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


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


 I would like to create an application that identifies animals in Chinese language image captions. Specifically, we want to know if a picture includes a cat or a dog.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


 I need a portable system to perform quick sentiment analysis of customer reviews.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'lvwerra/distilbert-imdb\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'lvwerra/distilbert-imdb\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\', \'pytorch\'], \'example_code\': "classifier(\'I love this movie!\')", \'performance\': {\'dataset\': \'imdb\', \'accuracy\': 0.928}, \'description\': \'This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It is used for sentiment analysis on movie reviews and achieves an accuracy of 0.928 on the evaluation set.\'}', metadata={})]


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


 We have movie reviews on our website, and we need to display whether the review is positive or negative.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'results-yelp\', \'api_call\': "AutoTokenizer.from_pretrained(\'bert-base-uncased\')", \'api_arguments\': {\'tokenizer\': "AutoTokenizer.from_pretrained(\'bert-base-uncased\')", \'config\': "AutoConfig.from_pretrained(\'potatobunny/results-yelp\')"}, \'python_environment_requirements\': {\'Transformers\': \'4.18.0\', \'Pytorch\': \'1.10.0+cu111\', \'Datasets\': \'2.0.0\', \'Tokenizers\': \'0.12.1\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'Yelp\', \'accuracy\': 0.9302}, \'description\': \'This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only. It is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.\'}', metadata={})]


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


 Develop a tool to analyze restaurant reviews from Yelp for positive or negative sentiments.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'results-yelp\', \'api_call\': "AutoTokenizer.from_pretrained(\'bert-base-uncased\')", \'api_arguments\': {\'tokenizer\': "AutoTokenizer.from_pretrained(\'bert-base-uncased\')", \'config\': "AutoConfig.from_pretrained(\'potatobunny/results-yelp\')"}, \'python_environment_requirements\': {\'Transformers\': \'4.18.0\', \'Pytorch\': \'1.10.0+cu111\', \'Datasets\': \'2.0.0\', \'Tokenizers\': \'0.12.1\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'Yelp\', \'accuracy\': 0.9302}, \'description\': \'This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only. It is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.\'}', metadata={})]


 We're creating a chatbot that will detect the user's emotion. I want to start by implementing the basic functionality of emotion detection from the user's responses.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Conversational\', \'api_name\': \'Pi3141/DialoGPT-medium-elon-3\', \'api_call\': "pipeline(\'text-generation\', model=\'Pi3141/DialoGPT-medium-elon-3\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'Input a message to start chatting with Pi3141/DialoGPT-medium-elon-3.\', \'performance\': {\'dataset\': \'Twitter tweets by Elon Musk\', \'accuracy\': \'N/A\'}, \'description\': \'DialoGPT model that talks like Elon Musk, trained on Twitter tweets by Elon Musk. This model will spew meaningless shit about 40% of the time. Trained on 8 epochs. But with a larger dataset this time. The AI can now use more emojis, I think.\'}', metadata={})]


 I am a journalist. I am writing an article about European start-ups. I need to see all the dates and company names in the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


 Create a tool to extract entities from news articles to help journalists research important figures and organizations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


 A famous writer is working on a novel. He needs your help to predict the punctuation marks needed in his written draft.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Information Retrieval\', \'api_name\': \'cross-encoder/ms-marco-TinyBERT-L-2-v2\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'cross-encoder/ms-marco-TinyBERT-L-2-v2\')", \'api_arguments\': {\'tokenizer\': "tokenizer = AutoTokenizer.from_pretrained(\'model_name\')", \'features\': "features = tokenizer([\'How many people live in Berlin?\', \'How many people live in Berlin?\'], [\'Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.\', \'New York City is famous for the Metropolitan Museum of Art.\'], padding=True, truncation=True, return_tensors=\'pt\')"}, \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': {\'import\': \'from transformers import AutoTokenizer, AutoModelForSequenceClassification\', \'model\': "model = AutoModelForSequenceClassification.from_pretrained(\'model_name\')", \'tokenizer\': "tokenizer = AutoTokenizer.from_pretrained(\'model_name\')", \'features\': "features = tokenizer([\'How many people live in Berlin?\', \'How many people live in Berlin?\'], [\'Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.\', \'New York City is famous for the Metropolitan Museum of Art.\'], padding=True, truncation=True, return_tensors=\'pt\')", \'scores\': \'with torch.no_grad():\\n    scores = model(**features).logits\\n    print(scores)\'}, \'performance\': {\'dataset\': \'TREC Deep Learning 2019\', \'accuracy\': \'69.84 (NDCG@10)\'}, \'description\': \'This model was trained on the MS Marco Passage Ranking task. It can be used for Information Retrieval: Given a query, encode the query with all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. The training code is available here: SBERT.net Training MS Marco.\'}', metadata={})]


