 The marketing team needs different variations of a product image to use in advertising and promotional materials.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image Variations\', \'api_name\': \'lambdalabs/sd-image-variations-diffusers\', \'api_call\': "StableDiffusionImageVariationPipeline.from_pretrained(\'lambdalabs/sd-image-variations-diffusers\', revision=\'v2.0\')", \'api_arguments\': {\'revision\': \'v2.0\'}, \'python_environment_requirements\': \'Diffusers >=0.8.0\', \'example_code\': \'from diffusers import StableDiffusionImageVariationPipeline\\nfrom PIL import Image\\ndevice = cuda:0\\nsd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(\\n lambdalabs/sd-image-variations-diffusers,\\n revision=v2.0,\\n)\\nsd_pipe = sd_pipe.to(device)\\nim = Image.open(path/to/image.jpg)\\ntform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Resize(\\n  (224, 224),\\n  interpolation=transforms.InterpolationMode.BICUBIC,\\n  antialias=False,\\n ),\\n transforms.Normalize(\\n  [0.48145466, 0.4578275, 0.40821073],\\n  [0.26862954, 0.26130258, 0.27577711]),\\n])\\ninp = tform(im).to(device).unsqueeze(0)\\nout = sd_pipe(inp, guidance_scale=3)\\nout[images][0].save(result.jpg)\', \'performance\': {\'dataset\': \'ChristophSchuhmann/improved_aesthetics_6plus\', \'accuracy\': \'N/A\'}, \'description\': \'This version of Stable Diffusion has been fine tuned from CompVis/stable-diffusion-v1-4-original to accept CLIP image embedding rather than text embeddings. This allows the creation of image variations similar to DALLE-2 using Stable Diffusion.\'}', metadata={})]


 A movie studio needs to estimate the human pose of an actor from an image for an upcoming film project.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Human Pose Estimation\', \'api_name\': \'lllyasviel/sd-controlnet-openpose\', \'api_call\': "ControlNetModel.from_pretrained(\'lllyasviel/sd-controlnet-openpose\')", \'api_arguments\': {\'text\': \'chef in the kitchen\', \'image\': \'image\', \'num_inference_steps\': 20}, \'python_environment_requirements\': {\'diffusers\': \'pip install diffusers\', \'transformers\': \'pip install transformers\', \'accelerate\': \'pip install accelerate\', \'controlnet_aux\': \'pip install controlnet_aux\'}, \'example_code\': "from PIL import Image\\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\\nimport torch\\nfrom controlnet_aux import OpenposeDetector\\nfrom diffusers.utils import load_image\\nopenpose = OpenposeDetector.from_pretrained(\'lllyasviel/ControlNet\')\\nimage = load_image(https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png)\\nimage = openpose(image)\\ncontrolnet = ControlNetModel.from_pretrained(\\n lllyasviel/sd-controlnet-openpose, torch_dtype=torch.float16\\n)\\npipe = StableDiffusionControlNetPipeline.from_pretrained(\\n runwayml/stable-diffusion-v1-5, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\\n)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nimage = pipe(chef in the kitchen, image, num_inference_steps=20).images[0]\\nimage.save(\'images/chef_pose_out.png\')", \'performance\': {\'dataset\': \'200k pose-image, caption pairs\', \'accuracy\': \'Not specified\'}, \'description\': \'ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Human Pose Estimation. It can be used in combination with Stable Diffusion.\'}', metadata={})]


 Create a program to determine the depth map from an input image of a street filled with people.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Monocular Depth Estimation\', \'api_name\': \'Intel/dpt-large\', \'api_call\': "DPTForDepthEstimation.from_pretrained(\'Intel/dpt-large\')", \'api_arguments\': {\'pretrained_model_name_or_path\': \'Intel/dpt-large\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import DPTImageProcessor, DPTForDepthEstimation\\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)\\nprocessor = DPTImageProcessor.from_pretrained(Intel/dpt-large)\\nmodel = DPTForDepthEstimation.from_pretrained(Intel/dpt-large)\\ninputs = processor(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\': \'MIX 6\', \'accuracy\': \'10.82\'}, \'description\': \'Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation. Introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021). DPT uses the Vision Transformer (ViT) as backbone and adds a neck + head on top for monocular depth estimation.\'}', metadata={})]


 I need to create a new piece of art to add to my digital gallery that resembles WikiArt images.\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\': \'johnowhitaker/sd-class-wikiart-from-bedrooms\', \'api_call\': "DDPMPipeline.from_pretrained(\'johnowhitaker/sd-class-wikiart-from-bedrooms\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'diffusers\', \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'johnowhitaker/sd-class-wikiart-from-bedrooms\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'https://huggingface.co/datasets/huggan/wikiart\', \'accuracy\': \'Not provided\'}, \'description\': \'This model is a diffusion model initialized from https://huggingface.co/google/ddpm-bedroom-256 and trained for 5000 steps on https://huggingface.co/datasets/huggan/wikiart.\'}', metadata={})]


 Our team is designing a butterfly-themed stationery set for children. We need to generate images of cute butterflies to use in the design.\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\': \'ntrant7/sd-class-butterflies-32\', \'api_call\': "DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')", \'api_arguments\': [], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'Not specified\', \'accuracy\': \'Not specified\'}, \'description\': \'This model is a diffusion model for unconditional image generation of cute butterflies.\'}', metadata={})]


 Design a mobile application that suggests pictures of butterflies whenever the user attempts to change their theme.\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\': \'ntrant7/sd-class-butterflies-32\', \'api_call\': "DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')", \'api_arguments\': [], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'Not specified\', \'accuracy\': \'Not specified\'}, \'description\': \'This model is a diffusion model for unconditional image generation of cute butterflies.\'}', metadata={})]


