 We are developing a medical records analysis software that automatically recognizes biomedical entities from physicians' case reports.\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={})]


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


 I am working on a Chinese language project and I need to tokenize the sentences for better processing.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'shibing624/text2vec-base-chinese\', \'api_call\': "SentenceModel(\'shibing624/text2vec-base-chinese\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': [\'text2vec\', \'transformers\'], \'example_code\': "from text2vec import SentenceModel\\nsentences = [\'\', \'\']\\nmodel = SentenceModel(\'shibing624/text2vec-base-chinese\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': [{\'name\': \'ATEC\', \'accuracy\': \'31.93\'}, {\'name\': \'BQ\', \'accuracy\': \'42.67\'}, {\'name\': \'LCQMC\', \'accuracy\': \'70.16\'}, {\'name\': \'PAWSX\', \'accuracy\': \'17.21\'}, {\'name\': \'STS-B\', \'accuracy\': \'79.30\'}]}, \'description\': \'This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese. It maps sentences to a 768 dimensional dense vector space and can be used for tasks like sentence embeddings, text matching or semantic search.\'}', metadata={})]


 I need a system that extracts all the well-known named entities such as person names, locations, and organizations from news articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'dslim/bert-base-NER-uncased\', \'api_call\': "pipeline(\'ner\', model=\'dslim/bert-base-NER-uncased\')", \'api_arguments\': {}, \'python_environment_requirements\': {\'transformers\': \'>=4.0.0\'}, \'example_code\': "nlp(\'My name is John and I live in New York.\')", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A pretrained BERT model for Named Entity Recognition (NER) on uncased text. It can be used to extract entities such as person names, locations, and organizations from text.\'}', metadata={})]


 I have a list of Olympic Game host cities and their corresponding years. I want to know which year the games were held in Beijing.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face\', \'functionality\': \'Visual Question Answering\', \'api_name\': \'sheldonxxxx/OFA_model_weights\', \'api_call\': "AutoModel.from_pretrained(\'sheldonxxxx/OFA_model_weights\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This is an unoffical mirror of the model weights for use with https://github.com/OFA-Sys/OFA. The original link is too slow when downloading from outside of China.\'}', metadata={})]


 I have a table with data about different types of bards and their magical abilities. Let me see if I can find the best bard given the data in the table.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'table-question-answering-tapas\', \'api_call\': "pipeline(\'table-question-answering\', model=\'Meena/table-question-answering-tapas\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'This model can be loaded on the Inference API on-demand.\', \'performance\': {\'dataset\': [{\'name\': \'SQA (Sequential Question Answering by Microsoft)\', \'accuracy\': None}, {\'name\': \'WTQ (Wiki Table Questions by Stanford University)\', \'accuracy\': None}, {\'name\': \'WikiSQL (by Salesforce)\', \'accuracy\': None}]}, \'description\': \'TAPAS, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refuted by the contents of a table. It is a BERT-based model specifically designed (and pre-trained) for answering questions about tabular data. TAPAS uses relative position embeddings and has 7 token types that encode tabular structure. It is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising millions of tables from English Wikipedia and corresponding texts.\'}', metadata={})]


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


 I am a lawyer, I have a lot of text. I need a system which reads context and answers the questions based on the context.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'castorini/doc2query-t5-base-msmarco\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'castorini/doc2query-t5-base-msmarco\')", \'api_arguments\': \'text, max_length\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'MS MARCO\', \'accuracy\': \'Not specified\'}, \'description\': \'A T5 model trained on the MS MARCO dataset for generating queries from documents.\'}', metadata={})]


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


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


 Our customer is a Spanish travel agency. They need to classify customer reviews into categories such as 'travel', 'cooking', and 'dancing'.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


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


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


 We have a user comment about a product, and we want to infer if the sentiment of the comment is positive or negative.\n###Input: \"I recently purchased this product and it completely exceeded my expectations! The build quality is top-notch, and I've already recommended it to several friends.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'bert-base-multilingual-uncased-sentiment\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'nlptown/bert-base-multilingual-uncased-sentiment\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "result = sentiment_pipeline(\'I love this product!\')", \'performance\': {\'dataset\': [{\'language\': \'English\', \'accuracy\': {\'exact\': \'67%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Dutch\', \'accuracy\': {\'exact\': \'57%\', \'off-by-1\': \'93%\'}}, {\'language\': \'German\', \'accuracy\': {\'exact\': \'61%\', \'off-by-1\': \'94%\'}}, {\'language\': \'French\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'94%\'}}, {\'language\': \'Italian\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Spanish\', \'accuracy\': {\'exact\': \'58%\', \'off-by-1\': \'95%\'}}]}, \'description\': \'This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).\'}', metadata={})]


