 Design a feature for a social media website to recommend articles to users based on how similar the articles are to their previously liked articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Summarization\', \'api_name\': \'facebook/bart-large-cnn\', \'api_call\': "pipeline(\'summarization\', model=\'facebook/bart-large-cnn\')", \'api_arguments\': [\'ARTICLE\', \'max_length\', \'min_length\', \'do_sample\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import pipeline\\nsummarizer = pipeline(summarization, model=facebook/bart-large-cnn)\\nARTICLE = ...\\nprint(summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False))\', \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 42.949, \'ROUGE-2\': 20.815, \'ROUGE-L\': 30.619, \'ROUGE-LSUM\': 40.038}}, \'description\': \'BART (large-sized model), fine-tuned on CNN Daily Mail. BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). This particular checkpoint has been fine-tuned on CNN Daily Mail, a large collection of text-summary pairs.\'}', metadata={})]


 The user is interested in a tool to find relationships between medical terms.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'d4data/biomedical-ner-all\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'d4data/biomedical-ner-all\')", \'api_arguments\': {\'model\': \'AutoModelForTokenClassification.from_pretrained(d4data/biomedical-ner-all)\', \'tokenizer\': \'AutoTokenizer.from_pretrained(d4data/biomedical-ner-all)\', \'aggregation_strategy\': \'simple\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification\'}, \'example_code\': \'pipe(The patient reported no recurrence of palpitations at follow-up 6 months after the ablation.)\', \'performance\': {\'dataset\': \'Maccrobat\', \'accuracy\': \'Not provided\'}, \'description\': \'An English Named Entity Recognition model, trained on Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.). This model was built on top of distilbert-base-uncased.\'}', metadata={})]


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


 A chat service needs a way to compare and cluster similar sentences from users in different languages. Find a suitable feature extraction method to achieve this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'sentence-transformers/distilbert-base-nli-mean-tokens\', \'api_call\': "SentenceTransformer(\'sentence-transformers/distilbert-base-nli-mean-tokens\')", \'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/distilbert-base-nli-mean-tokens\')\\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 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


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


 We need a product description for an image-based online store platform that will help customers understand the specifics of the product.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Linaqruf/anything-v3.0\', \'api_call\': "Text2ImagePipeline(model=\'Linaqruf/anything-v3.0\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A text-to-image model that generates images from text descriptions.\'}', metadata={})]


 Create a program to generate a description for an image provided as input.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'prompthero/openjourney-v4\', \'api_call\': "pipeline(\'text-to-image\', model=\'prompthero/openjourney-v4\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "generate_image(\'your text here\')", \'performance\': {\'dataset\': \'Midjourney v4 images\', \'accuracy\': \'Not provided\'}, \'description\': \'Openjourney v4 is trained on +124k Midjourney v4 images by PromptHero. It is used for generating images based on text inputs.\'}', metadata={})]


 I am a financial analyst, and I receive report after report filled with charts helping to explain trends and data in my field. However, I also need to have this information in tabular format. Please help me extract a linearized table from this chart.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'tabular regression\', \'api_name\': \'farouk97/autotrain-test7-2644pc-linearregr-38619101723\', \'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\': \'farouk97/autotrain-data-test7-2644pc-linearregr\', \'accuracy\': {\'Loss\': 0.145, \'R2\': 0.0, \'MSE\': 0.021, \'MAE\': 0.099, \'RMSLE\': 0.101}}, \'description\': \'A tabular regression model trained using AutoTrain to predict CO2 emissions (in grams).\'}', metadata={})]


 We are building an automatic video generation platform based on user-provided text. We need a reliable model to convert text instructions into appropriate videos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'camenduru/text2-video-zero\', \'api_call\': "pipeline(\'text-to-video\', model=\'camenduru/text2-video-zero\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is used for generating videos from text inputs. It is based on the Hugging Face framework and can be used with the transformers library. The model is trained on a variety of text and video datasets, and can be used for tasks such as video summarization, video generation from text prompts, and more.\'}', metadata={})]


 How can I extract video content from a text file? Provide a code sample to generate the video based on the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 We are developing a mobile app to demonstrate the AI's ability to generate a short video from text. The app focuses on processing written stories into video.\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={})]


 Hey, I want to analyze images in my phone gallery and answer questions about them.\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={})]