 There's a collection of texts that we want to be able to analyze for their part-of-speech tags to better understand the structure of the sentences within the texts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\', \'api_call\': "pipeline(\'image-classification\', model=\'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\')", \'api_arguments\': [\'image\', \'possible_class_names\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; classifier = pipeline(\'image-classification\', model=\'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\'); classifier(image, possible_class_names=[\'cat\', \'dog\'])", \'performance\': {\'dataset\': \'ImageNet-1k\', \'accuracy\': \'80.1\'}, \'description\': \'A CLIP ViT-bigG/14 model trained with the LAION-2B English subset of LAION-5B using OpenCLIP. The model is intended for research purposes and enables researchers to better understand and explore zero-shot, arbitrary image classification. It can be used for interdisciplinary studies of the potential impact of such models. The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k.\'}', metadata={})]


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


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


 Develop a tool that helps me get answers to questions related to a specific text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-small-finetuned-wikisql-supervised\', \'api_call\': "TapasForQuestionAnswering.from_pretrained(\'google/tapas-small-finetuned-wikisql-supervised\')", \'api_arguments\': "model = TapasForQuestionAnswering.from_pretrained(\'google/tapas-small-finetuned-wikisql-supervised\')", \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import TapasTokenizer, TapasForQuestionAnswering\\ntokenizer = TapasTokenizer.from_pretrained(\'google/tapas-small-finetuned-wikisql-supervised\')\\nmodel = TapasForQuestionAnswering.from_pretrained(\'google/tapas-small-finetuned-wikisql-supervised\')", \'performance\': {\'dataset\': \'wikisql\', \'accuracy\': \'Not specified\'}, \'description\': \'TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. This model is fine-tuned on WikiSQL and can be used for answering questions related to a table.\'}', metadata={})]


 A healthcare professional wants to get quick answers to COVID-19 related questions from the latest research articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-base-finetuned-sqa\', \'api_call\': "TapasTokenizer.from_pretrained(\'google/tapas-base-finetuned-sqa\')", \'api_arguments\': [\'question\', \'table\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\n\\n# Initialize the pipeline\\ntapas_pipeline = pipeline(\'table-question-answering\', model=\'google/tapas-base-finetuned-sqa\')\\n\\n# Define the table and question\\nquestion = \'How many goals did player A score?\'\\ntable = [[\'Player\', \'Goals\'], [\'Player A\', 5], [\'Player B\', 3]]\\n\\n# Get the answer\\nresult = tapas_pipeline(question=question, table=table)\\nprint(result)", \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6874}, \'description\': \'TAPAS base model fine-tuned on Sequential Question Answering (SQA). It is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia and fine-tuned on SQA. It can be used for answering questions related to a table in a conversational set-up.\'}', metadata={})]


 I have this app for sharing cooking recipes. Users upload photos and ask questions about the showcased recipe. I need to automatically answer their questions based on the recipe image provided.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 The company's legal team is working on a case. They need a highly accurate tool to extract answers from a large set of legal documents. Develop a tool for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'nlpaueb/legal-bert-small-uncased\', \'api_call\': "AutoModel.from_pretrained(\'nlpaueb/legal-bert-small-uncased\')", \'api_arguments\': {\'pretrained_model_name_or_path\': \'nlpaueb/legal-bert-small-uncased\'}, \'python_environment_requirements\': {\'transformers\': \'AutoTokenizer, AutoModel\'}, \'example_code\': "from transformers import AutoTokenizer, AutoModel\\ntokenizer = AutoTokenizer.from_pretrained(\'nlpaueb/legal-bert-small-uncased\')\\nmodel = AutoModel.from_pretrained(\'nlpaueb/legal-bert-small-uncased\')", \'performance\': {\'dataset\': \'Legal Corpora\', \'accuracy\': \'Comparable to larger models\'}, \'description\': \'LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. This is the light-weight version of BERT-BASE (33% the size of BERT-BASE) pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint.\'}', metadata={})]