 Extract a conclusion from the following text: \"Studies have been shown that owning a dog is good for you. Having a dog can help decrease stress levels, improve your mood, and increase physical activity.\"\n###Input: Studies have been shown that owning a dog is good for you. Having a dog can help decrease stress levels, improve your mood, and increase physical activity.\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={})]


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


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


 My friend wrote me an English text about the upcoming holiday. I need to translate it to Arabic so I can share it with my family.\n###Input: \"My friend is planning a holiday trip for our families. He found a beautiful place with a beach, swimming pool, and a wide range of outdoor activities for kids. There's also a famous seafood restaurant nearby! I think our families will have a great time together.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-ar\', \'api_call\': "pipeline(\'translation_en_to_ar\', model=\'Helsinki-NLP/opus-mt-en-ar\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_en_to_ar\', model=\'Helsinki-NLP/opus-mt-en-ar\')\\ntranslated_text = translation(\'Hello World\')", \'performance\': {\'dataset\': \'Tatoeba-test.eng.ara\', \'accuracy\': {\'BLEU\': 14.0, \'chr-F\': 0.437}}, \'description\': "A Hugging Face Transformers model for English to Arabic translation, trained on the Tatoeba dataset. It uses a transformer architecture and requires a sentence initial language token in the form of \'>>id<<\' (id = valid target language ID)."}', metadata={})]


 We received a customer feedback document which is very lengthy. We need a summarization of it.\n###Input: The customer support service was excellent. All our concerns were attended to promptly by the friendly and knowledgeable staff. The user interface, however, could use some improvement. Navigating through the platform can be challenging, and it took us quite some time to find the relevant information we needed.\nAdditionally, some of our team members faced technical issues while using the platform, particularly with the integration of third-party tools. We had to reach out to the support team multiple times to resolve these issues. Overall, while we genuinely appreciate your team's assistance, we expect better performance from the platform itself.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'impira/layoutlm-invoices\', \'api_call\': "pipeline(\'question-answering\', model=\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "qa_pipeline(question=\'your question\', context=\'your document context\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'not provided\'}, \'description\': \'This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


 I would like to give a brief overview of our team meeting to my supervisor, so I need a summary of the conversation.\n###Input: \"Anna: In today's meeting, we discussed increasing marketing budget. Tom: I suggested allocating more funds to social media campaigns. Sarah: I proposed focusing on improving SEO. Anna: We agreed on investing in content creation, too. Tom: The team will revise the strategy and present it next week. Sarah: Let's determine new KPIs for evaluating our progress.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'t5-efficient-large-nl36_fine_tune_sum_V2\', \'api_call\': "pipeline(\'summarization\', model=\'Samuel-Fipps/t5-efficient-large-nl36_fine_tune_sum_V2\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': [{\'name\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 54.933, \'ROUGE-2\': 31.797, \'ROUGE-L\': 47.006, \'ROUGE-LSUM\': 51.203, \'loss\': 1.131, \'gen_len\': 23.799}}, {\'name\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 34.406, \'ROUGE-2\': 14.127, \'ROUGE-L\': 24.335, \'ROUGE-LSUM\': 31.658, \'loss\': 2.446, \'gen_len\': 45.928}}]}, \'description\': "A T5-based summarization model trained on the Samsum dataset. This model can be used for text-to-text generation tasks such as summarization without adding \'summarize\' to the start of the input string. It has been fine-tuned for 10K steps with a batch size of 10."}', metadata={})]


 We need to develop a conversational chatbot that can answer users' queries, ask questions, and have a friendly conversation.\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={})]


 My daughter left her laptop logged in, and I found her English essay unfinished. There's a sentence that goes like \"In the story, the antagonist represents the <mask> nature of humanity.\" Can you help me complete her sentence with an appropriate word?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'cl-tohoku/bert-base-japanese-whole-word-masking\', \'api_call\': "AutoModelForMaskedLM.from_pretrained(\'cl-tohoku/bert-base-japanese-whole-word-masking\')", \'api_arguments\': {\'model\': \'cl-tohoku/bert-base-japanese-whole-word-masking\'}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': "fill_mask(\'[MASK]\')", \'performance\': {\'dataset\': \'Japanese Wikipedia\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a BERT model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.\'}', metadata={})]


