 Bob is designing an app for his city. He needs to quickly identify if the provided image has a bike or a car. Design a model that recognizes a bike or car and provides classification output for the given image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Transformers\', \'functionality\': \'Cross-Encoder for Natural Language Inference\', \'api_name\': \'cross-encoder/nli-deberta-v3-base\', \'api_call\': "CrossEncoder(\'cross-encoder/nli-deberta-v3-base\')", \'api_arguments\': [\'sentence_pairs\'], \'python_environment_requirements\': [\'sentence_transformers\', \'transformers\'], \'example_code\': "from sentence_transformers import CrossEncoder\\nmodel = CrossEncoder(\'cross-encoder/nli-deberta-v3-base\')\\nscores = model.predict([(\'A man is eating pizza\', \'A man eats something\'), (\'A black race car starts up in front of a crowd of people.\', \'A man is driving down a lonely road.\')])", \'performance\': {\'dataset\': {\'SNLI-test\': \'92.38\', \'MNLI mismatched set\': \'90.04\'}}, \'description\': \'This model is based on microsoft/deberta-v3-base and was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.\'}', metadata={})]


 I need to analyze the sentiment of tweets to find out which products are getting positive customer feedback.\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={})]


 We are building a content moderation system. Our clients upload the content, it can be generated by human or AI. We want to have a filtering API to advise on the original text if it is generated by GPT-2.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Detect GPT-2 generated text\', \'api_name\': \'roberta-base-openai-detector\', \'api_call\': "pipeline(\'text-classification\', model=\'roberta-base-openai-detector\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import pipeline\\npipe = pipeline(text-classification, model=roberta-base-openai-detector)\\nprint(pipe(Hello world! Is this content AI-generated?))\', \'performance\': {\'dataset\': \'WebText\', \'accuracy\': \'95%\'}, \'description\': \'RoBERTa base OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa base model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model.\'}', metadata={})]


 I have a list of comments related to stocks. Check the overall sentiment towards each stock.\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'd like to give a potted plant to my friend. Write me a short and simple plant care instruction.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Generative Commonsense Reasoning\', \'api_name\': \'mrm8488/t5-base-finetuned-common_gen\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'mrm8488/t5-base-finetuned-common_gen\')", \'api_arguments\': [\'words\', \'max_length\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelWithLMHead, AutoTokenizer\\ntokenizer = AutoTokenizer.from_pretrained(mrm8488/t5-base-finetuned-common_gen)\\nmodel = AutoModelWithLMHead.from_pretrained(mrm8488/t5-base-finetuned-common_gen)\\ndef gen_sentence(words, max_length=32):\\n input_text = words\\n features = tokenizer([input_text], return_tensors=\'pt\')\\noutput = model.generate(input_ids=features[\'input_ids\'], attention_mask=features[\'attention_mask\'], max_length=max_length)\\nreturn tokenizer.decode(output[0], skip_special_tokens=True)\\nwords = tree plant ground hole dig\\ngen_sentence(words)", \'performance\': {\'dataset\': \'common_gen\', \'accuracy\': {\'ROUGE-2\': 17.1, \'ROUGE-L\': 39.47}}, \'description\': "Google\'s T5 fine-tuned on CommonGen for Generative Commonsense Reasoning. CommonGen is a constrained text generation task, associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Given a set of common concepts; the task is to generate a coherent sentence describing an everyday scenario using these concepts."}', metadata={})]


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


 I need an AI feature capable of extracting names of people, organizations, and locations from various news articles in different languages.\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 are a consulting firm, and we want to easily identify company names from texts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Entity Extraction\', \'api_name\': \'903429548\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'ismail-lucifer011/autotrain-company_all-903429548\', use_auth_token=True)", \'api_arguments\': {\'inputs\': \'I love AutoTrain\'}, \'python_environment_requirements\': {\'transformers\': \'AutoModelForTokenClassification, AutoTokenizer\'}, \'example_code\': \'from transformers import AutoModelForTokenClassification, AutoTokenizer\\nmodel = AutoModelForTokenClassification.from_pretrained(ismail-lucifer011/autotrain-company_all-903429548, use_auth_token=True)\\ntokenizer = AutoTokenizer.from_pretrained(ismail-lucifer011/autotrain-company_all-903429548, use_auth_token=True)\\ninputs = tokenizer(I love AutoTrain, return_tensors=pt)\\noutputs = model(**inputs)\', \'performance\': {\'dataset\': \'ismail-lucifer011/autotrain-data-company_all\', \'accuracy\': 0.9979930566588805}, \'description\': \'A token classification model trained using AutoTrain for entity extraction. The model is based on the distilbert architecture and trained on the ismail-lucifer011/autotrain-data-company_all dataset. It can be used to identify and extract company names from text.\'}', metadata={})]