 We need to generate some vintage images for a promotion poster.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'vintedois-diffusion-v0-1\', \'api_call\': "pipeline(\'text-to-image\', model=\'22h/vintedois-diffusion-v0-1\')", \'api_arguments\': [\'prompt\', \'CFG Scale\', \'Scheduler\', \'Steps\', \'Seed\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "text2img(\'photo of an old man in a jungle, looking at the camera\', CFG Scale=7.5, Scheduler=\'diffusers.EulerAncestralDiscreteScheduler\', Steps=30, Seed=44)", \'performance\': {\'dataset\': \'large amount of high quality images\', \'accuracy\': \'not specified\'}, \'description\': \'Vintedois (22h) Diffusion model trained by Predogl and piEsposito with open weights, configs and prompts. This model generates beautiful images without a lot of prompt engineering. It can also generate high fidelity faces with a little amount of steps.\'}', metadata={})]


 Generate images of cute butterflies using myunus1/diffmodels_galaxies_scratchbook model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Diffusers\', \'api_name\': \'myunus1/diffmodels_galaxies_scratchbook\', \'api_call\': "DDPMPipeline.from_pretrained(\'myunus1/diffmodels_galaxies_scratchbook\')", \'api_arguments\': {\'from_pretrained\': \'myunus1/diffmodels_galaxies_scratchbook\'}, \'python_environment_requirements\': {\'package\': \'diffusers\', \'import\': \'from diffusers import DDPMPipeline\'}, \'example_code\': {\'initialize_pipeline\': "pipeline = DDPMPipeline.from_pretrained(\'myunus1/diffmodels_galaxies_scratchbook\')", \'generate_image\': \'image = pipeline().images[0]\', \'display_image\': \'image\'}, \'performance\': {\'dataset\': \'Not provided\', \'accuracy\': \'Not provided\'}, \'description\': \'This model is a diffusion model for unconditional image generation of cute 🦋.\'}', metadata={})]


 As part of the online platform's development, we need an AI that, given input text about a video, estimates the content of the video and judges its category.\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={})]


 We need to develop a model to classify sports clips by identifying the type of sports being played in the video.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'microsoft/xclip-base-patch16-zero-shot\', \'api_call\': "XClipModel.from_pretrained(\'microsoft/xclip-base-patch16-zero-shot\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'For code examples, we refer to the documentation.\', \'performance\': {\'dataset\': [{\'name\': \'HMDB-51\', \'accuracy\': 44.6}, {\'name\': \'UCF-101\', \'accuracy\': 72.0}, {\'name\': \'Kinetics-600\', \'accuracy\': 65.2}]}, \'description\': \'X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.\'}', metadata={})]


 We are designing an application for professional athletes. They need a tool to categorize their exercises based on videos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'MCG-NJU/videomae-base-short-finetuned-kinetics\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')", \'api_arguments\': [\'video\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')\\nmodel = VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')\\ninputs = processor(video, return_tensors=\'pt\')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\'Predicted class:\', model.config.id2label[predicted_class_idx])", \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': {\'top-1\': 79.4, \'top-5\': 94.1}}, \'description\': \'VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository.\'}', metadata={})]


 We are building a video analysis tool that can automatically detect the main action happening in a given video clip.\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={})]


 I would like to create an application that identifies animals in Chinese language image captions. Specifically, we want to know if a picture includes a cat or a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'OFA-Sys/chinese-clip-vit-large-patch14-336px\', \'api_call\': "ChineseCLIPModel.from_pretrained(\'OFA-Sys/chinese-clip-vit-large-patch14-336px\')", \'api_arguments\': {\'images\': \'image\', \'text\': \'texts\', \'return_tensors\': \'pt\', \'padding\': \'True\'}, \'python_environment_requirements\': [\'PIL\', \'requests\', \'transformers\'], \'example_code\': \'from PIL import Image\\nimport requests\\nfrom transformers import ChineseCLIPProcessor, ChineseCLIPModel\\nmodel = ChineseCLIPModel.from_pretrained(OFA-Sys/chinese-clip-vit-large-patch14-336px)\\nprocessor = ChineseCLIPProcessor.from_pretrained(OFA-Sys/chinese-clip-vit-large-patch14-336px)\\nurl = https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ntexts = []\\ninputs = processor(images=image, return_tensors=pt)\\nimage_features = model.get_image_features(**inputs)\\nimage_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\\ninputs = processor(text=texts, padding=True, return_tensors=pt)\\ntext_features = model.get_text_features(**inputs)\\ntext_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\\ninputs = processor(text=texts, images=image, return_tensors=pt, padding=True)\\noutputs = model(**inputs)\\nlogits_per_image = outputs.logits_per_image\\nprobs = logits_per_image.softmax(dim=1)\', \'performance\': {\'dataset\': {\'CIFAR10\': 96.0, \'CIFAR100\': 79.75, \'DTD\': 51.2, \'EuroSAT\': 52.0, \'FER\': 55.1, \'FGVC\': 26.2, \'KITTI\': 49.9, \'MNIST\': 79.4, \'PC\': 63.5, \'VOC\': 84.9}, \'accuracy\': \'various\'}, \'description\': \'Chinese CLIP is a simple implementation of CLIP on a large-scale dataset of around 200 million Chinese image-text pairs. It uses ViT-L/14@336px as the image encoder and RoBERTa-wwm-base as the text encoder.\'}', metadata={})]


 I need a portable system to perform quick sentiment analysis of customer reviews.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'cardiffnlp/twitter-xlm-roberta-base-sentiment\', \'api_call\': "pipeline(sentiment-analysis, model=\'cardiffnlp/twitter-xlm-roberta-base-sentiment\')", \'api_arguments\': [\'model_path\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nmodel_path = cardiffnlp/twitter-xlm-roberta-base-sentiment\\nsentiment_task = pipeline(sentiment-analysis, model=model_path, tokenizer=model_path)\\nsentiment_task(T\'estimo!)", \'performance\': {\'dataset\': \'Twitter\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).\'}', metadata={})]


