset\', \'api_call\': "pipeline(\'text-classification\', model=\'Seethal/sentiment_analysis_generic_dataset\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "sentiment_analysis(\'I love this product!\')", \'performance\': {\'dataset\': \'generic_dataset\', \'accuracy\': \'Not specified\'}, \'description\': \'This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text classification.\'}', metadata={})]


 Our organization sells movies. We need to collect reviews from various platforms to understand the popularity of a movie.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Multi-class Classification\', \'api_name\': \'Alexei1/imdb\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'IMDB\', \'accuracy\': 0.487}, \'description\': \'A tabular classification model trained using AutoTrain for sentiment analysis on the IMDB dataset. The model has a CO2 emission of 0.0186 grams and an accuracy of 0.487.\'}', metadata={})]


 We want to enhance our search function by improving the ranking of search results.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-small-finetuned-sqa\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-small-finetuned-sqa\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6155}, \'description\': \'TAPAS small model fine-tuned on Sequential Question Answering (SQA). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).\'}', metadata={})]


 We are developing an AI chatbot to interact with users. We need the bot to recognize the user's emotions based on their text input.\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={})]


 Please help me extract the names of people, organizations, and locations mentioned in the given text.\n###Input: Hello, my name is John Doe, and I work at Microsoft. Tomorrow, I'll be going to a conference in San Francisco.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'joeddav/distilbert-base-uncased-go-emotions-student\', \'api_call\': "pipeline(\'text-classification\', model=\'joeddav/distilbert-base-uncased-go-emotions-student\')", \'api_arguments\': \'text\', \'python_environment_requirements\': [\'transformers\', \'torch\', \'tensorflow\'], \'example_code\': "from transformers import pipeline\\nnlp = pipeline(\'text-classification\', model=\'joeddav/distilbert-base-uncased-go-emotions-student\')\\nresult = nlp(\'I am so happy today!\')", \'performance\': {\'dataset\': \'go_emotions\'}, \'description\': \'This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data.\'}', metadata={})]


 The school wants a tool to teach foreign students Chinese grammar. They want you to develop a part-of-speech tagging system to detect the words' grammatical roles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Part-of-speech tagging\', \'api_name\': \'ckiplab/bert-base-chinese-pos\', \'api_call\': "AutoModel.from_pretrained(\'ckiplab/bert-base-chinese-pos\')", \'api_arguments\': {\'tokenizer\': "BertTokenizerFast.from_pretrained(\'bert-base-chinese\')"}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import (\\n  BertTokenizerFast,\\n  AutoModel,\\n)\\ntokenizer = BertTokenizerFast.from_pretrained(\'bert-base-chinese\')\\nmodel = AutoModel.from_pretrained(\'ckiplab/bert-base-chinese-pos\')", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).\'}', metadata={})]


 Our company collects data on the salesperson performance in different regions for each month. We want to use the most suitable API from our subscribed_huggingface.co to analyze that in specific table format and then based on provided question, answer accordingly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-small-finetuned-sqa\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-small-finetuned-sqa\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6155}, \'description\': \'TAPAS small model fine-tuned on Sequential Question Answering (SQA). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).\'}', metadata={})]


 Assist me in finding the accurate information in a table related to the Korean stock market.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'dsba-lab/koreapas-finetuned-korwikitq\', \'api_call\': "pipeline(\'table-question-answering\', model=\'dsba-lab/koreapas-finetuned-korwikitq\')", \'api_arguments\': {}, \'python_environment_requirements\': {\'transformers\': \'>=4.0.0\'}, \'example_code\': "from transformers import pipeline; table_qa = pipeline(\'table-question-answering\', model=\'dsba-lab/koreapas-finetuned-korwikitq\'); table_qa(table=table, query=\'질문\')", \'performance\': {\'dataset\': \'korwikitq\', \'accuracy\': None}, \'description\': \'A Korean Table Question Answering model finetuned on the korwikitq dataset.\'}', metadata={})]


 My company has a large data table of employees, containing their names, titles, departments, and hire dates. We need a tool that can find all employees with the title of \"Software Engineer\" hired in 2020.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-small-finetuned-sqa\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-small-finetuned-sqa\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6155}, \'description\': \'TAPAS small model fine-tuned on Sequential Question Answering (SQA). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).\'}', metadata={})]


