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


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


 In our new app, we are building a feature that recommends books in different languages. To do this, first, we need to translate the book title and details from English to French. Help us to decide the best translation model to use here.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'financial-sentiment-analysis\', \'api_name\': \'yiyanghkust/finbert-tone\', \'api_call\': "BertForSequenceClassification.from_pretrained(\'yiyanghkust/finbert-tone\',num_labels=3)", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import BertTokenizer, BertForSequenceClassification\\nfrom transformers import pipeline\\nfinbert = BertForSequenceClassification.from_pretrained(\'yiyanghkust/finbert-tone\',num_labels=3)\\ntokenizer = BertTokenizer.from_pretrained(\'yiyanghkust/finbert-tone\')\\nnlp = pipeline(sentiment-analysis, model=finbert, tokenizer=tokenizer)\\nsentences = [there is a shortage of capital, and we need extra financing,\\n growth is strong and we have plenty of liquidity,\\n there are doubts about our finances,\\n profits are flat]\\nresults = nlp(sentences)\\nprint(results)", \'performance\': {\'dataset\': \'10,000 manually annotated sentences from analyst reports\', \'accuracy\': \'superior performance on financial tone analysis task\'}, \'description\': \'FinBERT is a BERT model pre-trained on financial communication text. It is trained on the following three financial communication corpus: Corporate Reports 10-K & 10-Q, Earnings Call Transcripts, and Analyst Reports. This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task.\'}', metadata={})]


 Our multinational company is dealing with a French client. Please help us communicate in French by translating an English sentence into French.\n###Input: \"Hello, how are you?\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 We want to communicate product information to online customers. Translate the information from English to French.\n###Input: \"Introducing the new eco-friendly water bottle made of high-quality stainless steel with double-wall insulation to keep your drinks cool for 24 hours or hot for 12 hours.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'wav2vec2-base-superb-sv\', \'api_call\': "AutoModelForAudioXVector.from_pretrained(\'anton-l/wav2vec2-base-superb-sv\')", \'api_arguments\': \'anton-l/wav2vec2-base-superb-sv\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'from transformers import AutoProcessor, AutoModelForAudioXVector\\nprocessor = AutoProcessor.from_pretrained(anton-l/wav2vec2-base-superb-sv)\\nmodel = AutoModelForAudioXVector.from_pretrained(anton-l/wav2vec2-base-superb-sv)\', \'performance\': {\'dataset\': \'superb\', \'accuracy\': \'More information needed\'}, \'description\': "This is a ported version of S3PRL\'s Wav2Vec2 for the SUPERB Speaker Verification task. The base model is wav2vec2-large-lv60, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to SUPERB: Speech processing Universal PERformance Benchmark."}', metadata={})]


 Our Customer is a Swedish travel magazine with an English version. Translate this Swedish text to English for them: \"Stockholm \u00e4r Sveriges huvudstad och st\u00f6rsta stad. Den har en rik historia och erbjuder m\u00e5nga kulturella och historiska sev\u00e4rdheter.\"\n###Input: \"Stockholm \u00e4r Sveriges huvudstad och st\u00f6rsta stad. Den har en rik historia och erbjuder m\u00e5nga kulturella och historiska sev\u00e4rdheter.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation\', \'api_name\': \'opus-mt-sv-en\', \'api_call\': "AutoModel.from_pretrained(\'Helsinki-NLP/opus-mt-sv-en\').", \'api_arguments\': [\'inputs\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Tatoeba.sv.en\', \'accuracy\': \'BLEU: 64.5, chr-F: 0.763\'}, \'description\': \'A Swedish to English translation model trained on the OPUS dataset using the transformer-align architecture. The model is pre-processed with normalization and SentencePiece.\'}', metadata={})]


 I have written a small story in English about the adventures of a superhero who saves the day from evil villains. I would like to translate it into French so that my friends in France can read it too.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'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={})]


 Create an application that reads news articles and provides a brief summary of the article contents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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 building a platform for developers and want to provide automatic code documentation generation for Python functions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