 A language model has identified a recent book review and provided a summary of its content. Perform sentiment analysis on the summary to evaluate the reviewer's feelings.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'cardiffnlp/twitter-xlm-roberta-base-sentiment\', \'api_call\': "pipeline(sentiment-analysis, model=\'cardiffnlp/twitter-xlm-roberta-base-sentiment\')", \'api_arguments\': [\'model_path\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nmodel_path = cardiffnlp/twitter-xlm-roberta-base-sentiment\\nsentiment_task = pipeline(sentiment-analysis, model=model_path, tokenizer=model_path)\\nsentiment_task(T\'estimo!)", \'performance\': {\'dataset\': \'Twitter\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).\'}', metadata={})]


 We have movie reviews on our website, and we need to display whether the review is positive or negative.\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={})]


 I wish to automatically classify a given text's emotion. What Transformers model should I use?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'michellejieli/emotion_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/emotion_text_classifier\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I love this!)\', \'performance\': {\'dataset\': [\'Crowdflower (2016)\', \'Emotion Dataset, Elvis et al. (2018)\', \'GoEmotions, Demszky et al. (2020)\', \'ISEAR, Vikash (2018)\', \'MELD, Poria et al. (2019)\', \'SemEval-2018, EI-reg, Mohammad et al. (2018)\', \'Emotion Lines (Friends)\'], \'accuracy\': \'Not provided\'}, \'description\': \'DistilRoBERTa-base is a transformer model that performs sentiment analysis. I fine-tuned the model on transcripts from the Friends show with the goal of classifying emotions from text data, specifically dialogue from Netflix shows or movies. The model predicts 6 Ekman emotions and a neutral class. These emotions include anger, disgust, fear, joy, neutrality, sadness, and surprise.\'}', metadata={})]


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


 We're creating a chatbot that will detect the user's emotion. I want to start by implementing the basic functionality of emotion detection from the user's responses.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Emotion Classification\', \'api_name\': \'j-hartmann/emotion-english-distilroberta-base\', \'api_call\': "pipeline(\'text-classification\', model=\'j-hartmann/emotion-english-distilroberta-base\', return_all_scores=True)", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': \'from transformers import pipeline\\nclassifier = pipeline(text-classification, model=j-hartmann/emotion-english-distilroberta-base, return_all_scores=True)\\nclassifier(I love this!)\', \'performance\': {\'dataset\': \'Balanced subset from 6 diverse datasets\', \'accuracy\': \'66%\'}, \'description\': "This model classifies emotions in English text data. It predicts Ekman\'s 6 basic emotions, plus a neutral class: anger, disgust, fear, joy, neutral, sadness, and surprise. The model is a fine-tuned checkpoint of DistilRoBERTa-base."}', metadata={})]


 I am a journalist. I am writing an article about European start-ups. I need to see all the dates and company names in the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'google/pegasus-newsroom\', \'api_call\': "pipeline(\'summarization\', model=\'google/pegasus-newsroom\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'This model can be loaded on the Inference API on-demand.\', \'performance\': {\'dataset\': \'newsroom\', \'accuracy\': \'45.98/34.20/42.18\'}, \'description\': \'PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. The model is trained on both C4 and HugeNews datasets and is designed for summarization tasks.\'}', metadata={})]


 Create a tool to extract entities from news articles to help journalists research important figures and organizations.\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={})]


 A famous writer is working on a novel. He needs your help to predict the punctuation marks needed in his written draft.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Natural Language Processing Token Classification', 'framework': 'Transformers', 'functionality': 'punctuation prediction', 'api_name': 'oliverguhr/fullstop-punctuation-multilang-large', 'api_call': 'PunctuationModel()', 'api_arguments': ['text'], 'python_environment_requirements': ['pip install deepmultilingualpunctuation'], 'example_code': 'from deepmultilingualpunctuation import PunctuationModel\\nmodel = PunctuationModel()\\ntext = My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller\\nresult = model.restore_punctuation(text)\\nprint(result)', 'performance': {'dataset': 'wmt/europarl', 'EN_accuracy': 0.775, 'DE_accuracy': 0.814, 'FR_accuracy': 0.782, 'IT_accuracy': 0.762}, 'description': 'This model predicts the punctuation of English, Italian, French and German texts. It was developed to restore the punctuation of transcribed spoken language and trained on the Europarl Dataset provided by the SEPP-NLG Shared Task. The model restores the following punctuation markers: ., ,, ?, -, :.'}", metadata={})]


 There's a collection of texts that we want to be able to analyze for their part-of-speech tags to better understand the structure of the sentences within the texts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 I am working on a project where I want to make predictions from my data that is stored in structured tables. Find a pre-trained model for table question answering.\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-mini-finetuned-wtq\', \'api_call\': "AutoModelForTableQuestionAnswering.from_pretrained(\'google/tapas-mini-finetuned-wtq\')", \'api_arguments\': "tokenizer = AutoTokenizer.from_pretrained(\'google/tapas-mini-finetuned-wtq\'); model = AutoModelForTableQuestionAnswering.from_pretrained(\'google/tapas-mini-finetuned-wtq\')", \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\n\\nnlp = pipeline(\'table-question-answering\', model=\'google/tapas-mini-finetuned-wtq\', tokenizer=\'google/tapas-mini-finetuned-wtq\')", \'performance\': {\'dataset\': \'wikitablequestions\', \'accuracy\': 0.2854}, \'description\': \'TAPAS mini model fine-tuned on WikiTable Questions (WTQ). It is pretrained on a large corpus of English data from Wikipedia and can be used for answering questions related to a table.\'}', metadata={})]