 My company wants to develop an application that will analyze images in relation to food and answer questions about them. We want it to handle questions like \"what is in the dish\" and \"how many calories does it have\".\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 an invoice document, and would like to extract the total amount from it.\n###Input: {'question': 'What is the total amount?', 'context': 'Invoice information for order ABC_123\\nProduct: Widget A, Quantity: 10, Price: $5 each\\nProduct: Widget B, Quantity: 5, Price: $3 each\\nProduct: Widget C, Quantity: 15, Price: $2 each\\nSubtotal: $75, Tax: $6.38, Total Amount Due: $81.38'}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'layoutlm-invoices\', \'api_call\': "AutoModelForDocumentQuestionAnswering.from_pretrained(\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "nlp(question=\'What is the total amount?\', context=\'your_invoice_text\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'Not provided\'}, \'description\': \'A fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens, this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


 As a clerk in a school, you want to extract information from some student enrollment forms. These forms contain students' details such as Name, age, and address.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'joeddav/distilbert-base-uncased-go-emotions-student\', \'api_call\': "pipeline(\'text-classification\', model=\'joeddav/distilbert-base-uncased-go-emotions-student\')", \'api_arguments\': \'text\', \'python_environment_requirements\': [\'transformers\', \'torch\', \'tensorflow\'], \'example_code\': "from transformers import pipeline\\nnlp = pipeline(\'text-classification\', model=\'joeddav/distilbert-base-uncased-go-emotions-student\')\\nresult = nlp(\'I am so happy today!\')", \'performance\': {\'dataset\': \'go_emotions\'}, \'description\': \'This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data.\'}', metadata={})]


 Find a model that can be used to predict the properties of molecules based on their graph representations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'kochetkovIT/autotrain-ironhack-49741119788\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'kochetkovIT/autotrain-data-ironhack\', \'accuracy\': {\'Loss\': 2.603, \'R2\': 0.013, \'MSE\': 6.776, \'MAE\': 1.666, \'RMSLE\': 0.502}}, \'description\': \'A tabular regression model trained using AutoTrain to predict carbon emissions (in grams) with an R2 score of 0.013.\'}', metadata={})]


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


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


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


 Assist me in setting up an image classifier that can recognize objects within an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'patrickjohncyh/fashion-clip\', \'api_call\': "CLIPModel.from_pretrained(\'patrickjohncyh/fashion-clip\')", \'api_arguments\': {\'image\': \'File\', \'class_names\': \'String (comma-separated)\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import CLIPProcessor, CLIPModel; model = CLIPModel.from_pretrained(\'patrickjohncyh/fashion-clip\'); processor = CLIPProcessor.from_pretrained(\'patrickjohncyh/fashion-clip\'); inputs = processor(text=\'blue shoes\', images=image, return_tensors=\'pt\', padding=True); logits_per_image = model(**inputs).logits_per_image; probs = logits_per_image.softmax(dim=-1).tolist()[0]", \'performance\': {\'dataset\': [{\'name\': \'FMNIST\', \'accuracy\': 0.83}, {\'name\': \'KAGL\', \'accuracy\': 0.73}, {\'name\': \'DEEP\', \'accuracy\': 0.62}]}, \'description\': \'FashionCLIP is a CLIP-based model developed to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by OpenAI, it is trained on a large, high-quality novel fashion dataset to study whether domain specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks.\'}', metadata={})]


 Identify an object within an image based on textual description. For example, find a dog in the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'zero-shot-object-detection\', \'api_name\': \'google/owlvit-base-patch16\', \'api_call\': "OwlViTForObjectDetection.from_pretrained(\'google/owlvit-base-patch16\')", \'api_arguments\': [\'texts\', \'images\'], \'python_environment_requirements\': [\'requests\', \'PIL\', \'torch\', \'transformers\'], \'example_code\': \'processor = OwlViTProcessor.from_pretrained(google/owlvit-base-patch16)\\nmodel = OwlViTForObjectDetection.from_pretrained(google/owlvit-base-patch16)\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ntexts = [[a photo of a cat, a photo of a dog]]\\ninputs = processor(text=texts, images=image, return_tensors=pt)\\noutputs = model(**inputs)\\ntarget_sizes = torch.Tensor([image.size[::-1]])\\nresults = processor.post_process(outputs=outputs, target_sizes=target_sizes)\', \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not provided\'}, \'description\': \'OWL-ViT is a zero-shot text-conditioned object detection model that can be used to query an image with one or multiple text queries. OWL-ViT uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features.\'}', metadata={})]