 We have received a customer inquiry. Help us categorize the inquiry into one of the following categories: \"sales\", \"technical support\", or \"billing\".\n###Input: \"I am experiencing difficulty with the installation process of your software.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'videomae-small-finetuned-ssv2\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-small-finetuned-ssv2\')", \'api_arguments\': {\'model_name\': \'MCG-NJU/videomae-small-finetuned-ssv2\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification\', \'numpy\': \'import numpy as np\', \'torch\': \'import torch\'}, \'example_code\': \'video = list(np.random.randn(16, 3, 224, 224))\\nfeature_extractor = VideoMAEFeatureExtractor.from_pretrained(MCG-NJU/videomae-small-finetuned-ssv2)\\nmodel = VideoMAEForVideoClassification.from_pretrained(MCG-NJU/videomae-small-finetuned-ssv2)\\ninputs = feature_extractor(video, return_tensors=pt)\\nwith torch.no_grad():\\n  outputs = model(**inputs)\\n  logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(Predicted class:, model.config.id2label[predicted_class_idx])\', \'performance\': {\'dataset\': \'Something-Something V2\', \'accuracy\': {\'top-1\': 66.8, \'top-5\': 90.3}}, \'description\': \'VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.\'}', metadata={})]


 Imagine you have a news aggregator platform, and you want it to separate news articles into categories like Politics, Sports, Technology, Business, and Entertainment. Implement the classification using this API.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Feature Extraction\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'DeepPavlov/rubert-base-cased\', \'api_call\': "AutoModel.from_pretrained(\'DeepPavlov/rubert-base-cased\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Russian part of Wikipedia and news data\', \'accuracy\': \'\'}, \'description\': \'RuBERT (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT[1].\'}', metadata={})]


 I need to classify German news articles into categories like crime, tragedy, and theft. The classifier should be able to understand German as well.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'padmalcom/wav2vec2-large-emotion-detection-german\', \'api_call\': "pipeline(\'audio-classification\', model=\'padmalcom/wav2vec2-large-emotion-detection-german\')", \'api_arguments\': \'audio_file\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': "from transformers import pipeline\\naudio_classifier = pipeline(\'audio-classification\', model=\'padmalcom/wav2vec2-large-emotion-detection-german\')\\nresult = audio_classifier(audio_file)", \'performance\': {\'dataset\': \'emo-DB\', \'accuracy\': \'Not provided\'}, \'description\': \'This wav2vec2 based emotion detection model is trained on the emo-DB dataset. It can classify emotions in German audio files into seven classes: anger, boredom, disgust, fear, happiness, sadness, and neutral.\'}', metadata={})]


 The company has just received a document written in French, and they need it translated into English.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 A business collaboration project requires staff to translate Russian documents into English.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'sberbank-ai/sbert_large_mt_nlu_ru\', \'api_call\': "AutoModel.from_pretrained(\'sberbank-ai/sbert_large_mt_nlu_ru\')", \'api_arguments\': [\'sentences\', \'padding\', \'truncation\', \'max_length\', \'return_tensors\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import AutoTokenizer, AutoModel\\nimport torch\\n\\n\\n# Mean Pooling - Take attention mask into account for correct averaging\\ndef mean_pooling(model_output, attention_mask):\\n    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings\\n    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\\n    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)\\n    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)\\n    return sum_embeddings / sum_mask\\n\\n\\n# Sentences we want sentence embeddings for sentences = [\'?\']\\n\\n# Load AutoModel from huggingface model repository\\ntokenizer = AutoTokenizer.from_pretrained(sberbank-ai/sbert_large_mt_nlu_ru)\\nmodel = AutoModel.from_pretrained(sberbank-ai/sbert_large_mt_nlu_ru)\\n\\n# Tokenize sentences\\nencoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, return_tensors=\'pt\')\\n\\n# Compute token embeddings\\nwith torch.no_grad():\\n    model_output = model(**encoded_input)\\n\\n# Perform pooling. In this case, mean pooling\\nsentence_embeddings = mean_pooling(model_output, encoded_input[\'attention_mask\'])", \'performance\': {\'dataset\': \'Russian SuperGLUE\', \'accuracy\': \'Not provided\'}, \'description\': \'BERT large model multitask (cased) for Sentence Embeddings in Russian language.\'}', metadata={})]