 Our company is making a chatbot that needs to extract information from a paragraph. Get the named entities in the paragraph.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'facebook/blenderbot-1B-distill\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'facebook/blenderbot-1B-distill\')", \'api_arguments\': {\'input_message\': \'string\'}, \'python_environment_requirements\': {\'library\': \'transformers\', \'version\': \'latest\'}, \'example_code\': "inputs = tokenizer(\'Hello, how are you?\', return_tensors=\'pt\'); outputs = model.generate(inputs[\'input_ids\']); decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)", \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not mentioned\'}, \'description\': \'BlenderBot-1B is a large-scale open-domain chatbot model that can engage in conversations, ask and answer questions, and display knowledge, empathy, and personality. This distilled version is smaller and faster than the original 9.4B parameter model, making it more accessible for use.\'}', metadata={})]


A journalist is looking for historical Olympic host cities and wants to find the year when Beijing hosted the games.\n###Input: {\"table\": {\n    \"year\": [1896, 1900, 1904, 2004, 2008, 2012],\n    \"city\": [\"Athens\", \"Paris\", \"St. Louis\", \"Athens\", \"Beijing\", \"London\"]},\n    \"query\": \"Select the year when Beijing hosted the Olympic games\"}\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={})]


 I am a Korean exchange student in the United States. I am building a Korean Table Question Answering assistant to help me with my studies.\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 an API that processes large data sets of financial transactions and can deliver information on the number of transactions and their monetary value, based on a date range.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Flair\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'flair/ner-english-ontonotes-large\', \'api_call\': "SequenceTagger.load(\'flair/ner-english-ontonotes-large\')", \'api_arguments\': [\'sentence\'], \'python_environment_requirements\': [\'flair\'], \'example_code\': "from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\n\\n# load tagger\\ntagger = SequenceTagger.load(\'flair/ner-english-ontonotes-large\')\\n\\n# make example sentence\\nsentence = Sentence(\'On September 1st George won 1 dollar while watching Game of Thrones.\')\\n\\n# predict NER tags\\ntagger.predict(sentence)\\n\\n# print sentence\\nprint(sentence)\\n\\n# print predicted NER spans\\nprint(\'The following NER tags are found:\')\\n\\n# iterate over entities and print\\nfor entity in sentence.get_spans(\'ner\'):\\n    print(entity)", \'performance\': {\'dataset\': \'Ontonotes\', \'accuracy\': 90.93}, \'description\': \'English NER in Flair (Ontonotes large model). This is the large 18-class NER model for English that ships with Flair. It predicts 18 tags such as cardinal value, date value, event name, building name, geo-political entity, language name, law name, location name, money name, affiliation, ordinal value, organization name, percent value, person name, product name, quantity value, time value, and name of work of art. The model is based on document-level XLM-R embeddings and FLERT.\'}', metadata={})]


 A product manager needs an explanation related to model conversion. They are confused why it is important. Can you please help them by providing an answer?\n###Input: \n{\n 'question': 'Why is model conversion important?',\n 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'\n}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'deepset/tinyroberta-squad2\', \'api_call\': "AutoModelForQuestionAnswering.from_pretrained(\'deepset/tinyroberta-squad2\')", \'api_arguments\': {\'model_name_or_path\': \'deepset/tinyroberta-squad2\', \'question\': \'Why is model conversion important?\', \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\\nmodel_name = deepset/tinyroberta-squad2\\nnlp = pipeline(\'question-answering\', model=model_name, tokenizer=model_name)\\nQA_input = {\\n \'question\': \'Why is model conversion important?\',\\n \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'\\n}\\nres = nlp(QA_input)", \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': {\'exact\': 78.69114798281817, \'f1\': 81.9198998536977}}, \'description\': \'This is the distilled version of the deepset/roberta-base-squad2 model. This model has a comparable prediction quality and runs at twice the speed of the base model.\'}', metadata={})]