 We are developing a program to teach French to English speakers. The program should complete a sentence with a missing word in French.\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 have a multilingual document, and there is a missing word in the document. Please help me find out what this missing word might be.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Natural Language Processing Token Classification', 'framework': 'Transformers', 'functionality': 'punctuation prediction', 'api_name': 'oliverguhr/fullstop-punctuation-multilang-large', 'api_call': 'PunctuationModel()', 'api_arguments': ['text'], 'python_environment_requirements': ['pip install deepmultilingualpunctuation'], 'example_code': 'from deepmultilingualpunctuation import PunctuationModel\\nmodel = PunctuationModel()\\ntext = My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller\\nresult = model.restore_punctuation(text)\\nprint(result)', 'performance': {'dataset': 'wmt/europarl', 'EN_accuracy': 0.775, 'DE_accuracy': 0.814, 'FR_accuracy': 0.782, 'IT_accuracy': 0.762}, 'description': 'This model predicts the punctuation of English, Italian, French and German texts. It was developed to restore the punctuation of transcribed spoken language and trained on the Europarl Dataset provided by the SEPP-NLG Shared Task. The model restores the following punctuation markers: ., ,, ?, -, :.'}", metadata={})]


 As a translation company, we are translating messages between co-workers in a multinational company. Translate the message from Hindi to French.\n###Input: \"\u0906\u092a\u0915\u0940 \u092a\u094d\u0930\u0947\u091c\u093c\u091f\u0947\u0936\u0928 \u0915\u093e \u0906\u0927\u093e\u0930 \u0905\u091a\u094d\u091b\u093e \u0925\u093e, \u0932\u0947\u0915\u093f\u0928 \u0921\u0947\u091f\u093e \u0935\u093f\u0936\u094d\u0932\u0947\u0937\u0923 \u092a\u0930 \u0927\u094d\u092f\u093e\u0928 \u0926\u0947\u0928\u093e \u091a\u093e\u0939\u093f\u090f\u0964\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


 Our task is to complete a given sentence with a missing word. The sentence is from an electronic health record.\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={})]


 A Dutch friend asked for help in completing a sentence with a missing word. Can you fill in the blank?\n###Input: \"Het is vandaag erg koud, dus vergeet niet je ___ mee te nemen.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'GroNLP/bert-base-dutch-cased\', \'api_call\': "AutoModel.from_pretrained(\'GroNLP/bert-base-dutch-cased\')", \'api_arguments\': [\'pretrained_model_name_or_path\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModel, TFAutoModel\\ntokenizer = AutoTokenizer.from_pretrained(GroNLP/bert-base-dutch-cased)\\nmodel = AutoModel.from_pretrained(GroNLP/bert-base-dutch-cased)\', \'performance\': {\'dataset\': [{\'name\': \'CoNLL-2002\', \'accuracy\': \'90.24\'}, {\'name\': \'SoNaR-1\', \'accuracy\': \'84.93\'}, {\'name\': \'spaCy UD LassySmall\', \'accuracy\': \'86.10\'}]}, \'description\': \'BERTje is a Dutch pre-trained BERT model developed at the University of Groningen.\'}', metadata={})]


 Our school needs to create fill-in-the-blank quizzes for students. Is it possible to generate a fill-in-the-blank question from the following sentence: \"The cat chased the mouse and then climbed the tree.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 We are building a customer support chatbot that needs to find the most related FAQ for a given customer query.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'Zixtrauce/BaekBot\', \'api_call\': "pipeline(\'conversational\', model=\'Zixtrauce/BaekBot\')", \'api_arguments\': \'input_message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "conversational_pipeline(\'input_message\')", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'BaekBot is a conversational model based on the GPT-2 architecture for text generation. It can be used for generating human-like responses in a chat-like environment.\'}', metadata={})]


 We want to make a system that reads our email messages out loud in different voices.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Marathi_Male_TTS\', \'api_call\': "api.load(\'ESPnet/espnet_model_zoo:SYSPIN/Marathi_Male_TTS\').", \'api_arguments\': [], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Marathi Male Text-to-Speech model using ESPnet framework.\'}', metadata={})]


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


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


 I have a collection of audio recordings from different sources, and I want to convert them into text to make an archive.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'mio/Artoria\', \'api_call\': "pipeline(\'text-to-speech\', model=\'mio/Artoria\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'mio/Artoria\'); tts(\'s\')", \'performance\': {\'dataset\': \'fate\', \'accuracy\': \'Not provided\'}, \'description\': \'This model was trained by mio using fate recipe in espnet. It is a text-to-speech model that can convert text input into speech output.\'}', metadata={})]