 We received a business document in French. We need to extract some specific information from it. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'moussaKam/barthez-orangesum-abstract\', \'api_call\': "BarthezModel.from_pretrained(\'moussaKam/barthez-orangesum-abstract\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'orangeSum\', \'accuracy\': \'\'}, \'description\': \'Barthez model finetuned on orangeSum for abstract generation in French language\'}', metadata={})]


 We have a coffee shop with different types of coffee on the menu. Determine the caffeine levels in each cup of coffee.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'pachi107/autotrain-in-class-test-1780161764\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data.csv\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(data.csv)\\ndata = data[features]\\ndata.columns = [feat_ + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'Validation Metrics\', \'accuracy\': 0.974}, \'description\': \'A binary classification model for predicting CO2 emissions based on tabular data. Trained using AutoTrain with a model ID of 1780161764.\'}', metadata={})]


 You are the head of the QA department and you want to create an application that extracts answers from large product manuals. Explain how you can use this API for the project.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 I want to have a personal assistant app that can answer questions from a given text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'castorini/doc2query-t5-base-msmarco\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'castorini/doc2query-t5-base-msmarco\')", \'api_arguments\': \'text, max_length\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'MS MARCO\', \'accuracy\': \'Not specified\'}, \'description\': \'A T5 model trained on the MS MARCO dataset for generating queries from documents.\'}', metadata={})]


 I want to build a tool that helps me answer questions about specific information in a Korean newspaper article.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'dsba-lab/koreapas-finetuned-korwikitq\', \'api_call\': "pipeline(\'table-question-answering\', model=\'dsba-lab/koreapas-finetuned-korwikitq\')", \'api_arguments\': {}, \'python_environment_requirements\': {\'transformers\': \'>=4.0.0\'}, \'example_code\': "from transformers import pipeline; table_qa = pipeline(\'table-question-answering\', model=\'dsba-lab/koreapas-finetuned-korwikitq\'); table_qa(table=table, query=\'질문\')", \'performance\': {\'dataset\': \'korwikitq\', \'accuracy\': None}, \'description\': \'A Korean Table Question Answering model finetuned on the korwikitq dataset.\'}', metadata={})]


 Develop a tool to help our team members find answers to essential questions from a long document.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 There is a news article stating, \"Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU.\" We need to determine which category this article should fall under.\n###Input: Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU.\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/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'MoritzLaurer/DeBERTa-v3-xsmall-mnli-fever-anli-ling-binary\')", \'api_arguments\': {\'sequence_to_classify\': \'Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU\', \'candidate_labels\': [\'politics\', \'economy\', \'entertainment\', \'environment\'], \'multi_label\': False}, \'python_environment_requirements\': [\'transformers==4.13\'], \'example_code\': \'from transformers import pipeline\\nclassifier = pipeline(zero-shot-classification, model=MoritzLaurer/mDeBERTa-v3-base-mnli-xnli)\\nsequence_to_classify = Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU\\ncandidate_labels = [politics, economy, entertainment, environment]\\noutput = classifier(sequence_to_classify, candidate_labels, multi_label=False)\\nprint(output)\', \'performance\': {\'dataset\': [{\'name\': \'MultiNLI-matched\', \'accuracy\': 0.857}, {\'name\': \'MultiNLI-mismatched\', \'accuracy\': 0.856}, {\'name\': \'ANLI-all\', \'accuracy\': 0.537}, {\'name\': \'ANLI-r3\', \'accuracy\': 0.497}, {\'name\': \'WANLI\', \'accuracy\': 0.732}, {\'name\': \'LingNLI\', \'accuracy\': 0.788}, {\'name\': \'fever-nli\', \'accuracy\': 0.761}]}, \'description\': \'This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying mDeBERTa-v3-base model was pre-trained by Microsoft on the CC100 multilingual dataset with 100 languages. The model was then fine-tuned on the XNLI dataset and on the multilingual-NLI-26lang-2mil7 dataset. Both datasets contain more than 2.7 million hypothesis-premise pairs in 27 languages spoken by more than 4 billion people.\'}', metadata={})]


 We have generated user reviews for movies. We need to check user opinions about the movie 'Inception' based on their reviews.\n###Input: The movie 'Inception' is an exceptional piece of cinematic art. The storyline is thought-provoking and keeps the audience engaged till the end. The special effects are breathtaking and complement the plot perfectly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'lvwerra/distilbert-imdb\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'lvwerra/distilbert-imdb\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\', \'pytorch\'], \'example_code\': "classifier(\'I love this movie!\')", \'performance\': {\'dataset\': \'imdb\', \'accuracy\': 0.928}, \'description\': \'This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It is used for sentiment analysis on movie reviews and achieves an accuracy of 0.928 on the evaluation set.\'}', metadata={})]