 I have a table in CSV format and a query related to it. Could you obtain an answer for my query?\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={})]


 Develop a tool that helps me get answers to questions related to a specific text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'distilbert-base-uncased-distilled-squad\', \'api_call\': "pipeline(\'question-answering\', model=\'distilbert-base-uncased-distilled-squad\')", \'api_arguments\': [\'question\', \'context\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nquestion_answerer = pipeline(question-answering, model=\'distilbert-base-uncased-distilled-squad\')\\ncontext = r\\n... Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a\\n... question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune\\n... a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.\\n... \\nresult = question_answerer(question=What is a good example of a question answering dataset?, context=context)\\nprint(\\n... fAnswer: \'{result[\'answer\']}\', score: {round(result[\'score\'], 4)}, start: {result[\'start\']}, end: {result[\'end\']}\\n...)", \'performance\': {\'dataset\': \'SQuAD v1.1\', \'accuracy\': \'86.9 F1 score\'}, \'description\': "DistilBERT base uncased distilled SQuAD is a fine-tuned version of DistilBERT-base-uncased for the task of question answering. It has 40% less parameters than bert-base-uncased, runs 60% faster while preserving over 95% of BERT\'s performances as measured on the GLUE language understanding benchmark."}', metadata={})]


 A healthcare professional wants to get quick answers to COVID-19 related questions from the latest research articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'deepset/roberta-base-squad2-covid\', \'api_call\': "pipeline(\'question-answering\', model=RobertaForQuestionAnswering.from_pretrained(\'deepset/roberta-base-squad2-covid\'), tokenizer=RobertaTokenizer.from_pretrained(\'deepset/roberta-base-squad2-covid\'))", \'api_arguments\': {\'model_name\': \'deepset/roberta-base-squad2-covid\', \'tokenizer\': \'deepset/roberta-base-squad2-covid\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': {\'QA_input\': {\'question\': \'Why is model conversion important?\', \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'}, \'res\': \'nlp(QA_input)\'}, \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': {\'XVAL_EM\': 0.17890995260663506, \'XVAL_f1\': 0.49925444207319924, \'XVAL_top_3_recall\': 0.8021327014218009}}, \'description\': \'This model is a Roberta-based model fine-tuned on SQuAD-style CORD-19 annotations for the task of extractive question answering in the context of COVID-19. It can be used with the Hugging Face Transformers library for question answering tasks.\'}', metadata={})]


 I have this app for sharing cooking recipes. Users upload photos and ask questions about the showcased recipe. I need to automatically answer their questions based on the recipe image provided.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'promptcap-coco-vqa\', \'api_call\': "PromptCap(\'vqascore/promptcap-coco-vqa\')", \'api_arguments\': {\'prompt\': \'string\', \'image\': \'string\'}, \'python_environment_requirements\': \'pip install promptcap\', \'example_code\': [\'import torch\', \'from promptcap import PromptCap\', \'model = PromptCap(vqascore/promptcap-coco-vqa)\', \'if torch.cuda.is_available():\', \'  model.cuda()\', \'prompt = please describe this image according to the given question: what piece of clothing is this boy putting on?\', \'image = glove_boy.jpeg\', \'print(model.caption(prompt, image))\'], \'performance\': {\'dataset\': {\'coco\': {\'accuracy\': \'150 CIDEr\'}, \'OK-VQA\': {\'accuracy\': \'60.4%\'}, \'A-OKVQA\': {\'accuracy\': \'59.6%\'}}}, \'description\': \'PromptCap is a captioning model that can be controlled by natural language instruction. The instruction may contain a question that the user is interested in. It achieves SOTA performance on COCO captioning (150 CIDEr) and knowledge-based VQA tasks when paired with GPT-3 (60.4% on OK-VQA and 59.6% on A-OKVQA).\'}', metadata={})]


 The company's legal team is working on a case. They need a highly accurate tool to extract answers from a large set of legal documents. Develop a tool for this purpose.\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={})]


 We have received a customer inquiry. Help us categorize the inquiry into one of the following categories: \"sales\", \"technical support\", or \"billing\".\n###Input: \"I am experiencing difficulty with the installation process of your software.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'impira/layoutlm-invoices\', \'api_call\': "pipeline(\'question-answering\', model=\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "qa_pipeline(question=\'your question\', context=\'your document context\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'not provided\'}, \'description\': \'This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


 Imagine you have a news aggregator platform, and you want it to separate news articles into categories like Politics, Sports, Technology, Business, and Entertainment. Implement the classification using this API.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


 I need to classify German news articles into categories like crime, tragedy, and theft. The classifier should be able to understand German as well.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Natural Language Processing Text Classification', 'framework': 'Hugging Face Transformers', 'functionality': 'German Sentiment Classification', 'api_name': 'oliverguhr/german-sentiment-bert', 'api_call': 'SentimentModel()', 'api_arguments': ['texts'], 'python_environment_requirements': 'pip install germansentiment', 'example_code': ['from germansentiment import SentimentModel', 'model = SentimentModel()', 'texts = [', ' Mit keinem guten Ergebniss,Das ist gar nicht mal so gut,', ' Total awesome!,nicht so schlecht wie erwartet,', ' Der Test verlief positiv.,Sie fhrt ein grnes Auto.]', 'result = model.predict_sentiment(texts)', 'print(result)'], 'performance': {'dataset': ['holidaycheck', 'scare', 'filmstarts', 'germeval', 'PotTS', 'emotions', 'sb10k', 'Leipzig Wikipedia Corpus 2016', 'all'], 'accuracy': [0.9568, 0.9418, 0.9021, 0.7536, 0.678, 0.9649, 0.7376, 0.9967, 0.9639]}, 'description': 'This model was trained for sentiment classification of German language texts. The model uses the Googles Bert architecture and was trained on 1.834 million German-language samples. The training data contains texts from various domains like Twitter, Facebook and movie, app and hotel reviews.'}", metadata={})]