 A company developing an application for transcribing customer service calls requires a model that can understand spoken language.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 We are building an audio assistant. Apply noise suppression to our new voice commands.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Voice Activity Detection, Speech-to-Noise Ratio, and C50 Room Acoustics Estimation\', \'api_name\': \'pyannote/brouhaha\', \'api_call\': "Model.from_pretrained(\'pyannote/brouhaha\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote-audio\', \'brouhaha-vad\'], \'example_code\': [\'from pyannote.audio import Model\', \'model = Model.from_pretrained(pyannote/brouhaha, use_auth_token=ACCESS_TOKEN_GOES_HERE)\', \'from pyannote.audio import Inference\', \'inference = Inference(model)\', \'output = inference(audio.wav)\', \'for frame, (vad, snr, c50) in output:\', \'  t = frame.middle\', \'  print(f{t:8.3f} vad={100*vad:.0f}% snr={snr:.0f} c50={c50:.0f})\'], \'performance\': {\'dataset\': \'LibriSpeech, AudioSet, EchoThief, MIT-Acoustical-Reverberation-Scene\', \'accuracy\': \'Not provided\'}, \'description\': \'Brouhaha is a joint voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation model. It is based on the PyTorch framework and uses the pyannote.audio library.\'}', metadata={})]


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


 We have just built an app for hosting podcasts, and we need a speech enhancement model to clean noise from the audio recordings submitted by our users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 My coworker sent me a voice message in Spanish. Translate this audio message to English, so that I can understand it.\n###Input: spanish_voice_message.wav\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 We are developing a virtual assistant and need to integrate a speech enhancement feature in it.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Marathi_Male_TTS\', \'api_call\': "api.load(\'ESPnet/espnet_model_zoo:SYSPIN/Marathi_Male_TTS\').", \'api_arguments\': [], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Marathi Male Text-to-Speech model using ESPnet framework.\'}', metadata={})]


 A kid's educational toy company is including a voice assistant that recognizes simple voice commands from children. They need an example on how to recognize spoken digits.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face\', \'functionality\': \'Voice Activity Detection\', \'api_name\': \'d4data/Indian-voice-cloning\', \'api_call\': "pipeline(\'voice-activity-detection\', model=\'d4data/Indian-voice-cloning\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A model for detecting voice activity in Indian languages.\'}', metadata={})]


 Our company needs to analyze customer phone call recordings and identify specific numbers mentioned by customers during the call.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face\', \'functionality\': \'Voice Activity Detection\', \'api_name\': \'d4data/Indian-voice-cloning\', \'api_call\': "pipeline(\'voice-activity-detection\', model=\'d4data/Indian-voice-cloning\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A model for detecting voice activity in Indian languages.\'}', metadata={})]


 You are building a virtual global tour guide that can identify languages from the audio of people speaking. Use a model to identify which language is being spoken.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 I am working as the head of customer service for a Spanish speaking market. I want to know the sentiment of my customers on their last call with our support agents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 I have a recording of a meeting from which I need to extract the parts where people are talking and eliminate the silent parts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 We are a company producing wine. Based on the chemical properties of our wine data, we need to analyze the quality of our products and determine whether they are good or bad.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Scikit-learn\', \'functionality\': \'Wine Quality classification\', \'api_name\': \'julien-c/wine-quality\', \'api_call\': "joblib.load(cached_download(hf_hub_url(\'julien-c/wine-quality\', \'winequality-red.csv\')))", \'api_arguments\': [\'X\'], \'python_environment_requirements\': [\'huggingface_hub\', \'joblib\', \'pandas\'], \'example_code\': \'from huggingface_hub import hf_hub_url, cached_download\\nimport joblib\\nimport pandas as pd\\nREPO_ID = julien-c/wine-quality\\nFILENAME = sklearn_model.joblib\\nmodel = joblib.load(cached_download(\\n hf_hub_url(REPO_ID, FILENAME)\\n))\\ndata_file = cached_download(\\n hf_hub_url(REPO_ID, winequality-red.csv)\\n)\\nwinedf = pd.read_csv(data_file, sep=;)\\nX = winedf.drop([quality], axis=1)\\nY = winedf[quality]\\nprint(X[:3])\\nlabels = model.predict(X[:3])\\nmodel.score(X, Y)\', \'performance\': {\'dataset\': \'julien-c/wine-quality\', \'accuracy\': 0.6616635397123202}, \'description\': \'A Simple Example of Scikit-learn Pipeline for Wine Quality classification. Inspired by https://towardsdatascience.com/a-simple-example-of-pipeline-in-machine-learning-with-scikit-learn-e726ffbb6976 by Saptashwa Bhattacharyya.\'}', metadata={})]