 In our new app, we are building a feature that recommends books in different languages. To do this, first, we need to translate the book title and details from English to French. Help us to decide the best translation model to use here.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'facebook/textless_sm_en_fr\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/textless_sm_en_fr\')", \'api_arguments\': [\'input_file\'], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is a speech-to-speech translation model trained by Facebook. It is designed for translating English speech to French speech.\'}', metadata={})]


 Our multinational company is dealing with a French client. Please help us communicate in French by translating an English sentence into French.\n###Input: \"Hello, how are you?\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


 We want to communicate product information to online customers. Translate the information from English to French.\n###Input: \"Introducing the new eco-friendly water bottle made of high-quality stainless steel with double-wall insulation to keep your drinks cool for 24 hours or hot for 12 hours.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


 Our Customer is a Swedish travel magazine with an English version. Translate this Swedish text to English for them: \"Stockholm \u00e4r Sveriges huvudstad och st\u00f6rsta stad. Den har en rik historia och erbjuder m\u00e5nga kulturella och historiska sev\u00e4rdheter.\"\n###Input: \"Stockholm \u00e4r Sveriges huvudstad och st\u00f6rsta stad. Den har en rik historia och erbjuder m\u00e5nga kulturella och historiska sev\u00e4rdheter.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-es-en\', \'api_call\': "pipeline(\'translation_es_to_en\', model=\'Helsinki-NLP/opus-mt-es-en\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_es_to_en\', model=\'Helsinki-NLP/opus-mt-es-en\')(\'Hola, ¿cómo estás?\')", \'performance\': {\'dataset\': [{\'name\': \'newssyscomb2009-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 30.6, \'chr-F\': 0.57}}, {\'name\': \'news-test2008-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 27.9, \'chr-F\': 0.553}}, {\'name\': \'newstest2009-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 30.4, \'chr-F\': 0.572}}, {\'name\': \'newstest2010-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 36.1, \'chr-F\': 0.614}}, {\'name\': \'newstest2011-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 34.2, \'chr-F\': 0.599}}, {\'name\': \'newstest2012-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 37.9, \'chr-F\': 0.624}}, {\'name\': \'newstest2013-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 35.3, \'chr-F\': 0.609}}, {\'name\': \'Tatoeba-test.spa.eng\', \'accuracy\': {\'BLEU\': 59.6, \'chr-F\': 0.739}}]}, \'description\': \'Helsinki-NLP/opus-mt-es-en is a machine translation model trained to translate from Spanish to English using the Hugging Face Transformers library. The model is based on the Marian framework and was trained on the OPUS dataset.\'}', metadata={})]


 I have written a small story in English about the adventures of a superhero who saves the day from evil villains. I would like to translate it into French so that my friends in France can read it too.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Abstractive Text Summarization\', \'api_name\': \'plguillou/t5-base-fr-sum-cnndm\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'plguillou/t5-base-fr-sum-cnndm\')", \'api_arguments\': {\'input_text\': \'summarize: ARTICLE\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import T5Tokenizer, T5ForConditionalGeneration\'}, \'example_code\': \'tokenizer = T5Tokenizer.from_pretrained(plguillou/t5-base-fr-sum-cnndm)\\nmodel = T5ForConditionalGeneration.from_pretrained(plguillou/t5-base-fr-sum-cnndm)\', \'performance\': {\'dataset\': \'cnn_dailymail\', \'ROUGE-1\': 44.5252, \'ROUGE-2\': 22.652, \'ROUGE-L\': 29.8866}, \'description\': \'This model is a T5 Transformers model (JDBN/t5-base-fr-qg-fquad) that was fine-tuned in French for abstractive text summarization.\'}', metadata={})]


 Create an application that reads news articles and provides a brief summary of the article contents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'it5-base-news-summarization\', \'api_call\': "pipeline(\'summarization\', model=\'it5/it5-base-news-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'newsum(Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.)\', \'performance\': {\'dataset\': \'NewsSum-IT\', \'accuracy\': {\'Rouge1\': 0.339, \'Rouge2\': 0.16, \'RougeL\': 0.263}}, \'description\': \'IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora for Italian Language Understanding and Generation.\'}', metadata={})]