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


 A business collaboration project requires staff to translate Russian documents into English.\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={})]


 In this fast world, a user spends very little time on reading news articles and requires summary of the news articles.\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 need to summarize a scientific article. The input must include all the important points discussed in the article, and the result should be a concise abstraction of the content.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Summarization\', \'api_name\': \'distilbart-cnn-12-6-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/distilbart-cnn-12-6-samsum\')", \'api_arguments\': {\'model\': \'philschmid/distilbart-cnn-12-6-samsum\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/distilbart-cnn-12-6-samsum)\\nconversation = \'\'\'Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? \\nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \\nJeff: ok.\\nJeff: and how can I get started? \\nJeff: where can I find documentation? \\nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face <br />\\n\'\'\'\\nsummarizer(conversation)", \'performance\': {\'dataset\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 41.09, \'ROUGE-2\': 20.746, \'ROUGE-L\': 31.595, \'ROUGE-LSUM\': 38.339}}, \'description\': \'This model is a DistilBART-based text summarization model trained on the SAMsum dataset. It can be used to generate summaries of conversational text.\'}', metadata={})]


 Now I need to create a summary of my chat with my friend last night.\n###Input: conversation = '''Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry, can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at the park together\nHannah: I don't know him well\nAmanda: Don't be shy, he's very nice\nHannah: If you say so..\nHannah: I'd rather you texted him\nAmanda: Just text him \ud83d\ude42\nHannah: Urgh.. Alright\nHannah: Bye\nAmanda: Bye bye\n'''\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'lidiya/bart-large-xsum-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'lidiya/bart-large-xsum-samsum\')", \'api_arguments\': \'conversation\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=lidiya/bart-large-xsum-samsum)\\nconversation = \'\'\'Hannah: Hey, do you have Betty\'s number?\\nAmanda: Lemme check\\nAmanda: Sorry, can\'t find it.\\nAmanda: Ask Larry\\nAmanda: He called her last time we were at the park together\\nHannah: I don\'t know him well\\nAmanda: Don\'t be shy, he\'s very nice\\nHannah: If you say so..\\nHannah: I\'d rather you texted him\\nAmanda: Just text him 🙂\\nHannah: Urgh.. Alright\\nHannah: Bye\\nAmanda: Bye bye <br />\\n\'\'\'\\nsummarizer(conversation)", \'performance\': {\'dataset\': \'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization\', \'accuracy\': {\'rouge1\': 53.306, \'rouge2\': 28.355, \'rougeL\': 44.095}}, \'description\': \'This model was obtained by fine-tuning facebook/bart-large-xsum on Samsum dataset.\'}', metadata={})]


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


 Let's build a Chatbot that can solve daily life problems of a research Assistant.\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 need to integrate the personal assistant we're building with the capability of having conversations with people, sometimes answering general knowledge 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={})]


 I want to create a chatbot for discussing gardening topics and tips, which can refer to external information while generating responses.\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're trying to help out a friend who's developing an application for composing text. He is trying to create a bot that comes up with creative ideas for your paragraph.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 We are creating an AI newsletter application that generates summaries of news articles. We need the AI to generate a brief summary for a given article.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'mrm8488/t5-base-finetuned-summarize-news\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'mrm8488/t5-base-finetuned-summarize-news\')", \'api_arguments\': [\'text\', \'max_length\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoModelWithLMHead, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained(mrm8488/t5-base-finetuned-summarize-news)\\nmodel = AutoModelWithLMHead.from_pretrained(mrm8488/t5-base-finetuned-summarize-news)\\ndef summarize(text, max_length=150):\\n input_ids = tokenizer.encode(text, return_tensors=pt, add_special_tokens=True)\\ngenerated_ids = model.generate(input_ids=input_ids, num_beams=2, max_length=max_length, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True)\\npreds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\\nreturn preds[0]\', \'performance\': {\'dataset\': \'News Summary\', \'accuracy\': \'Not provided\'}, \'description\': "Google\'s T5 base fine-tuned on News Summary dataset for summarization downstream task. The dataset consists of 4515 examples and contains Author_name, Headlines, Url of Article, Short text, Complete Article. Time period ranges from February to August 2017."}', metadata={})]


 I want a code completion tool to assist with finishing my incomplete Python code.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


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


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


 I have some paragraphs that I want to use as a source for generating questions, based on the information present in them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'t5_sentence_paraphraser\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'ramsrigouthamg/t5_sentence_paraphraser\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A T5 model for paraphrasing sentences\'}', metadata={})]


 I am a novel writer. I plan to write some paragraphs, but I cannot find the exact word placeholder or missing word. Can you help me make a tool to complete the given text below?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


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


 I am building a recommendation engine to group news articles. I need a way to determine 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\': \'sentence-transformers/distiluse-base-multilingual-cased-v2\', \'api_call\': "SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v2\')", \'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(\'sentence-transformers/distiluse-base-multilingual-cased-v2\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 The company needs a tool to analyze customers' reviews about their products. We need to find out which ones are positive, neutral, or negative.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'bert-base-multilingual-uncased-sentiment\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'nlptown/bert-base-multilingual-uncased-sentiment\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "result = sentiment_pipeline(\'I love this product!\')", \'performance\': {\'dataset\': [{\'language\': \'English\', \'accuracy\': {\'exact\': \'67%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Dutch\', \'accuracy\': {\'exact\': \'57%\', \'off-by-1\': \'93%\'}}, {\'language\': \'German\', \'accuracy\': {\'exact\': \'61%\', \'off-by-1\': \'94%\'}}, {\'language\': \'French\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'94%\'}}, {\'language\': \'Italian\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Spanish\', \'accuracy\': {\'exact\': \'58%\', \'off-by-1\': \'95%\'}}]}, \'description\': \'This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).\'}', metadata={})]