 In this fast world, a user spends very little time on reading news articles and requires summary of the news articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Feature Extraction\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'DeepPavlov/rubert-base-cased\', \'api_call\': "AutoModel.from_pretrained(\'DeepPavlov/rubert-base-cased\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Russian part of Wikipedia and news data\', \'accuracy\': \'\'}, \'description\': \'RuBERT (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT[1].\'}', metadata={})]


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


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


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


 Let's build a Chatbot that can solve daily life problems of a research Assistant.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image Generation\', \'api_name\': \'stabilityai/stable-diffusion-2-1-base\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'stabilityai/stable-diffusion-2-1-base\', scheduler=EulerDiscreteScheduler.from_pretrained(stabilityai/stable-diffusion-2-1-base, subfolder=scheduler), torch_dtype=torch.float16)", \'api_arguments\': {\'prompt\': \'a photo of an astronaut riding a horse on mars\'}, \'python_environment_requirements\': [\'diffusers\', \'transformers\', \'accelerate\', \'scipy\', \'safetensors\'], \'example_code\': {\'install_dependencies\': \'pip install diffusers transformers accelerate scipy safetensors\', \'code\': \'from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\\nimport torch\\nmodel_id = stabilityai/stable-diffusion-2-1-base\\nscheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=scheduler)\\npipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = a photo of an astronaut riding a horse on mars\\nimage = pipe(prompt).images[0]\\nimage.save(astronaut_rides_horse.png)\'}, \'performance\': {\'dataset\': \'COCO2017 validation set\', \'accuracy\': \'Not optimized for FID scores\'}, \'description\': \'Stable Diffusion v2-1-base is a diffusion-based text-to-image generation model that can generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H). It is intended for research purposes only and can be used in areas such as safe deployment of models, understanding limitations and biases of generative models, generation of artworks, and research on generative models.\'}', metadata={})]


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


 I want to create a chatbot for discussing gardening topics and tips, which can refer to external information while generating responses.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Conversational\', \'api_name\': \'microsoft/GODEL-v1_1-base-seq2seq\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'microsoft/GODEL-v1_1-base-seq2seq\')", \'api_arguments\': [\'instruction\', \'knowledge\', \'dialog\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained(microsoft/GODEL-v1_1-base-seq2seq)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(microsoft/GODEL-v1_1-base-seq2seq)\\ndef generate(instruction, knowledge, dialog):\\n if knowledge != \'\':\\n knowledge = \'[KNOWLEDGE] \' + knowledge\\n dialog = \' EOS \'.join(dialog)\\n query = f{instruction} [CONTEXT] {dialog} {knowledge}\\n input_ids = tokenizer(f{query}, return_tensors=pt).input_ids\\n outputs = model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True)\\n output = tokenizer.decode(outputs[0], skip_special_tokens=True)\\n return output", \'performance\': {\'dataset\': \'Reddit discussion thread, instruction and knowledge grounded dialogs\', \'accuracy\': \'N/A\'}, \'description\': \'GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs. The v1.1 model is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs.\'}', metadata={})]