 Our team is working on a podcast app, and we want to automatically generate transcripts with timestamps for each speaker.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'imdanboy/jets\', \'api_call\': "pipeline(\'text-to-speech\', model=\'imdanboy/jets\')", \'api_arguments\': None, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'imdanboy/jets\'); tts(\'Hello world\')", \'performance\': {\'dataset\': \'ljspeech\', \'accuracy\': None}, \'description\': \'This model was trained by imdanboy using ljspeech recipe in espnet.\'}', metadata={})]


 I have a large dataset with both numerical and categorical features related to customer behavior. I want to classify them into different segments for targeted marketing. How do I use a TabTransformer for this task?\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={})]


 Determine if an employee's annual salary meets or exceeds $50000.\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={})]


 Identify whether a newly-setup chemical plant is exceeding carbon emission limits based on a CSV file containing data collected.\n###Input: data.csv\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'pachi107/autotrain-in-class-test-1780161764\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data.csv\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(data.csv)\\ndata = data[features]\\ndata.columns = [feat_ + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'Validation Metrics\', \'accuracy\': 0.974}, \'description\': \'A binary classification model for predicting CO2 emissions based on tabular data. Trained using AutoTrain with a model ID of 1780161764.\'}', metadata={})]


 I am working in a bank, I want to estimate the mortgage for a given housing using the housing's features.\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={})]


 We are building a robot for hopping in a controlled environment. Train it to perform a hop using Decision Transformers.\n**Input:<noinput>**\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'decision-transformer-gym-hopper-medium\', \'api_call\': "AutoModel.from_pretrained(\'edbeeching/decision-transformer-gym-hopper-medium\')", \'api_arguments\': {\'mean\': [1.311279, -0.08469521, -0.5382719, -0.07201576, 0.04932366, 2.1066856, -0.15017354, 0.00878345, -0.2848186, -0.18540096, -0.28461286], \'std\': [0.17790751, 0.05444621, 0.21297139, 0.14530419, 0.6124444, 0.85174465, 1.4515252, 0.6751696, 1.536239, 1.6160746, 5.6072536]}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': \'See our Blog Post, Colab notebook or Example Script for usage.\', \'performance\': {\'dataset\': \'Gym Hopper environment\', \'accuracy\': \'Not provided\'}, \'description\': \'Decision Transformer model trained on medium trajectories sampled from the Gym Hopper environment.\'}', metadata={})]


 We are building a robot for elderly care. The robot should be able to understand what's happening and take appropriate actions based on the elderly's current activities.\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={})]


 To build a source code recommendation engine, we need to extract features from text and code using Transformer models.\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={})]


 Our team needs to prepare a feature matrix based on the given source code's programming constructs and comments for data analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'tabular regression\', \'api_name\': \'farouk97/autotrain-test7-2644pc-linearregr-38619101723\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'farouk97/autotrain-data-test7-2644pc-linearregr\', \'accuracy\': {\'Loss\': 0.145, \'R2\': 0.0, \'MSE\': 0.021, \'MAE\': 0.099, \'RMSLE\': 0.101}}, \'description\': \'A tabular regression model trained using AutoTrain to predict CO2 emissions (in grams).\'}', metadata={})]


 We are developing an interactive story app that would display a picture of each character as mentioned in the story given to our GPT-3 model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'prompthero/openjourney-v4\', \'api_call\': "pipeline(\'text-to-image\', model=\'prompthero/openjourney-v4\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "generate_image(\'your text here\')", \'performance\': {\'dataset\': \'Midjourney v4 images\', \'accuracy\': \'Not provided\'}, \'description\': \'Openjourney v4 is trained on +124k Midjourney v4 images by PromptHero. It is used for generating images based on text inputs.\'}', metadata={})]