 We are building a memory game where a description is displayed for a few seconds and later on, questions on what is shown comes up for the user to remember.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli\', \'api_call\': "Wav2Vec2ForCTC.from_pretrained(\'jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'https://github.com/neonbjb/ocotillo\', \'performance\': {\'dataset\': \'librispeech validation set\', \'accuracy\': \'4.45%\'}, \'description\': \'This checkpoint is a wav2vec2-large model that is useful for generating transcriptions with punctuation. It is intended for use in building transcriptions for TTS models, where punctuation is very important for prosody. This model was created by fine-tuning the facebook/wav2vec2-large-robust-ft-libri-960h checkpoint on the libritts and voxpopuli datasets with a new vocabulary that includes punctuation.\'}', metadata={})]


 We are a medical research team working on a COVID-19 project. We need assistance in question answering related to the pandemic and related research papers.\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={})]


 The customer is writing a book about the solar system and just needs the answer to a question: How long does it take for Mars to orbit the sun?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'DialogLED-base-16384\', \'api_call\': "LEDForConditionalGeneration.from_pretrained(\'MingZhong/DialogLED-base-16384\')", \'api_arguments\': \'input_text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'arxiv\', \'accuracy\': \'2109.02492\'}, \'description\': \'DialogLED is a pre-trained model for long dialogue understanding and summarization. It builds on the Longformer-Encoder-Decoder (LED) architecture and uses window-based denoising as the pre-training task on a large amount of long dialogue data for further training. Here is a base version of DialogLED, the input length is limited to 16,384 in the pre-training phase.\'}', metadata={})]


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


 The company develops a digital assistant that can answer questions about software products. Implement a feature to provide answers to user questions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face\', \'functionality\': \'Visual Question Answering\', \'api_name\': \'JosephusCheung/GuanacoVQAOnConsumerHardware\', \'api_call\': "pipeline(\'visual-question-answering\', model=\'JosephusCheung/GuanacoVQAOnConsumerHardware\')", \'api_arguments\': {\'model\': \'JosephusCheung/GuanacoVQAOnConsumerHardware\', \'tokenizer\': \'JosephusCheung/GuanacoVQAOnConsumerHardware\'}, \'python_environment_requirements\': {\'transformers\': \'latest\', \'torch\': \'latest\'}, \'example_code\': \'vqa(image_path, question)\', \'performance\': {\'dataset\': \'JosephusCheung/GuanacoVQADataset\', \'accuracy\': \'unknown\'}, \'description\': \'A Visual Question Answering model trained on the GuanacoVQADataset, designed to work on consumer hardware like Colab Free T4 GPU. The model can be used to answer questions about images.\'}', metadata={})]


 I'm talking to a new person online. Can this API help keep me safe by filtering out any inappropriate messages they send me?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


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


 I am reading a book and, whenever I engage with an interesting topic, I write a short sentence summarizing that topic. I would like to have an assistant that, given a sentence, automatically classifies that topic among 'technology', 'literature', and 'science'.\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={})]


 Determine if one Russian sentence logically contradicts the information provided by another Russian sentence.\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={})]


 The assignment is to develop a software for translating sentences from Spanish to English at a near-human performance.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'facebook/textless_sm_en_fr\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/textless_sm_en_fr\')", \'api_arguments\': [\'input_file\'], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is a speech-to-speech translation model trained by Facebook. It is designed for translating English speech to French speech.\'}', metadata={})]


 I am an author. I have a new book that needs a summary. I want the summary to resemble the style of SparkNotes.\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={})]