 Our client is an AI gaming company and we need to develop a bot for the game Valorant. The bot should detect objects like dropped spike, enemy, planted spike, and teammate within the game.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-valorant-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-valorant-detection\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8m-valorant-detection\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'valorant-object-detection\', \'accuracy\': 0.965}, \'description\': \'A YOLOv8 model for object detection in Valorant game, trained on a custom dataset. It detects dropped spike, enemy, planted spike, and teammate objects.\'}', metadata={})]


 A client from real estate agency needs to get a list of objects present in a series of pictures to prepare their property listings.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'dreamlike-art/dreamlike-photoreal-2.0\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'dreamlike-art/dreamlike-photoreal-2.0\', torch_dtype=torch.float16)(prompt).images[0]", \'api_arguments\': {\'prompt\': \'photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens\'}, \'python_environment_requirements\': {\'torch\': \'torch.float16\', \'diffusers\': \'StableDiffusionPipeline\'}, \'example_code\': \'from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = dreamlike-art/dreamlike-photoreal-2.0\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens\\nimage = pipe(prompt).images[0]\\nimage.save(./result.jpg)\', \'performance\': {\'dataset\': \'Stable Diffusion 1.5\', \'accuracy\': \'Not specified\'}, \'description\': \'Dreamlike Photoreal 2.0 is a photorealistic model based on Stable Diffusion 1.5, made by dreamlike.art. It can be used to generate photorealistic images from text prompts.\'}', metadata={})]


 We are developing an application for smartphones which automatically separates elements in a user's photo, and we need to implement this feature.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'kha-white/manga-ocr-base\', \'api_call\': "pipeline(\'ocr\', model=\'kha-white/manga-ocr-base\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'manga109s\', \'accuracy\': \'\'}, \'description\': \'Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses Vision Encoder Decoder framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: both vertical and horizontal text, text with furigana, text overlaid on images, wide variety of fonts and font styles, and low quality images.\'}', metadata={})]


 I have a picture of a room demonstrating a mixture of objects. The model needs to seperate the objects and label them accordingly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Multi-class Classification\', \'api_name\': \'Alexei1/imdb\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'IMDB\', \'accuracy\': 0.487}, \'description\': \'A tabular classification model trained using AutoTrain for sentiment analysis on the IMDB dataset. The model has a CO2 emission of 0.0186 grams and an accuracy of 0.487.\'}', metadata={})]


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


 Generate a new image based on the online database of bedroom art.\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={})]


 I run an online store that sells butterfly-themed products. Please generate an image of a cute butterfly for our social media page.\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 a video-based AI model for security purposes. We want the AI to check and categorize footage based on existing security guidelines.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'videomae-base-finetuned-RealLifeViolenceSituations-subset\', \'api_call\': "AutoModelForVideoClassification.from_pretrained(\'dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset\')", \'api_arguments\': {\'model_name\': \'dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset\'}, \'python_environment_requirements\': {\'transformers\': \'4.27.2\', \'pytorch\': \'1.13.1\', \'datasets\': \'2.10.1\', \'tokenizers\': \'0.13.2\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': 0.9533}, \'description\': \'This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It is trained for video classification task, specifically for RealLifeViolenceSituations.\'}', metadata={})]


 A new project demands to classify videos for a social media platform. Let us create a video classification pipeline.\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 am an insurance adjustor. I need a zero-shot image classifier that will tell me whether a car has been involved in a major accident or had minor damages.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'openai/clip-vit-large-patch14-336\', \'api_call\': "CLIPModel.from_pretrained(\'openai/clip-vit-large-patch14\').", \'api_arguments\': \'image_path, tokenizer, model\', \'python_environment_requirements\': \'Transformers 4.21.3, TensorFlow 2.8.2, Tokenizers 0.12.1\', \'example_code\': \'N/A\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': \'N/A\'}, \'description\': \'This model was trained from scratch on an unknown dataset.\'}', metadata={})]


 I want to analyze a medical image to find out if it's an X-ray, an MRI scan, or a CT scan.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'kha-white/manga-ocr-base\', \'api_call\': "pipeline(\'ocr\', model=\'kha-white/manga-ocr-base\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'manga109s\', \'accuracy\': \'\'}, \'description\': \'Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses Vision Encoder Decoder framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: both vertical and horizontal text, text with furigana, text overlaid on images, wide variety of fonts and font styles, and low quality images.\'}', metadata={})]