 I am working on a project that ranks answers to questions based on their relevance. Can you help me find the most relevant answer to a specific question by using sentence similarity?\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\': \'sentence-transformers/multi-qa-mpnet-base-dot-v1\', \'api_call\': "SentenceTransformer(\'sentence-transformers/multi-qa-mpnet-base-dot-v1\')", \'api_arguments\': [\'query\', \'docs\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer, util\\nquery = How many people live in London?\\ndocs = [Around 9 Million people live in London, London is known for its financial district]\\nmodel = SentenceTransformer(\'sentence-transformers/multi-qa-mpnet-base-dot-v1\')\\nquery_emb = model.encode(query)\\ndoc_emb = model.encode(docs)\\nscores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()\\ndoc_score_pairs = list(zip(docs, scores))\\ndoc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\\nfor doc, score in doc_score_pairs:\\n print(score, doc)", \'performance\': {\'dataset\': [{\'name\': \'WikiAnswers\', \'accuracy\': 77427422}, {\'name\': \'PAQ\', \'accuracy\': 64371441}, {\'name\': \'Stack Exchange\', \'accuracy\': 25316456}]}, \'description\': \'This is a sentence-transformers model that maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources.\'}', metadata={})]


 Assist a client producing an audiobook in Japanese. They need a solution to convert their text script into spoken Japanese.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804\', \'api_call\': "AutoModelForCausalLM.from_pretrained(\'espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Japanese text-to-speech model trained using the ESPnet framework. It is designed to convert text input into natural-sounding speech.\'}', metadata={})]


 We have a product for the German-speaking audience, and we need to generate some sample voiceovers for the promotional videos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'speechbrain\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'padmalcom/tts-tacotron2-german\', \'api_call\': "Tacotron2.from_hparams(source=\'padmalcom/tts-tacotron2-german\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'pip install speechbrain\'], \'example_code\': [\'import torchaudio\', \'from speechbrain.pretrained import Tacotron2\', \'from speechbrain.pretrained import HIFIGAN\', \'tacotron2 = Tacotron2.from_hparams(source=padmalcom/tts-tacotron2-german, savedir=tmpdir_tts)\', \'hifi_gan = HIFIGAN.from_hparams(source=speechbrain/tts-hifigan-ljspeech, savedir=tmpdir_vocoder)\', \'mel_output, mel_length, alignment = tacotron2.encode_text(Die Sonne schien den ganzen Tag.)\', \'waveforms = hifi_gan.decode_batch(mel_output)\', "torchaudio.save(\'example_TTS.wav\',waveforms.squeeze(1), 22050)"], \'performance\': {\'dataset\': \'custom german dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'Text-to-Speech (TTS) with Tacotron2 trained on a custom german dataset with 12 days voice using speechbrain. Trained for 39 epochs (english speechbrain models are trained for 750 epochs) so there is room for improvement and the model is most likely to be updated soon. The hifigan vocoder can fortunately be used language-independently.\'}', metadata={})]


 I work as a transcriptionist, and I have an hour-long recording of a meeting. I need to identify and segment the speech of various speakers in the recording.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'pyannote.audio\', \'functionality\': \'Speaker diarization\', \'api_name\': \'johnislarry/cloned-pyannote-speaker-diarization-endpoint\', \'api_call\': "Pipeline.from_pretrained(\'pyannote/speaker-diarization@2.1\',use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'num_speakers\', \'min_speakers\', \'max_speakers\', \'segmentation_onset\'], \'python_environment_requirements\': \'pyannote.audio 2.0\', \'example_code\': {\'load_pipeline\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/speaker-diarization@2022.07)\', \'apply_pipeline\': \'diarization = pipeline(audio.wav)\', \'save_output\': \'with open(audio.rttm, w) as rttm:\\n  diarization.write_rttm(rttm)\'}, \'performance\': {\'dataset\': [{\'name\': \'AISHELL-4\', \'accuracy\': {\'DER%\': 14.61, \'FA%\': 3.31, \'Miss%\': 4.35, \'Conf%\': 6.95}}, {\'name\': \'AMI Mix-Headset only_words\', \'accuracy\': {\'DER%\': 18.21, \'FA%\': 3.28, \'Miss%\': 11.07, \'Conf%\': 3.87}}, {\'name\': \'AMI Array1-01 only_words\', \'accuracy\': {\'DER%\': 29.0, \'FA%\': 2.71, \'Miss%\': 21.61, \'Conf%\': 4.68}}, {\'name\': \'CALLHOME Part2\', \'accuracy\': {\'DER%\': 30.24, \'FA%\': 3.71, \'Miss%\': 16.86, \'Conf%\': 9.66}}, {\'name\': \'DIHARD 3 Full\', \'accuracy\': {\'DER%\': 20.99, \'FA%\': 4.25, \'Miss%\': 10.74, \'Conf%\': 6.0}}, {\'name\': \'REPERE Phase 2\', \'accuracy\': {\'DER%\': 12.62, \'FA%\': 1.55, \'Miss%\': 3.3, \'Conf%\': 7.76}}, {\'name\': \'VoxConverse v0.0.2\', \'accuracy\': {\'DER%\': 12.76, \'FA%\': 3.45, \'Miss%\': 3.85, \'Conf%\': 5.46}}]}, \'description\': \'This API provides speaker diarization functionality using the pyannote.audio framework. It is capable of processing audio files and outputting speaker diarization results in RTTM format. The API supports providing the number of speakers, minimum and maximum number of speakers, and adjusting the segmentation onset threshold.\'}', metadata={})]