 An editor wants to summarize his article in French.\n###Input: \"Selon un rapport r\u00e9cent, les constructeurs automobiles pr\u00e9voient d'acc\u00e9l\u00e9rer la production de voitures \u00e9lectriques et de r\u00e9duire la production de voitures \u00e0 moteur \u00e0 combustion interne. Les constructeurs pr\u00e9voient de consacrer davantage de ressources \u00e0 la recherche et au d\u00e9veloppement de technologies pour am\u00e9liorer l'efficacit\u00e9 des batteries, r\u00e9duire les co\u00fbts de production et \u00e9tendre le r\u00e9seau de recharge. Les gouvernements du monde entier continuent de soutenir l'adoption de la mobilit\u00e9 \u00e9lectrique \u00e0 travers des incitations financi\u00e8res et des objectifs ambitieux de r\u00e9duction des \u00e9missions de CO2.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'Jean-Baptiste/camembert-ner\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'Jean-Baptiste/camembert-ner\')", \'api_arguments\': {\'model\': \'model\', \'tokenizer\': \'tokenizer\', \'aggregation_strategy\': \'simple\'}, \'python_environment_requirements\': {\'transformers\': \'AutoTokenizer, AutoModelForTokenClassification, pipeline\'}, \'example_code\': "from transformers import AutoTokenizer, AutoModelForTokenClassification\\ntokenizer = AutoTokenizer.from_pretrained(Jean-Baptiste/camembert-ner)\\nmodel = AutoModelForTokenClassification.from_pretrained(Jean-Baptiste/camembert-ner)\\n\\nfrom transformers import pipeline\\nnlp = pipeline(\'ner\', model=model, tokenizer=tokenizer, aggregation_strategy=simple)\\nnlp(Apple est cre le 1er avril 1976 dans le garage de la maison d\'enfance de Steve Jobs  Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constitue sous forme de socit le 3 janvier 1977  l\'origine sous le nom d\'Apple Computer, mais pour ses 30 ans et pour reflter la diversification de ses produits, le mot « computer  est retir le 9 janvier 2015.)", \'performance\': {\'dataset\': \'wikiner-fr\', \'accuracy\': {\'overall_f1\': 0.8914, \'PER_f1\': 0.9483, \'ORG_f1\': 0.8181, \'LOC_f1\': 0.8955, \'MISC_f1\': 0.8146}}, \'description\': \'camembert-ner is a Named Entity Recognition (NER) model fine-tuned from camemBERT on the wikiner-fr dataset. It can recognize entities such as persons, organizations, locations, and miscellaneous entities.\'}', metadata={})]


 Design a chatbot for a school website, which can answer queries related to admissions, classes, teachers, and extracurriculars.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'facebook/blenderbot-1B-distill\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'facebook/blenderbot-1B-distill\')", \'api_arguments\': {\'input_message\': \'string\'}, \'python_environment_requirements\': {\'library\': \'transformers\', \'version\': \'latest\'}, \'example_code\': "inputs = tokenizer(\'Hello, how are you?\', return_tensors=\'pt\'); outputs = model.generate(inputs[\'input_ids\']); decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)", \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not mentioned\'}, \'description\': \'BlenderBot-1B is a large-scale open-domain chatbot model that can engage in conversations, ask and answer questions, and display knowledge, empathy, and personality. This distilled version is smaller and faster than the original 9.4B parameter model, making it more accessible for use.\'}', metadata={})]


 We have a long article, and we want to generate a summary to understand the main points of the 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={})]


 We need to prepare some sample conversations featuring frequently asked questions for helping customers with our products.\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={})]


 Can you give me advice on how to choose the best video game for me?\n###Input: {\"instruction\": \"what is the best way to choose a video game?\", \"knowledge\": \"Some factors to consider when choosing a video game are personal preferences, genre, graphics, gameplay, storyline, platform, and reviews from other players or gaming websites.\", \"dialog\": [\"What type of video games do you prefer playing?\", \"I enjoy action-adventure games and a decent storyline.\"]}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'camenduru/text2-video-zero\', \'api_call\': "pipeline(\'text-to-video\', model=\'camenduru/text2-video-zero\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is used for generating videos from text inputs. It is based on the Hugging Face framework and can be used with the transformers library. The model is trained on a variety of text and video datasets, and can be used for tasks such as video summarization, video generation from text prompts, and more.\'}', metadata={})]


 Write a script to translate the following French sentence into English: \"Je t\u2019aime.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'text-generation\', \'api_name\': \'satvikag/chatbot\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'output-small\')", \'api_arguments\': {\'tokenizer\': "AutoTokenizer.from_pretrained(\'microsoft/DialoGPT-small\')", \'model\': "AutoModelWithLMHead.from_pretrained(\'output-small\')"}, \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': "for step in range(100):\\n  new_user_input_ids = tokenizer.encode(input(\'>> User:\') + tokenizer.eos_token, return_tensors=\'pt\')\\n  bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\\n  chat_history_ids = model.generate(bot_input_ids, max_length=500, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature = 0.8)\\n  print(\'AI: {}\'.format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))", \'performance\': {\'dataset\': \'Kaggle game script dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'DialoGPT Trained on the Speech of a Game Character, Joshua from The World Ends With You.\'}', metadata={})]


 Your company is developing a chatbot and requires accurate summaries of lengthy dialogues without losing context.\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={})]


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


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


 I need a French language model to help me fill in the blanks in given 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={})]