 We are building a quiz application where the image will be shown, and we have to choose a dressings matching that image. Please help in classifying the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'clip-vit-base-patch32-ko\', \'api_call\': "pipeline(\'zero-shot-image-classification\', model=\'Bingsu/clip-vit-base-patch32-ko\')", \'api_arguments\': {\'images\': \'url\', \'candidate_labels\': \'Array of strings\', \'hypothesis_template\': \'String\'}, \'python_environment_requirements\': [\'transformers\', \'torch\', \'PIL\', \'requests\'], \'example_code\': "from transformers import pipeline\\nrepo = \'Bingsu/clip-vit-base-patch32-ko\'\\npipe = pipeline(\'zero-shot-image-classification\', model=repo)\\nurl = \'http://images.cocodataset.org/val2017/000000039769.jpg\'\\nresult = pipe(images=url, candidate_labels=[], hypothesis_template=\'{}\')\\nresult", \'performance\': {\'dataset\': \'AIHUB\', \'accuracy\': \'Not provided\'}, \'description\': \'Korean CLIP model trained by Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. It is a zero-shot image classification model that can be used to classify images without any training data.\'}', metadata={})]


 We're developing a chatbot that can quickly identify and describe images for our Chinese-speaking users.\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 would like to understand the sentiment of user's messages in a customer support chat system.\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={})]


 As a book store owner, I want to classify customer reviews into positive and negative sentiments.\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 am the owner of a news website. I have several consumers' comments about our publishing news. I want to analyze the sentiments of these comments.\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={})]


 Our business is expanding to international markets. Analyze the sentiment of the following customer review to better understand their satisfaction with our product: \"\u00a1Esto es maravilloso! Me encanta.\"\n###Input: \"\u00a1Esto es maravilloso! Me encanta.\"\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={})]


 We are a forum moderator team looking for a solution to classify comments into toxic or non-toxic categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'martin-ha/toxic-comment-model\', \'api_call\': "pipeline(model=\'martin-ha/toxic-comment-model\')", \'api_arguments\': {\'model_path\': \'martin-ha/toxic-comment-model\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline\\nmodel_path = martin-ha/toxic-comment-model\\ntokenizer = AutoTokenizer.from_pretrained(model_path)\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_path)\\npipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)\\nprint(pipeline(\'This is a test text.\'))", \'performance\': {\'dataset\': \'held-out test set\', \'accuracy\': 0.94, \'f1-score\': 0.59}, \'description\': \'This model is a fine-tuned version of the DistilBERT model to classify toxic comments.\'}', metadata={})]


 My company is launching a social media campaign. We need an AI-based system that would automatically analyze the sentiment of any user-generated reviews or tweets concerning our product.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'cardiffnlp/twitter-roberta-base-sentiment-latest\', \'api_call\': "pipeline(sentiment-analysis, model=AutoModel.from_pretrained(\'cardiffnlp/twitter-roberta-base-sentiment-latest\'), tokenizer=AutoTokenizer.from_pretrained(\'cardiffnlp/twitter-roberta-base-sentiment-latest\'))", \'api_arguments\': {\'model\': \'model_path\', \'tokenizer\': \'model_path\'}, \'python_environment_requirements\': [\'transformers\', \'numpy\', \'scipy\'], \'example_code\': \'from transformers import pipeline\\nsentiment_task = pipeline(sentiment-analysis, model=model_path, tokenizer=model_path)\\nsentiment_task(Covid cases are increasing fast!)\', \'performance\': {\'dataset\': \'tweet_eval\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The model is suitable for English.\'}', metadata={})]


 I have jobs descriptions in French for sales manager, please highlight names of organizations or cities within the text.\n###Input: \"La soci\u00e9t\u00e9 de Paris est sp\u00e9cialis\u00e9e dans la vente de v\u00e9hicules \u00e9lectriques. Responsable des ventes, vous travaillerez au sein d'une \u00e9quipe dynamique dans l'agence de Lyon. Vous \u00eates charg\u00e9(e) de d\u00e9velopper le portefeuille client et d'assurer la satisfaction des clients existants. Dans ce contexte, vous devrez travailler en lien \u00e9troit avec le directeur commercial et les autres \u00e9quipes de l'entreprise. Une exp\u00e9rience pr\u00e9alable chez Renault est un atout.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'facebook/textless_sm_en_fr\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/textless_sm_en_fr\')", \'api_arguments\': [\'input_file\'], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is a speech-to-speech translation model trained by Facebook. It is designed for translating English speech to French speech.\'}', metadata={})]