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


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


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


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


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


 I have some paragraphs that I want to use as a source for generating questions, based on the information present in them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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 am a novel writer. I plan to write some paragraphs, but I cannot find the exact word placeholder or missing word. Can you help me make a tool to complete the given text below?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Summarization\', \'api_name\': \'distilbart-cnn-12-6-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/distilbart-cnn-12-6-samsum\')", \'api_arguments\': {\'model\': \'philschmid/distilbart-cnn-12-6-samsum\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/distilbart-cnn-12-6-samsum)\\nconversation = \'\'\'Jeff: Can I train a 🤗 Transformers model on Amazon SageMaker? \\nPhilipp: Sure you can use the new Hugging Face Deep Learning Container. \\nJeff: ok.\\nJeff: and how can I get started? \\nJeff: where can I find documentation? \\nPhilipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face <br />\\n\'\'\'\\nsummarizer(conversation)", \'performance\': {\'dataset\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 41.09, \'ROUGE-2\': 20.746, \'ROUGE-L\': 31.595, \'ROUGE-LSUM\': 38.339}}, \'description\': \'This model is a DistilBART-based text summarization model trained on the SAMsum dataset. It can be used to generate summaries of conversational text.\'}', metadata={})]


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


 I am building a recommendation engine to group news articles. I need a way to determine the similarity between two sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


 The company needs a tool to analyze customers' reviews about their products. We need to find out which ones are positive, neutral, or negative.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


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


 Assist a client producing an audiobook in Japanese. They need a solution to convert their text script into spoken Japanese.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'mio/Artoria\', \'api_call\': "pipeline(\'text-to-speech\', model=\'mio/Artoria\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'mio/Artoria\'); tts(\'s\')", \'performance\': {\'dataset\': \'fate\', \'accuracy\': \'Not provided\'}, \'description\': \'This model was trained by mio using fate recipe in espnet. It is a text-to-speech model that can convert text input into speech output.\'}', metadata={})]


 We have a product for the German-speaking audience, and we need to generate some sample voiceovers for the promotional videos.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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 work as a transcriptionist, and I have an hour-long recording of a meeting. I need to identify and segment the speech of various speakers in the recording.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 An audio file is recorded in a conference and we need the text version of the conversation for record-keeping purposes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Provide a way to translate spoken English to spoken Hokkien for an audio file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'microsoft/wavlm-large\', \'api_call\': "Wav2Vec2Model.from_pretrained(\'microsoft/wavlm-large\')", \'api_arguments\': \'speech input\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'To fine-tune the model for speech recognition, see the official speech recognition example. To fine-tune the model for speech classification, see the official audio classification example.\', \'performance\': {\'dataset\': \'SUPERB benchmark\', \'accuracy\': \'state-of-the-art performance\'}, \'description\': \'WavLM-Large is a large model pretrained on 16kHz sampled speech audio. It is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. WavLM is pretrained on 60,000 hours of Libri-Light, 10,000 hours of GigaSpeech, and 24,000 hours of VoxPopuli. It achieves state-of-the-art performance on the SUPERB benchmark and brings significant improvements for various speech processing tasks on their representative benchmarks.\'}', metadata={})]


 We want to separate the speaker sources from the original audio file to filter the noise.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'pyannote.audio\', \'functionality\': \'Speaker Diarization\', \'api_name\': \'pyannote/speaker-diarization\', \'api_call\': "Pipeline.from_pretrained(\'pyannote/speaker-diarization@2.1\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': {\'num_speakers\': \'int (optional)\', \'min_speakers\': \'int (optional)\', \'max_speakers\': \'int (optional)\'}, \'python_environment_requirements\': \'pyannote.audio 2.1.1\', \'example_code\': [\'from pyannote.audio import Pipeline\', \'pipeline = Pipeline.from_pretrained(pyannote/speaker-diarization@2.1, use_auth_token=ACCESS_TOKEN_GOES_HERE)\', \'diarization = pipeline(audio.wav)\', \'with open(audio.rttm, w) as rttm:\', \'  diarization.write_rttm(rttm)\'], \'performance\': {\'dataset\': \'ami\', \'accuracy\': {\'DER%\': \'18.91\', \'FA%\': \'4.48\', \'Miss%\': \'9.51\', \'Conf%\': \'4.91\'}}, \'description\': \'This API provides an automatic speaker diarization pipeline using the pyannote.audio framework. It can process audio files and output speaker diarization results in RTTM format. The pipeline can also handle cases where the number of speakers is known in advance or when providing lower and/or upper bounds on the number of speakers.\'}', metadata={})]