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


 I want to create a playlist of similar songs based on lyrics. How can I compare the sentences and find similar ones?\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\u2019m putting together a dating site where users can submit questions they'd like the matching algorithm to ask. I want to suggest questions like the ones they have already submitted. Can you provide me with a model to do that?\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'm working on a phonebot, and I need the bot to be able to read a sensitive warning message to the users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Masked Language Modeling\', \'api_name\': \'bert-base-multilingual-cased\', \'api_call\': "pipeline(\'fill-mask\', model=\'bert-base-multilingual-cased\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nunmasker = pipeline(\'fill-mask\', model=\'bert-base-multilingual-cased\')\\nunmasker(Hello I\'m a [MASK] model.)", \'performance\': {\'dataset\': \'wikipedia\', \'accuracy\': \'Not provided\'}, \'description\': \'BERT multilingual base model (cased) is pretrained on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective. The model is case sensitive and can be used for masked language modeling or next sentence prediction. It is intended to be fine-tuned on a downstream task, such as sequence classification, token classification, or question answering.\'}', metadata={})]


 Our customer wants to have this Chinese text \"<Chinese_text>\" read aloud as an audio file in female voice.\n###Input: \"<Chinese_text>\": \"\u4f60\u597d\uff0c\u6b22\u8fce\u6765\u5230\u6570\u5b57\u4e16\u754c\u3002\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'pyannote.audio\', \'functionality\': \'overlapped-speech-detection\', \'api_name\': \'pyannote/overlapped-speech-detection\', \'api_call\': "pipeline.from_pretrained(\'pyannote/overlapped-speech-detection\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote.audio 2.1\'], \'example_code\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/overlapped-speech-detection, use_auth_token=ACCESS_TOKEN_GOES_HERE)\\noutput = pipeline(audio.wav)\\nfor speech in output.get_timeline().support():\\n  # two or more speakers are active between speech.start and speech.end\\n  ...\', \'performance\': {\'dataset\': \'ami\', \'accuracy\': None}, \'description\': \'Automatic overlapped speech detection using pyannote.audio framework. The model detects when two or more speakers are active in an audio file.\'}', metadata={})]


 Develop a text-to-speech model for our mobile app to read news articles for our users.\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={})]


 You are an Indian in Telugu pandit teaching kids how to pronounce conventional holy prayers.. Teach me mantras in Telugu synthesized by human like voice pronunciation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Telugu_Male_TTS\', \'api_call\': "pipeline(\'text-to-speech\', model=\'SYSPIN/Telugu_Male_TTS\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Telugu Male Text-to-Speech model using the ESPnet framework, provided by Hugging Face.\'}', metadata={})]


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


 Create Japanese audio from the following text: \"\u3053\u3093\u306b\u3061\u306f\u3001\u79c1\u305f\u3061\u306f\u3042\u306a\u305f\u306e\u52a9\u3051\u304c\u5fc5\u8981\u3067\u3059\u3002\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Feature Extraction\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'facebook/dragon-plus-context-encoder\', \'api_call\': "AutoModel.from_pretrained(\'facebook/dragon-plus-context-encoder\')", \'api_arguments\': [\'pretrained\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': "import torch\\nfrom transformers import AutoTokenizer, AutoModel\\ntokenizer = AutoTokenizer.from_pretrained(\'facebook/dragon-plus-query-encoder\')\\nquery_encoder = AutoModel.from_pretrained(\'facebook/dragon-plus-query-encoder\')\\ncontext_encoder = AutoModel.from_pretrained(\'facebook/dragon-plus-context-encoder\')\\nquery = \'Where was Marie Curie born?\'\\ncontexts = [\\n  \'Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.\',\\n  \'Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace.\'\\n]\\nquery_input = tokenizer(query, return_tensors=\'pt\')\\nctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors=\'pt\')\\nquery_emb = query_encoder(query_input).last_hidden_state[:, 0, :]\\nctx_emb = context_encoder(ctx_input).last_hidden_state[:, 0, :]\\nscore1 = query_emb @ ctx_emb[0]\\nscore2 = query_emb @ ctx_emb[1]", \'performance\': {\'dataset\': \'MS MARCO\', \'accuracy\': 39.0}, \'description\': \'DRAGON+ is a BERT-base sized dense retriever initialized from RetroMAE and further trained on the data augmented from MS MARCO corpus, following the approach described in How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval. The associated GitHub repository is available here https://github.com/facebookresearch/dpr-scale/tree/main/dragon. We use asymmetric dual encoder, with two distinctly parameterized encoders.\'}', metadata={})]