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


 Use this API to get a suggestion on how to respond to a customer's complaint about the late delivery of their package.\n###Input: {\"instruction\": \"How can I respond to a customer complaint about late delivery?\", \"knowledge\": \"The courier had external delays due to bad winter weather.\", \"dialog\": [\"Customer: My package is late. What's going on?\", \"Support: I apologize for the inconvenience. I'll check what's happening with the package and get back to you.\"]}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'wavymulder/Analog-Diffusion\', \'api_call\': "pipeline(\'text-to-image\', model=\'wavymulder/Analog-Diffusion\')", \'api_arguments\': [\'prompt\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "text_to_image(\'analog style landscape\')", \'performance\': {\'dataset\': \'analog photographs\', \'accuracy\': \'Not specified\'}, \'description\': "Analog Diffusion is a dreambooth model trained on a diverse set of analog photographs. It can generate images based on text prompts with an analog style. Use the activation token \'analog style\' in your prompt to get the desired output. The model is available on the Hugging Face Inference API and can be used with the transformers library."}', metadata={})]


 A game studio is now creating a story for their new action game, they need a hint for creating the setting of the game.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'financial-summarization-pegasus\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'human-centered-summarization/financial-summarization-pegasus\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration\\nmodel_name = human-centered-summarization/financial-summarization-pegasus\\ntokenizer = PegasusTokenizer.from_pretrained(model_name)\\nmodel = PegasusForConditionalGeneration.from_pretrained(model_name)\\ntext_to_summarize = National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region’s third-largest lender. The entity’s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion of assets.\\ninput_ids = tokenizer(text_to_summarize, return_tensors=pt).input_ids\\noutput = model.generate(input_ids, max_length=32, num_beams=5, early_stopping=True)\\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\', \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 35.206, \'ROUGE-2\': 16.569, \'ROUGE-L\': 30.128, \'ROUGE-LSUM\': 30.171}}, \'description\': \'This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies. It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.\'}', metadata={})]


 We are working on an AI chatbot for customer support and we need our chatbot to generate human-like responses to customers' questions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 Translate the following English sentence to German: \"I have a doctor's appointment tomorrow morning.\"\n###Input: \"I have a doctor's appointment tomorrow morning.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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 am struggling with grammar while writing. Thus, I want to create a grammar correction tool for myself.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Grammar Synthesis\', \'api_name\': \'pszemraj/flan-t5-large-grammar-synthesis\', \'api_call\': "pipeline(\'text2text-generation\', \'pszemraj/flan-t5-large-grammar-synthesis\')", \'api_arguments\': [\'raw_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\n\\ncorrector = pipeline(\\n \'text2text-generation\',\\n \'pszemraj/flan-t5-large-grammar-synthesis\',\\n )\\n\\nraw_text = \'i can has cheezburger\'\\nresults = corrector(raw_text)\\nprint(results)", \'performance\': {\'dataset\': \'jfleg\', \'accuracy\': \'Not provided\'}, \'description\': \'A fine-tuned version of google/flan-t5-large for grammar correction on an expanded version of the JFLEG dataset.\'}', metadata={})]


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


 The publisher has sent us a draft of an article, but some of the words have been masked. We need to identify the masked words.\n###Input: \"<mask> are large, slow-moving reptiles native to the southeastern United States. They are well-adapted to life in <mask>, and they are a common sight in swamps, rivers, and lakes.\" \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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 exploring the Chinese market and we need to communicate effectively with our clients. Help me create a generic Chinese response.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'financial-sentiment-analysis\', \'api_name\': \'yiyanghkust/finbert-tone\', \'api_call\': "BertForSequenceClassification.from_pretrained(\'yiyanghkust/finbert-tone\',num_labels=3)", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import BertTokenizer, BertForSequenceClassification\\nfrom transformers import pipeline\\nfinbert = BertForSequenceClassification.from_pretrained(\'yiyanghkust/finbert-tone\',num_labels=3)\\ntokenizer = BertTokenizer.from_pretrained(\'yiyanghkust/finbert-tone\')\\nnlp = pipeline(sentiment-analysis, model=finbert, tokenizer=tokenizer)\\nsentences = [there is a shortage of capital, and we need extra financing,\\n growth is strong and we have plenty of liquidity,\\n there are doubts about our finances,\\n profits are flat]\\nresults = nlp(sentences)\\nprint(results)", \'performance\': {\'dataset\': \'10,000 manually annotated sentences from analyst reports\', \'accuracy\': \'superior performance on financial tone analysis task\'}, \'description\': \'FinBERT is a BERT model pre-trained on financial communication text. It is trained on the following three financial communication corpus: Corporate Reports 10-K & 10-Q, Earnings Call Transcripts, and Analyst Reports. This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task.\'}', metadata={})]