 We are a company focusing on transcription services for Arabic speakers. We need to convert their speech to text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'Fairseq\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'tts_transformer-ar-cv7\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/tts_transformer-ar-cv7\')", \'api_arguments\': {\'text\': \'input text\'}, \'python_environment_requirements\': [\'fairseq\', \'IPython\'], \'example_code\': \'from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub(\\n facebook/tts_transformer-ar-cv7,\\n arg_overrides={vocoder: hifigan, fp16: False}\\n)\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = مرحبًا ، هذا اختبار تشغيل.\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\', \'performance\': {\'dataset\': \'common_voice\', \'accuracy\': \'Not specified\'}, \'description\': \'Transformer text-to-speech model for Arabic language with a single-speaker male voice, trained on Common Voice v7 dataset.\'}', metadata={})]


 An audio file is recorded in a conference and we need the text version of the conversation for record-keeping purposes.\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_ro_en\', \'api_call\': "pipeline(\'audio-to-audio\', model=\'facebook/textless_sm_ro_en\')", \'api_arguments\': \'audio file or recording\', \'python_environment_requirements\': \'fairseq, huggingface_hub\', \'example_code\': \'https://huggingface.co/facebook/textless_sm_cs_en\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': \'unknown\'}, \'description\': \'A speech-to-speech translation model for Romanian to English developed by Facebook AI\'}', metadata={})]


 Provide a way to translate spoken English to spoken Hokkien for an audio file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'Fairseq\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'unit_hifigan_HK_layer12.km2500_frame_TAT-TTS\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TT\')", \'api_arguments\': {\'unit\': \'Text input for the TTS model\'}, \'python_environment_requirements\': [\'fairseq\', \'huggingface_hub\', \'torchaudio\'], \'example_code\': "import json\\nimport os\\nfrom pathlib import Path\\nimport IPython.display as ipd\\nfrom fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nfrom fairseq.models.text_to_speech import CodeHiFiGANVocoder\\nfrom fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\\nfrom huggingface_hub import snapshot_download\\nimport torchaudio\\ncache_dir = os.getenv(HUGGINGFACE_HUB_CACHE)\\nlibrary_name = fairseq\\ncache_dir = (\\n cache_dir or (Path.home() / .cache / library_name).as_posix()\\n)\\ncache_dir = snapshot_download(\\n ffacebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS, cache_dir=cache_dir, library_name=library_name\\n)\\nx = hub_utils.from_pretrained(\\n cache_dir,\\n model.pt,\\n .,\\n archive_map=CodeHiFiGANVocoder.hub_models(),\\n config_yaml=config.json,\\n fp16=False,\\n is_vocoder=True,\\n)\\nwith open(f{x[\'args\'][\'data\']}/config.json) as f:\\n vocoder_cfg = json.load(f)\\nassert (\\n len(x[args][model_path]) == 1\\n), Too many vocoder models in the input\\nvocoder = CodeHiFiGANVocoder(x[args][model_path][0], vocoder_cfg)\\ntts_model = VocoderHubInterface(vocoder_cfg, vocoder)\\ntts_sample = tts_model.get_model_input(unit)\\nwav, sr = tts_model.get_prediction(tts_sample)\\nipd.Audio(wav, rate=sr)", \'performance\': {\'dataset\': \'TAT-TTS\', \'accuracy\': \'Not provided\'}, \'description\': \'Hokkien unit HiFiGAN based vocoder from fairseq. Trained with TAT-TTS data with 4 speakers in Taiwanese Hokkien accent.\'}', metadata={})]


 We want to separate the speaker sources from the original audio file to filter the noise.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'SpeechBrain\', \'functionality\': \'Audio Source Separation\', \'api_name\': \'sepformer-wsj02mix\', \'api_call\': "separator.from_hparams(source=\'speechbrain/sepformer-wsj02mix\')", \'api_arguments\': [\'path\'], \'python_environment_requirements\': [\'pip install speechbrain\'], \'example_code\': [\'from speechbrain.pretrained import SepformerSeparation as separator\', \'import torchaudio\', "model = separator.from_hparams(source=speechbrain/sepformer-wsj02mix, savedir=\'pretrained_models/sepformer-wsj02mix\')", "est_sources = model.separate_file(path=\'speechbrain/sepformer-wsj02mix/test_mixture.wav\')", \'torchaudio.save(source1hat.wav, est_sources[:, :, 0].detach().cpu(), 8000)\', \'torchaudio.save(source2hat.wav, est_sources[:, :, 1].detach().cpu(), 8000)\'], \'performance\': {\'dataset\': \'WSJ0-2Mix\', \'accuracy\': \'22.4 dB\'}, \'description\': \'This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset.\'}', metadata={})]


 Our goal is to analyze the emotions expressed in a user's recorded message.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'padmalcom/wav2vec2-large-emotion-detection-german\', \'api_call\': "pipeline(\'audio-classification\', model=\'padmalcom/wav2vec2-large-emotion-detection-german\')", \'api_arguments\': \'audio_file\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': "from transformers import pipeline\\naudio_classifier = pipeline(\'audio-classification\', model=\'padmalcom/wav2vec2-large-emotion-detection-german\')\\nresult = audio_classifier(audio_file)", \'performance\': {\'dataset\': \'emo-DB\', \'accuracy\': \'Not provided\'}, \'description\': \'This wav2vec2 based emotion detection model is trained on the emo-DB dataset. It can classify emotions in German audio files into seven classes: anger, boredom, disgust, fear, happiness, sadness, and neutral.\'}', metadata={})]