 We need to create automated transcripts from recorded podcasts that include punctuation for better readability.\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={})]


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


 I want to use speech enhancement for an audio call of a coworker.\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={})]


 How do I separate the speakers from an audio file using the pre-trained ConvTasNet_Libri2Mix_sepclean_8k model from Hugging Face?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'pyannote.audio\', \'functionality\': \'overlapped-speech-detection\', \'api_name\': \'pyannote/overlapped-speech-detection\', \'api_call\': "pipeline.from_pretrained(\'pyannote/overlapped-speech-detection\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote.audio 2.1\'], \'example_code\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/overlapped-speech-detection, use_auth_token=ACCESS_TOKEN_GOES_HERE)\\noutput = pipeline(audio.wav)\\nfor speech in output.get_timeline().support():\\n  # two or more speakers are active between speech.start and speech.end\\n  ...\', \'performance\': {\'dataset\': \'ami\', \'accuracy\': None}, \'description\': \'Automatic overlapped speech detection using pyannote.audio framework. The model detects when two or more speakers are active in an audio file.\'}', metadata={})]


 A travel app we work on can translate language of a guide in real-time. We are targeting Spanish-speaking tourists.\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={})]


 Design an audio description system for a user who needs help translating spoken English audio to spoken Hokkien audio.\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={})]


 Implement a method to perform speech-to-speech translation between Hokkien and English using the xm_transformer_s2ut_hk-en model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'audio\', \'api_name\': \'textless_sm_cs_en\', \'api_call\': "Wav2Vec2Model.from_pretrained(cached_download(\'https://huggingface.co/facebook/textless_sm_cs_en/resolve/main/model.pt\'))", \'api_arguments\': [], \'python_environment_requirements\': [\'fairseq\', \'huggingface_hub\'], \'example_code\': "from fairseq.models.wav2vec.wav2vec2_asr import Wav2Vec2Model\\nfrom huggingface_hub import cached_download\\n\\nmodel = Wav2Vec2Model.from_pretrained(cached_download(\'https://huggingface.co/facebook/textless_sm_cs_en/resolve/main/model.pt\'))", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A speech-to-speech translation model for converting between languages without using text as an intermediate representation. This model is designed for the task of audio-to-audio translation.\'}', metadata={})]


 A company wants to analyze the sentiment of the customer feedback in their Spanish-speaking call center. Generate a script for this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'financial-sentiment-analysis\', \'api_name\': \'ProsusAI/finbert\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'ProsusAI/finbert\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline; classifier = pipeline(\'sentiment-analysis\', model=\'ProsusAI/finbert\'); classifier(\'your_text_here\')", \'performance\': {\'dataset\': \'Financial PhraseBank\', \'accuracy\': \'Not provided\'}, \'description\': \'FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning.\'}', metadata={})]


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


 A transcription service wants to identify which speaker said which words in an audio file. Help them with this task.\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={})]


 I am running a chain of wine stores and I want to categorize wines and recommend them based on their quality.\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 own an e-commerce platform and I need to predict which customers will make a purchase based on their browsing behavior.\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={})]


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


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


 A new city planning company wants to estimate carbon emissions for different types of buildings to provide sustainable living solutions. We need a model to classify the carbon emissions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'pcoloc/autotrain-600-dragino-1839063122\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'pcoloc/autotrain-data-600-dragino\', \'accuracy\': {\'Loss\': 93.595, \'R2\': 0.502, \'MSE\': 8760.052, \'MAE\': 77.527, \'RMSLE\': 0.445}}, \'description\': \'This model is trained to perform single column regression on carbon emissions data using the AutoTrain framework. It predicts CO2 emissions in grams given the input data.\'}', metadata={})]


 We want to evaluate the carbon footprint of a construction project based on the tabular data of material consumption.\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 is an environmental consultancy firm. Determine the carbon emissions of different facilities based on the provided data.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'pcoloc/autotrain-600-dragino-1839063122\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'pcoloc/autotrain-data-600-dragino\', \'accuracy\': {\'Loss\': 93.595, \'R2\': 0.502, \'MSE\': 8760.052, \'MAE\': 77.527, \'RMSLE\': 0.445}}, \'description\': \'This model is trained to perform single column regression on carbon emissions data using the AutoTrain framework. It predicts CO2 emissions in grams given the input data.\'}', metadata={})]