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


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


 We are working on creating an audiobook. Convert this text: \"The sun was shining brightly, and the birds were singing sweetly\" into speech.\n###Input: The sun was shining brightly, and the birds were singing sweetly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'facebook/mask2former-swin-large-coco-panoptic\', \'api_call\': "Mask2FormerForUniversalSegmentation.from_pretrained(\'facebook/mask2former-swin-large-coco-panoptic\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'requests\', \'torch\', \'PIL\', \'transformers\'], \'example_code\': \'processor = AutoImageProcessor.from_pretrained(facebook/mask2former-swin-large-coco-panoptic)\\nmodel = Mask2FormerForUniversalSegmentation.from_pretrained(facebook/mask2former-swin-large-coco-panoptic)\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = processor(images=image, return_tensors=pt)\\nwith torch.no_grad():\\n    outputs = model(**inputs)\\nclass_queries_logits = outputs.class_queries_logits\\nmasks_queries_logits = outputs.masks_queries_logits\\nresult = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]\\npredicted_panoptic_map = result[segmentation]\', \'performance\': {\'dataset\': \'COCO\', \'accuracy\': \'Not provided\'}, \'description\': \'Mask2Former model trained on COCO panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency.\'}', metadata={})]


 A researcher needs information about how to use the Whisper ASR model to transcribe and analyze the sentiment of an audio file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 I recently interviewed a person in Japanese. I need to transcribe the interview in order to find relevant quotes for my article.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text-to-Text Generation\', \'api_name\': \'philschmid/bart-large-cnn-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/bart-large-cnn-samsum\')", \'api_arguments\': {\'model\': \'philschmid/bart-large-cnn-samsum\'}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/bart-large-cnn-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\': {\'eval_rouge1\': 42.621, \'eval_rouge2\': 21.9825, \'eval_rougeL\': 33.034, \'eval_rougeLsum\': 39.6783, \'test_rouge1\': 41.3174, \'test_rouge2\': 20.8716, \'test_rougeL\': 32.1337, \'test_rougeLsum\': 38.4149}}, \'description\': \'philschmid/bart-large-cnn-samsum is a BART-based model trained for text summarization on the SAMSum dataset. It can be used to generate abstractive summaries of conversations.\'}', metadata={})]


 Our startup produces a podcast editing software. We need to convert speech to text automatically for transcription purposes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transcription and Translation\', \'api_name\': \'openai/whisper-tiny\', \'api_call\': "WhisperForConditionalGeneration.from_pretrained(\'openai/whisper-tiny\')", \'api_arguments\': {\'forced_decoder_ids\': "WhisperProcessor.get_decoder_prompt_ids(language=\'english\', task=\'transcribe\')"}, \'python_environment_requirements\': [\'transformers\', \'datasets\'], \'example_code\': [\'from transformers import WhisperProcessor, WhisperForConditionalGeneration\', \'from datasets import load_dataset\', "processor = WhisperProcessor.from_pretrained(\'openai/whisper-tiny\')", "model = WhisperForConditionalGeneration.from_pretrained(\'openai/whisper-tiny\')", \'model.config.forced_decoder_ids = None\', "ds = load_dataset(\'hf-internal-testing/librispeech_asr_dummy\', \'clean\', split=\'validation\')", "sample = ds[0][\'audio\']", "input_features = processor(sample[\'array\'], sampling_rate=sample[\'sampling_rate\'], return_tensors=\'pt\').input_features", \'predicted_ids = model.generate(input_features)\', \'transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\'], \'performance\': {\'dataset\': \'LibriSpeech (clean)\', \'accuracy\': 7.54}, \'description\': \'Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. It is a Transformer-based encoder-decoder model that can be used for transcription and translation tasks.\'}', metadata={})]