 We are building a platform for developers and want to provide automatic code documentation generation for Python functions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'Code Documentation Generation\', \'api_name\': \'code_trans_t5_base_code_documentation_generation_python\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'SEBIS/code_trans_t5_base_code_documentation_generation_python\')", \'api_arguments\': [\'tokenized_code\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline\\npipeline = SummarizationPipeline(\\n model=AutoModelWithLMHead.from_pretrained(SEBIS/code_trans_t5_base_code_documentation_generation_python),\\n tokenizer=AutoTokenizer.from_pretrained(SEBIS/code_trans_t5_base_code_documentation_generation_python, skip_special_tokens=True),\\n device=0\\n)\\ntokenized_code = def e ( message , exit_code = None ) : print_log ( message , YELLOW , BOLD ) if exit_code is not None : sys . exit ( exit_code )\\npipeline([tokenized_code])\', \'performance\': {\'dataset\': \'CodeSearchNet Corpus python dataset\', \'accuracy\': \'20.26 BLEU score\'}, \'description\': \'This CodeTrans model is based on the t5-base model and is trained on tokenized python code functions. It can be used to generate descriptions for python functions or be fine-tuned on other python code tasks. The model works best with tokenized python functions but can also be used on unparsed and untokenized python code.\'}', metadata={})]


 In order to engage our users and keep them interested in our platform, we require a conversational chatbot that discusses a wide range of topics.\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={})]


 Use this API to get a suggestion on how to respond to a customer's complaint about the late delivery of their package.\n###Input: {\"instruction\": \"How can I respond to a customer complaint about late delivery?\", \"knowledge\": \"The courier had external delays due to bad winter weather.\", \"dialog\": [\"Customer: My package is late. What's going on?\", \"Support: I apologize for the inconvenience. I'll check what's happening with the package and get back to you.\"]}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Conversational\', \'api_name\': \'Pi3141/DialoGPT-medium-elon-3\', \'api_call\': "pipeline(\'text-generation\', model=\'Pi3141/DialoGPT-medium-elon-3\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'Input a message to start chatting with Pi3141/DialoGPT-medium-elon-3.\', \'performance\': {\'dataset\': \'Twitter tweets by Elon Musk\', \'accuracy\': \'N/A\'}, \'description\': \'DialoGPT model that talks like Elon Musk, trained on Twitter tweets by Elon Musk. This model will spew meaningless shit about 40% of the time. Trained on 8 epochs. But with a larger dataset this time. The AI can now use more emojis, I think.\'}', metadata={})]


 A game studio is now creating a story for their new action game, they need a hint for creating the setting of the game.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'castorini/doc2query-t5-base-msmarco\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'castorini/doc2query-t5-base-msmarco\')", \'api_arguments\': \'text, max_length\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'MS MARCO\', \'accuracy\': \'Not specified\'}, \'description\': \'A T5 model trained on the MS MARCO dataset for generating queries from documents.\'}', metadata={})]


 We are working on an AI chatbot for customer support and we need our chatbot to generate human-like responses to customers' 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={})]


 Translate the following English sentence to German: \"I have a doctor's appointment tomorrow morning.\"\n###Input: \"I have a doctor's appointment tomorrow morning.\"\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={})]


 I am struggling with grammar while writing. Thus, I want to create a grammar correction tool for myself.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Grammar Correction\', \'api_name\': \'vennify/t5-base-grammar-correction\', \'api_call\': "HappyTextToText(\'T5\', \'vennify/t5-base-grammar-correction\')", \'api_arguments\': {\'num_beams\': 5, \'min_length\': 1}, \'python_environment_requirements\': {\'package\': \'happytransformer\', \'installation\': \'pip install happytransformer\'}, \'example_code\': \'from happytransformer import HappyTextToText, TTSettings\\nhappy_tt = HappyTextToText(T5, vennify/t5-base-grammar-correction)\\nargs = TTSettings(num_beams=5, min_length=1)\\nresult = happy_tt.generate_text(grammar: This sentences has has bads grammar., args=args)\\nprint(result.text)\', \'performance\': {\'dataset\': \'jfleg\', \'accuracy\': \'Not provided\'}, \'description\': \'This model generates a revised version of inputted text with the goal of containing fewer grammatical errors. It was trained with Happy Transformer using a dataset called JFLEG.\'}', metadata={})]