 In order to have a better understanding of our clients, I'd like to identify the names of people and organizations mentioned in the following customer review.\n###Input: \"I recently purchased a MacBook Pro from Apple Inc. and had a fantastic customer support experience. John from their tech support team was incredibly helpful and professional.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'Zixtrauce/JohnBot\', \'api_call\': "AutoModelForCausalLM.from_pretrained(\'Zixtrauce/JohnBot\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'Input a message to start chatting with Zixtrauce/JohnBot.\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'JohnBot is a conversational model based on the gpt2 architecture and trained using the Hugging Face Transformers library. It can be used for generating text responses in a chat-based interface.\'}', metadata={})]


 I am building a social media app that requires people to write fascinating stories rather than boring sentences. Detect named entities in a sentence by using an NER model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'dslim/bert-base-NER-uncased\', \'api_call\': "pipeline(\'ner\', model=\'dslim/bert-base-NER-uncased\')", \'api_arguments\': {}, \'python_environment_requirements\': {\'transformers\': \'>=4.0.0\'}, \'example_code\': "nlp(\'My name is John and I live in New York.\')", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A pretrained BERT model for Named Entity Recognition (NER) on uncased text. It can be used to extract entities such as person names, locations, and organizations from text.\'}', metadata={})]


 We have a large dataset of customer orders in the form of a table. Help us answer questions about this data.\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={})]


 Gather information about annual income and age demographics of employees to predict retirement patterns. Make sure to identify top employees for potential promotions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Transformers\', \'api_name\': \'abhishek/autotrain-adult-census-xgboost\', \'api_call\': "AutoModel.from_pretrained(\'abhishek/autotrain-adult-census-xgboost\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'scikit-learn/adult-census-income\', \'accuracy\': 0.8750191923844618}, \'description\': "A binary classification model trained on the Adult Census Income dataset using the XGBoost algorithm. The model predicts whether an individual\'s income is above or below $50,000 per year."}', metadata={})]


 To track our sales data, we need to find total sales of a specific product based on a table containing sales information per week.\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={})]


 I have a table containing information about various animals and their important characteristics. I need the system to answer a query to provide information about the tallest animal in the table.\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={})]


 You are building an app that allows users to find quick answers to textbook questions. Users will send a message with the question, and the answer should be detected directly from the textbook content.\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={})]


 We want to make sure clarify some questions about the legal implications of a new partnership contract for a real estate development project.\n###Input: We hereby grant the Licensee the exclusive right to develop, construct, operate and promote the Project, as well as to manage the daily operations of the Licensed Facilities during the Term. In consideration for the grant of the License, the Licensee shall pay to the Licensor the full amount of Ten Million (10,000,000) Dollars within thirty (30) days after the execution hereof.\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={})]


 Help me setup a tinyroberta model from deepset for Question and Answer. Provide a sample input and output.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'deepset/tinyroberta-squad2\', \'api_call\': "AutoModelForQuestionAnswering.from_pretrained(\'deepset/tinyroberta-squad2\')", \'api_arguments\': {\'model_name_or_path\': \'deepset/tinyroberta-squad2\', \'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.\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel_name = deepset/tinyroberta-squad2\\nnlp = pipeline(\'question-answering\', model=model_name, tokenizer=model_name)\\nQA_input = {\\n \'question\': \'Why is model conversion important?\',\\n \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'\\n}\\nres = nlp(QA_input)", \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': {\'exact\': 78.69114798281817, \'f1\': 81.9198998536977}}, \'description\': \'This is the distilled version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.\'}', metadata={})]


 I want to build a tool to answer questions automatically from a given document. Which model do you recommend for this task?\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={})]


 We have a French news agency and we want to categorize the news articles based on sports, politics, and science.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Classification\', \'api_name\': \'BaptisteDoyen/camembert-base-xnli\', \'api_call\': "pipeline(\'zero-shot-classification\', model=\'BaptisteDoyen/camembert-base-xnli\')", \'api_arguments\': {\'sequence\': \'str\', \'candidate_labels\': \'List[str]\', \'hypothesis_template\': \'str\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "sequence = L\'équipe de France joue aujourd\'hui au Parc des Princes\\ncandidate_labels = [sport,politique,science]\\nhypothesis_template = Ce texte parle de {}.\\nclassifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)", \'performance\': {\'dataset\': \'xnli\', \'accuracy\': {\'validation\': 81.4, \'test\': 81.7}}, \'description\': \'Camembert-base model fine-tuned on french part of XNLI dataset. One of the few Zero-Shot classification models working on French.\'}', metadata={})]