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


 I want to make a conference call app which differentiates between the times when the user is speaking and when there is no voice activity. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'promptcap-coco-vqa\', \'api_call\': "PromptCap(\'vqascore/promptcap-coco-vqa\')", \'api_arguments\': {\'prompt\': \'string\', \'image\': \'string\'}, \'python_environment_requirements\': \'pip install promptcap\', \'example_code\': [\'import torch\', \'from promptcap import PromptCap\', \'model = PromptCap(vqascore/promptcap-coco-vqa)\', \'if torch.cuda.is_available():\', \'  model.cuda()\', \'prompt = please describe this image according to the given question: what piece of clothing is this boy putting on?\', \'image = glove_boy.jpeg\', \'print(model.caption(prompt, image))\'], \'performance\': {\'dataset\': {\'coco\': {\'accuracy\': \'150 CIDEr\'}, \'OK-VQA\': {\'accuracy\': \'60.4%\'}, \'A-OKVQA\': {\'accuracy\': \'59.6%\'}}}, \'description\': \'PromptCap is a captioning model that can be controlled by natural language instruction. The instruction may contain a question that the user is interested in. It achieves SOTA performance on COCO captioning (150 CIDEr) and knowledge-based VQA tasks when paired with GPT-3 (60.4% on OK-VQA and 59.6% on A-OKVQA).\'}', metadata={})]


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


 Our company wants to predict the potential negative impact on the environment based on certain factors.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\', \'api_call\': "pipeline(\'image-classification\', model=\'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\')", \'api_arguments\': [\'image\', \'possible_class_names\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; classifier = pipeline(\'image-classification\', model=\'laion/CLIP-ViT-bigG-14-laion2B-39B-b160k\'); classifier(image, possible_class_names=[\'cat\', \'dog\'])", \'performance\': {\'dataset\': \'ImageNet-1k\', \'accuracy\': \'80.1\'}, \'description\': \'A CLIP ViT-bigG/14 model trained with the LAION-2B English subset of LAION-5B using OpenCLIP. The model is intended for research purposes and enables researchers to better understand and explore zero-shot, arbitrary image classification. It can be used for interdisciplinary studies of the potential impact of such models. The model achieves a 80.1 zero-shot top-1 accuracy on ImageNet-1k.\'}', metadata={})]


 I have a dataset with CO2 emissions in a CSV file, and I want to classify which sources have high or low emissions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Text-to-Text Generation\', \'api_name\': \'optimum/t5-small\', \'api_call\': "ORTModelForSeq2SeqLM.from_pretrained(\'optimum/t5-small\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'optimum.onnxruntime\'], \'example_code\': \'from transformers import AutoTokenizer, pipeline\\nfrom optimum.onnxruntime import ORTModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained(optimum/t5-small)\\nmodel = ORTModelForSeq2SeqLM.from_pretrained(optimum/t5-small)\\ntranslator = pipeline(translation_en_to_fr, model=model, tokenizer=tokenizer)\\nresults = translator(My name is Eustache and I have a pet raccoon)\\nprint(results)\', \'performance\': {\'dataset\': \'c4\', \'accuracy\': \'N/A\'}, \'description\': \'T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. It can be used for translation, text-to-text generation, and summarization.\'}', metadata={})]


 I am building a recommendation engine to recommend linkbuilding strategy to clients. I have data in csv files that needs to be classified.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Create a model to estimate the carbon emissions of a specific device by using features like idle power, standby power, and active power.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'sshleifer/distilbart-cnn-12-6\', \'api_call\': "BartForConditionalGeneration.from_pretrained(\'sshleifer/distilbart-cnn-12-6\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'huggingface/transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': [{\'name\': \'cnn_dailymail\', \'accuracy\': {\'Rouge 2\': \'22.12\', \'Rouge-L\': \'36.99\'}}]}, \'description\': "DistilBART is a distilled version of BART, a model for text summarization. This specific checkpoint, \'sshleifer/distilbart-cnn-12-6\', is trained on the cnn_dailymail dataset and provides a fast and effective way to generate summaries of text. The model can be loaded using the Hugging Face Transformers library."}', metadata={})]