 Our company is cooperating with a German partner. We have some materials in English, but need to translate them into German.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'sshleifer/tiny-marian-en-de\', \'api_call\': "pipeline(\'translation_en_to_de\', model=\'sshleifer/tiny-marian-en-de\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A tiny English to German translation model using the Marian framework in Hugging Face Transformers.\'}', metadata={})]


 The publisher has sent us a draft of an article, but some of the words have been masked. We need to identify the masked words.\n###Input: \"<mask> are large, slow-moving reptiles native to the southeastern United States. They are well-adapted to life in <mask>, and they are a common sight in swamps, rivers, and lakes.\" \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Masked Language Modeling and Next Sentence Prediction\', \'api_name\': \'bert-large-uncased\', \'api_call\': "pipeline(\'fill-mask\', model=\'bert-large-uncased\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nunmasker = pipeline(\'fill-mask\', model=\'bert-large-uncased\')\\nunmasker(Hello I\'m a [MASK] model.)", \'performance\': {\'dataset\': {\'SQUAD 1.1 F1/EM\': \'91.0/84.3\', \'Multi NLI Accuracy\': \'86.05\'}}, \'description\': \'BERT large model (uncased) is a transformer model pretrained on a large corpus of English data using a masked language modeling (MLM) objective. It has 24 layers, 1024 hidden dimensions, 16 attention heads, and 336M parameters. The model is intended to be fine-tuned on a downstream task, such as sequence classification, token classification, or question answering.\'}', metadata={})]


 Our company is exploring the Chinese market and we need to communicate effectively with our clients. Help me create a generic Chinese response.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 We are an educational platform improving student writing skills. We need a program that can fill in the blanks in sentences.\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={})]


 I am building a plagiarism-detection tool. I need to evaluate the similarity between two sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Transformers\', \'api_name\': \'nikcheerla/nooks-amd-detection-realtime\', \'api_call\': "SentenceTransformer(\'nikcheerla/nooks-amd-detection-realtime\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer\\nsentences = [This is an example sentence, Each sentence is converted]\\nmodel = SentenceTransformer(\'{MODEL_NAME}\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Automated evaluation\'}, \'description\': \'This is a sentence-transformers model that maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 We are working on creating an audiobook. Convert this text: \"The sun was shining brightly, and the birds were singing sweetly\" into speech.\n###Input: The sun was shining brightly, and the birds were singing sweetly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'lakahaga/novel_reading_tts\', \'api_call\': "AutoModelForTTS.from_pretrained(\'lakahaga/novel_reading_tts\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "inputs = processor(text, return_tensors=\'pt\'); generated_audio = model.generate(**inputs);", \'performance\': {\'dataset\': \'novelspeech\', \'accuracy\': None}, \'description\': \'This model was trained by lakahaga using novelspeech recipe in espnet. It is designed for Korean text-to-speech tasks.\'}', metadata={})]


 A researcher needs information about how to use the Whisper ASR model to transcribe and analyze the sentiment of an audio file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transcription and Translation\', \'api_name\': \'openai/whisper-medium\', \'api_call\': "WhisperForConditionalGeneration.from_pretrained(\'openai/whisper-medium\')", \'api_arguments\': [\'sample\', \'sampling_rate\', \'language\', \'task\', \'skip_special_tokens\'], \'python_environment_requirements\': [\'transformers\', \'datasets\'], \'example_code\': \'from transformers import WhisperProcessor, WhisperForConditionalGeneration\\nfrom datasets import load_dataset\\n\\nprocessor = WhisperProcessor.from_pretrained(openai/whisper-medium)\\nmodel = WhisperForConditionalGeneration.from_pretrained(openai/whisper-medium)\\n\\nmodel.config.forced_decoder_ids = None\\n\\nds = load_dataset(hf-internal-testing/librispeech_asr_dummy, clean, split=validation)\\nsample = ds[0][audio]\\ninput_features = processor(sample[array], sampling_rate=sample[sampling_rate], return_tensors=pt).input_features\\n\\npredicted_ids = model.generate(input_features)\\ntranscription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\', \'performance\': {\'dataset\': [{\'name\': \'LibriSpeech (clean)\', \'accuracy\': 2.9}, {\'name\': \'LibriSpeech (other)\', \'accuracy\': 5.9}, {\'name\': \'Common Voice 11.0\', \'accuracy\': 53.87}]}, \'description\': \'Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. It is a Transformer-based encoder-decoder model and was trained on either English-only data or multilingual data.\'}', metadata={})]