 I want to make a conference call app which differentiates between the times when the user is speaking and when there is no voice activity. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face\', \'functionality\': \'Voice Activity Detection\', \'api_name\': \'d4data/Indian-voice-cloning\', \'api_call\': "pipeline(\'voice-activity-detection\', model=\'d4data/Indian-voice-cloning\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A model for detecting voice activity in Indian languages.\'}', metadata={})]


 We want to analyze a conference call recording to identify the speakers and the segments of the conversation they participated in.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'pyannote.audio\', \'functionality\': \'Speaker diarization\', \'api_name\': \'johnislarry/cloned-pyannote-speaker-diarization-endpoint\', \'api_call\': "Pipeline.from_pretrained(\'pyannote/speaker-diarization@2.1\',use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'num_speakers\', \'min_speakers\', \'max_speakers\', \'segmentation_onset\'], \'python_environment_requirements\': \'pyannote.audio 2.0\', \'example_code\': {\'load_pipeline\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/speaker-diarization@2022.07)\', \'apply_pipeline\': \'diarization = pipeline(audio.wav)\', \'save_output\': \'with open(audio.rttm, w) as rttm:\\n  diarization.write_rttm(rttm)\'}, \'performance\': {\'dataset\': [{\'name\': \'AISHELL-4\', \'accuracy\': {\'DER%\': 14.61, \'FA%\': 3.31, \'Miss%\': 4.35, \'Conf%\': 6.95}}, {\'name\': \'AMI Mix-Headset only_words\', \'accuracy\': {\'DER%\': 18.21, \'FA%\': 3.28, \'Miss%\': 11.07, \'Conf%\': 3.87}}, {\'name\': \'AMI Array1-01 only_words\', \'accuracy\': {\'DER%\': 29.0, \'FA%\': 2.71, \'Miss%\': 21.61, \'Conf%\': 4.68}}, {\'name\': \'CALLHOME Part2\', \'accuracy\': {\'DER%\': 30.24, \'FA%\': 3.71, \'Miss%\': 16.86, \'Conf%\': 9.66}}, {\'name\': \'DIHARD 3 Full\', \'accuracy\': {\'DER%\': 20.99, \'FA%\': 4.25, \'Miss%\': 10.74, \'Conf%\': 6.0}}, {\'name\': \'REPERE Phase 2\', \'accuracy\': {\'DER%\': 12.62, \'FA%\': 1.55, \'Miss%\': 3.3, \'Conf%\': 7.76}}, {\'name\': \'VoxConverse v0.0.2\', \'accuracy\': {\'DER%\': 12.76, \'FA%\': 3.45, \'Miss%\': 3.85, \'Conf%\': 5.46}}]}, \'description\': \'This API provides speaker diarization functionality using the pyannote.audio framework. It is capable of processing audio files and outputting speaker diarization results in RTTM format. The API supports providing the number of speakers, minimum and maximum number of speakers, and adjusting the segmentation onset threshold.\'}', metadata={})]


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


 I have a dataset with CO2 emissions in a CSV file, and I want to classify which sources have high or low emissions.\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={})]


 I am building a recommendation engine to recommend linkbuilding strategy to clients. I have data in csv files that needs to be classified.\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={})]


 Create a model to estimate the carbon emissions of a specific device by using features like idle power, standby power, and active power.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'45473113800\', \'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\': \'samvelkoch/autotrain-data-prknsn-2\', \'accuracy\': {\'Loss\': 5.079, \'R2\': 0.109, \'MSE\': 25.795, \'MAE\': 3.78, \'RMSLE\': 0.849}}, \'description\': \'A tabular regression model trained with AutoTrain for predicting carbon emissions.\'}', metadata={})]


 We have a virtual reality investment in soccer simulations. We need an intelligent learning-based soccer player to make it more engaging and challenging.\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={})]


 I want to build an AI that identifies the best marketing strategies for my website by trying different combinations of headlines and images.\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 team is working on a video game where the player needs to land the spaceship on the lunar surface without crashing. They want to implement an AI module that can play the game and test it.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'LunarLander-v2\', \'api_name\': \'araffin/dqn-LunarLander-v2\', \'api_call\': "DQN.load(load_from_hub(\'araffin/dqn-LunarLander-v2\', \'dqn-LunarLander-v2.zip\'), **kwargs)", \'api_arguments\': {\'checkpoint\': \'araffin/dqn-LunarLander-v2\', \'kwargs\': {\'target_update_interval\': 30}}, \'python_environment_requirements\': [\'huggingface_sb3\', \'stable_baselines3\'], \'example_code\': {\'load_model\': \'from huggingface_sb3 import load_from_hub\\nfrom stable_baselines3 import DQN\\nfrom stable_baselines3.common.env_util import make_vec_env\\nfrom stable_baselines3.common.evaluation import evaluate_policy\\n\\ncheckpoint = load_from_hub(araffin/dqn-LunarLander-v2, dqn-LunarLander-v2.zip)\\n\\nkwargs = dict(target_update_interval=30)\\n\\nmodel = DQN.load(checkpoint, **kwargs)\\nenv = make_vec_env(LunarLander-v2, n_envs=1)\', \'evaluate\': \'mean_reward, std_reward = evaluate_policy(\\n model,\\n env,\\n n_eval_episodes=20,\\n deterministic=True,\\n)\\nprint(fMean reward = {mean_reward:.2f} +/- {std_reward:.2f})\'}, \'performance\': {\'dataset\': \'LunarLander-v2\', \'accuracy\': \'280.22 +/- 13.03\'}, \'description\': \'This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.\'}', metadata={})]


 Develop an AI character that can play the SoccerTwos game with advanced strategies.\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={})]