 I need a solution to detect whether a piece of news is talking about technology, sports, or politics.\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 want to build a chatbot that is used by language learners who want to communicate in French while they only know English. Generate a response for an English message.\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={})]


 Translate the following text from French to English: \u201cLe syst\u00e8me \u00e9ducatif fran\u00e7ais est compos\u00e9 d'\u00e9coles maternelles, d'\u00e9coles \u00e9l\u00e9mentaires, de coll\u00e8ges et de lyc\u00e9es.\u201d\n###Input: Le syst\u00e8me \u00e9ducatif fran\u00e7ais est compos\u00e9 d'\u00e9coles maternelles, d'\u00e9coles \u00e9l\u00e9mentaires, de coll\u00e8ges et de lyc\u00e9es.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Translation\', \'api_name\': \'opus-mt-fr-en\', \'api_call\': "pipeline(\'translation_fr_to_en\', model=\'Helsinki-NLP/opus-mt-fr-en\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "translation_pipeline(\'Bonjour, comment ça va?\')", \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.fr.en\': 33.1, \'newsdiscusstest2015-enfr.fr.en\': 38.7, \'newssyscomb2009.fr.en\': 30.3, \'news-test2008.fr.en\': 26.2, \'newstest2009.fr.en\': 30.2, \'newstest2010.fr.en\': 32.2, \'newstest2011.fr.en\': 33.0, \'newstest2012.fr.en\': 32.8, \'newstest2013.fr.en\': 33.9, \'newstest2014-fren.fr.en\': 37.8, \'Tatoeba.fr.en\': 57.5}}}, \'description\': \'Helsinki-NLP/opus-mt-fr-en is a machine translation model trained to translate from French to English. It is based on the Marian NMT framework and trained on the OPUS dataset.\'}', metadata={})]


 I want to translate a text from one language to another.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


 Our team member has written a long article that needs to be published on a company blog. Can you provide a shorter summary to be used as a snippet on the landing page?\n###Input: \"Apple Inc. reported its quarterly earnings results yesterday. The company posted a record-breaking revenue of $123.9 billion for the first quarter of 2022, up by 11% from the same period last year. The increase was fueled by stronger demand for iPhones, iPads, and Macs, as well as continued growth in its services segment. Apple's operating profit for the quarter came in at $38.3 billion, up 17% from a year earlier. The results surpassed analysts' expectations, who had anticipated revenue of around $118 billion. This strong performance is largely attributed to the successful launch of the iPhone 13, which has enjoyed robust sales since its debut in September. Apple CEO Tim Cook said in a statement, \"Our record-breaking quarter reflects the strength of our entire ecosystem, from our innovative products and services to the unmatched dedication of our teams around the world.\" Despite the ongoing global supply chain disruptions, Apple has managed to maintain its growth trajectory, thanks in part to its vertically integrated operations and nimble supply chain management. The company is expected to face stiffer competition going forward, particularly in the smartphone market, as rivals introduce new devices and increased pricing pressures.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'financial-summarization-pegasus\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'human-centered-summarization/financial-summarization-pegasus\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration\\nmodel_name = human-centered-summarization/financial-summarization-pegasus\\ntokenizer = PegasusTokenizer.from_pretrained(model_name)\\nmodel = PegasusForConditionalGeneration.from_pretrained(model_name)\\ntext_to_summarize = National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region’s third-largest lender. The entity’s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion of assets.\\ninput_ids = tokenizer(text_to_summarize, return_tensors=pt).input_ids\\noutput = model.generate(input_ids, max_length=32, num_beams=5, early_stopping=True)\\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\', \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 35.206, \'ROUGE-2\': 16.569, \'ROUGE-L\': 30.128, \'ROUGE-LSUM\': 30.171}}, \'description\': \'This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies. It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.\'}', metadata={})]