 Lately, the quality of our audio has degraded due to background noise, and our robot needs to process speech to understand voice commands. Please help.\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 a recommendation engine for a podcast platform that enhances the audio quality of low-quality recordings before recommending it to users seeking high-quality content.\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={})]


 Create a system that translates and synthesizes speech from one language to another using the given model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Program Synthesis\', \'api_name\': \'Salesforce/codegen-350M-multi\', \'api_call\': "AutoTokenizer.from_pretrained(\'Salesforce/codegen-350M-multi\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelForCausalLM\\ntokenizer = AutoTokenizer.from_pretrained(Salesforce/codegen-350M-multi)\\nmodel = AutoModelForCausalLM.from_pretrained(Salesforce/codegen-350M-multi)\\ntext = def hello_world():\\ninput_ids = tokenizer(text, return_tensors=pt).input_ids\\ngenerated_ids = model.generate(input_ids, max_length=128)\\nprint(tokenizer.decode(generated_ids[0], skip_special_tokens=True))\', \'performance\': {\'dataset\': \'HumanEval and MTPB\', \'accuracy\': \'Refer to the paper for accuracy details\'}, \'description\': \'CodeGen is a family of autoregressive language models for program synthesis. The checkpoint included in this repository is denoted as CodeGen-Multi 350M, where Multi means the model is initialized with CodeGen-NL 350M and further pre-trained on a dataset of multiple programming languages, and 350M refers to the number of trainable parameters. The model is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. It is best at program synthesis, generating executable code given English prompts, and can complete partially-generated code as well.\'}', metadata={})]


 I have a single channel audio recording containing the voices of two speakers very close, and I would like to separate the voices in this audio recording.\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={})]


 We are building an app to improve people's public speaking skills. We want to analyze their emotional speech as feedback.\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 psychology company is building a revolutionary means to detect emotions of its clients. Help them create a system to understand emotions from spoken words.\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={})]


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


 I am a hearing impaired individual who relies on technology to lead a qualitative life. Develop an application that measures noise levels in the environment to help me decide if it's suitable for communication.\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={})]


Can you classify the audio clip to determine whether it is silent or contains speech?\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 need to predict digit categories based on some tabular data inputs. I need a pretrained model to use for classification.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text-to-Text Generation\', \'api_name\': \'philschmid/bart-large-cnn-samsum\', \'api_call\': "pipeline(\'summarization\', model=\'philschmid/bart-large-cnn-samsum\')", \'api_arguments\': {\'model\': \'philschmid/bart-large-cnn-samsum\'}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(summarization, model=philschmid/bart-large-cnn-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\': {\'eval_rouge1\': 42.621, \'eval_rouge2\': 21.9825, \'eval_rougeL\': 33.034, \'eval_rougeLsum\': 39.6783, \'test_rouge1\': 41.3174, \'test_rouge2\': 20.8716, \'test_rougeL\': 32.1337, \'test_rougeLsum\': 38.4149}}, \'description\': \'philschmid/bart-large-cnn-samsum is a BART-based model trained for text summarization on the SAMSum dataset. It can be used to generate abstractive summaries of conversations.\'}', metadata={})]


 In surveillance operations, transcripts are used to turn the audio feed into chunks such that after processing transcripts there are no speakers talking over each other in the output.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Keras\', \'functionality\': \'Structured data learning with TabTransformer\', \'api_name\': \'keras-io/tab_transformer\', \'api_call\': \'TabTransformer.from_config()\', \'api_arguments\': \'N/A\', \'python_environment_requirements\': \'Hugging Face\', \'example_code\': \'N/A\', \'performance\': {\'dataset\': \'United States Census Income Dataset\', \'accuracy\': \'N/A\'}, \'description\': "This model uses self-attention based Transformers structure followed by multiple feed forward layers to serve supervised and semi-supervised learning tasks on tabular data. The model\'s inputs can contain both numerical and categorical features. Categorical features are encoded into embedding vectors before being fed into a stack of Transformer blocks. The contextual embeddings of the categorical features after the final Transformer layer are concatenated with the input numerical features and fed into a final MLP block. A SoftMax function is applied at the end of the model."}', metadata={})]


 I work at GreenTech, a startup that provides eco-friendly solutions, and need to determine if a given set of input data will result in high carbon emissions or not.\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={})]


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