 Write a summary of a conference held by the World Health Organization discussing the impacts of climate change on human health.\n###Input: Over the past week, the World Health Organization held a conference discussing the impacts of climate change on human health. The conference brought together leading experts from around the world to examine the current problems affecting people's health due to changing environmental conditions. The topics of discussion included increased occurrence of heat-related illnesses, heightened rates of vector-borne diseases, and the growing problem of air pollution. The conference concluded with a call to action for governments and organizations to invest in mitigating and adapting to the negative consequences of climate change for the sake of public health.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Abstractive Text Summarization\', \'api_name\': \'plguillou/t5-base-fr-sum-cnndm\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'plguillou/t5-base-fr-sum-cnndm\')", \'api_arguments\': {\'input_text\': \'summarize: ARTICLE\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import T5Tokenizer, T5ForConditionalGeneration\'}, \'example_code\': \'tokenizer = T5Tokenizer.from_pretrained(plguillou/t5-base-fr-sum-cnndm)\\nmodel = T5ForConditionalGeneration.from_pretrained(plguillou/t5-base-fr-sum-cnndm)\', \'performance\': {\'dataset\': \'cnn_dailymail\', \'ROUGE-1\': 44.5252, \'ROUGE-2\': 22.652, \'ROUGE-L\': 29.8866}, \'description\': \'This model is a T5 Transformers model (JDBN/t5-base-fr-qg-fquad) that was fine-tuned in French for abstractive text summarization.\'}', metadata={})]


 Please provide a brief overview of a news article.\n###Input: A new study suggests that eating chocolate at least once a week can lead to better cognition. The study, published in the journal Appetite, analyzed data from over 900 adults and found that individuals who consumed chocolate at least once a week performed better on cognitive tests than those who consumed chocolate less frequently. Researchers believe that the beneficial effects of chocolate on cognition may be due to the presence of flavonoids, which have been shown to be antioxidant-rich and to improve brain blood flow.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Hugging Face\', \'functionality\': \'Binary Classification\', \'api_name\': \'desertdev/autotrain-imdb-sentiment-analysis-44994113085\', \'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\': \'desertdev/autotrain-data-imdb-sentiment-analysis\', \'accuracy\': 0.565}, \'description\': \'A binary classification model trained on the IMDb sentiment analysis dataset using AutoTrain. The model is capable of predicting sentiment (positive or negative) for movie reviews.\'}', metadata={})]


 I developed a document generation app, I need to create a summary of a long article given as input to provide to my users before they read the full article.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'pszemraj/long-t5-tglobal-base-16384-book-summary\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'pszemraj/long-t5-tglobal-base-16384-book-summary\')", \'api_arguments\': [\'long_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\nimport torch\\nsummarizer = pipeline(\\n summarization,\\n pszemraj/long-t5-tglobal-base-16384-book-summary,\\n device=0 if torch.cuda.is_available() else -1,\\n)\\nlong_text = Here is a lot of text I don\'t want to read. Replace me\\nresult = summarizer(long_text)\\nprint(result[0][summary_text])", \'performance\': {\'dataset\': \'kmfoda/booksum\', \'accuracy\': {\'ROUGE-1\': 36.408, \'ROUGE-2\': 6.065, \'ROUGE-L\': 16.721, \'ROUGE-LSUM\': 33.34}}, \'description\': \'A fine-tuned version of google/long-t5-tglobal-base on the kmfoda/booksum dataset, which can be used to summarize long text and generate SparkNotes-esque summaries of arbitrary topics. The model generalizes reasonably well to academic and narrative text.\'}', metadata={})]


 We need a quick summary of a news article we found online. Can you help us with that?\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={})]


 We'd like our chatbot to act as a fictional character for engaging with our users.\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={})]


 Write a story about a spaceship journey to a distant planet in search of a new home for humanity.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'prompthero/openjourney\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'prompthero/openjourney\', torch_dtype=torch.float16)", \'api_arguments\': {\'prompt\': \'string\'}, \'python_environment_requirements\': [\'diffusers\', \'torch\'], \'example_code\': \'from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = prompthero/openjourney\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = retro serie of different cars with different colors and shapes, mdjrny-v4 style\\nimage = pipe(prompt).images[0]\\nimage.save(./retro_cars.png)\', \'performance\': {\'dataset\': \'Midjourney images\', \'accuracy\': \'Not specified\'}, \'description\': \'Openjourney is an open source Stable Diffusion fine-tuned model on Midjourney images, by PromptHero. It can be used for generating AI art based on text prompts.\'}', metadata={})]


 I want to write a story about a brave knight and a dragon but I'm unable to come up with a good start. Help me with that.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


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


 Help me fill in the blanks in the following Chinese sentence: \"\u4e0a\u6d77\u662f\u4e2d\u56fd\u7684[MASK]\u5927\u57ce\u5e02\u3002\"\n###Input: \u4e0a\u6d77\u662f\u4e2d\u56fd\u7684[MASK]\u5927\u57ce\u5e02\u3002\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'uer/albert-base-chinese-cluecorpussmall\', \'api_call\': "AlbertForMaskedLM.from_pretrained(\'uer/albert-base-chinese-cluecorpussmall\')", \'api_arguments\': [\'model\', \'tokenizer\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import BertTokenizer, AlbertForMaskedLM, FillMaskPipeline\\ntokenizer = BertTokenizer.from_pretrained(uer/albert-base-chinese-cluecorpussmall)\\nmodel = AlbertForMaskedLM.from_pretrained(uer/albert-base-chinese-cluecorpussmall)\\nunmasker = FillMaskPipeline(model, tokenizer)\\nunmasker(中国的首都是[MASK]京。)\', \'performance\': {\'dataset\': \'CLUECorpusSmall\', \'accuracy\': \'Not provided\'}, \'description\': \'This is the set of Chinese ALBERT models pre-trained by UER-py on the CLUECorpusSmall dataset. The model can be used for tasks like text generation and feature extraction.\'}', metadata={})]


 We are building a source code autocompletion tool which will complete the code snippet containing a masked token.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'camembert-base\', \'api_call\': "pipeline(\'fill-mask\', model=\'camembert-base\', tokenizer=\'camembert-base\')", \'api_arguments\': [\'model\', \'tokenizer\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline; camembert_fill_mask = pipeline(\'fill-mask\', model=\'camembert-base\', tokenizer=\'camembert-base\'); results = camembert_fill_mask(\'Le camembert est <mask> :)\')", \'performance\': {\'dataset\': \'oscar\', \'accuracy\': \'N/A\'}, \'description\': \'CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data, and pretraining data source domains. It can be used for Fill-Mask tasks.\'}', metadata={})]


 I work for a Japanese company, and my manager needs me to take a look at a request from a client. I can understand fluent Japanese, but I need a little help filling in missing words from the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'cl-tohoku/bert-base-japanese\', \'api_call\': "AutoModelForMaskedLM.from_pretrained(\'cl-tohoku/bert-base-japanese\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "fill_mask(\'[MASK]\')", \'performance\': {\'dataset\': \'wikipedia\', \'accuracy\': \'N/A\'}, \'description\': \'This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization.\'}', metadata={})]


 We are building a platform to compare and contrast user input sentences with existing sentences in our database. It should return similar results.\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-v1\', \'api_call\': "SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v1\')", \'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-v1\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'N/A\'}, \'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={})]


 I need a method to compare the similarity between two sentences to be used within a meme generator, so we can produce a meme with a similar caption.\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={})]


 A student is writing a research paper and needs help with finding similar articles in order to include them in the literature review section.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Embeddings\', \'api_name\': \'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\', \'api_call\': "SentenceTransformer(\'sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Automated evaluation\'}, \'description\': \'This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 Create a solution to convert a given Japanese sentence into a speech audio file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Speech Recognition\', \'api_name\': \'jonatasgrosman/wav2vec2-large-xlsr-53-japanese\', \'api_call\': "SpeechRecognitionModel(\'jonatasgrosman/wav2vec2-large-xlsr-53-japanese\')", \'api_arguments\': [\'audio_paths\'], \'python_environment_requirements\': [\'huggingsound\', \'torch\', \'librosa\', \'datasets\', \'transformers\'], \'example_code\': \'from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel(jonatasgrosman/wav2vec2-large-xlsr-53-japanese)\\naudio_paths = [/path/to/file.mp3, /path/to/another_file.wav]\\ntranscriptions = model.transcribe(audio_paths)\', \'performance\': {\'dataset\': \'common_voice\', \'accuracy\': {\'WER\': 81.8, \'CER\': 20.16}}, \'description\': \'Fine-tuned XLSR-53 large model for speech recognition in Japanese. Trained on Common Voice 6.1, CSS10, and JSUT datasets. Make sure your speech input is sampled at 16kHz.\'}', metadata={})]


 We are working on a transcription service for our customers. We need a way to convert audio files into text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]