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


 I need to build a solution that generates textual descriptions for images in my database.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Captioning\', \'api_name\': \'microsoft/git-base\', \'api_call\': "pipeline(\'image-to-text\', model=\'microsoft/git-base\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'git_base(image)\', \'performance\': {\'dataset\': [\'COCO\', \'Conceptual Captions (CC3M)\', \'SBU\', \'Visual Genome (VG)\', \'Conceptual Captions (CC12M)\', \'ALT200M\'], \'accuracy\': \'Refer to the paper for evaluation results\'}, \'description\': "GIT (short for GenerativeImage2Text) model, base-sized version. It was introduced in the paper GIT: A Generative Image-to-text Transformer for Vision and Language by Wang et al. and first released in this repository. The model is trained using \'teacher forcing\' on a lot of (image, text) pairs. The goal for the model is simply to predict the next text token, giving the image tokens and previous text tokens. This allows the model to be used for tasks like image and video captioning, visual question answering (VQA) on images and videos, and even image classification (by simply conditioning the model on the image and asking it to generate a class for it in text)."}', metadata={})]


 We are a company managing hotel bookings. We need to answer our customer's questions regarding rental rates from our pricing document.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'impira/layoutlm-invoices\', \'api_call\': "pipeline(\'question-answering\', model=\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "qa_pipeline(question=\'your question\', context=\'your document context\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'not provided\'}, \'description\': \'This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


 Provide me a pipeline for extracting relevant information from an insurance policy document.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'impira/layoutlm-invoices\', \'api_call\': "pipeline(\'question-answering\', model=\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "qa_pipeline(question=\'your question\', context=\'your document context\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'not provided\'}, \'description\': \'This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


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


 We are a team of architects and civil engineers looking to estimate the depth of elements in architectural designs from 2D images of the structures. We want to implement a depth estimation model that will transform these images into depictions of depth.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'dpt-large-redesign\', \'api_call\': "AutoModelForDepthEstimation.from_pretrained(\'nielsr/dpt-large-redesign\')", \'api_arguments\': [], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A depth estimation model based on the DPT architecture.\'}', metadata={})]


 I have an image on my computer named \"eye.jpg\". I want to use a model to predict whether this image indicates diabetic retinopathy.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\', \'api_call\': "pipeline(\'image-classification\', \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\')", \'api_arguments\': {\'model_name\': \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\'}, \'python_environment_requirements\': {\'transformers\': \'4.28.1\', \'pytorch\': \'2.0.0+cu118\', \'datasets\': \'2.11.0\', \'tokenizers\': \'0.13.3\'}, \'example_code\': "from transformers import pipeline\\nimage_classifier = pipeline(\'image-classification\', \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\')\\nresult = image_classifier(\'path/to/image.jpg\')", \'performance\': {\'dataset\': \'None\', \'accuracy\': 0.7744}, \'description\': \'This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It is designed for image classification tasks, specifically for diabetic retinopathy detection.\'}', metadata={})]


 We need to build a model that recognizes objects in images. Build a model using the Vision Transformer (ViT) for this purpose.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'google/vit-base-patch16-384\', \'api_call\': "ViTForImageClassification.from_pretrained(\'google/vit-base-patch16-384\')", \'api_arguments\': {\'pretrained_model_name_or_path\': \'google/vit-base-patch16-384\'}, \'python_environment_requirements\': [\'transformers\', \'PIL\', \'requests\'], \'example_code\': "from transformers import ViTFeatureExtractor, ViTForImageClassification\\nfrom PIL import Image\\nimport requests\\nurl = \'http://images.cocodataset.org/val2017/000000039769.jpg\'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nfeature_extractor = ViTFeatureExtractor.from_pretrained(\'google/vit-base-patch16-384\')\\nmodel = ViTForImageClassification.from_pretrained(\'google/vit-base-patch16-384\')\\ninputs = feature_extractor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(Predicted class:, model.config.id2label[predicted_class_idx])", \'performance\': {\'dataset\': \'ImageNet\', \'accuracy\': \'Refer to tables 2 and 5 of the original paper\'}, \'description\': \'Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. Images 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 absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.\'}', metadata={})]


 My pet store website needs a tool to recognize different dog breeds from user uploaded images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 As a programmer, I want to classify different animal species based on their images. Help me determine the type of species. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 We are a robotics company specializing in object detection for logistics. We need to detect objects in warehouses using an advanced object detection model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-forklift-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-forklift-detection\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.23\', \'ultralytics==8.0.21\'], \'example_code\': [\'from ultralyticsplus import YOLO, render_result\', "model = YOLO(\'keremberke/yolov8m-forklift-detection\')", "model.overrides[\'conf\'] = 0.25", "model.overrides[\'iou\'] = 0.45", "model.overrides[\'agnostic_nms\'] = False", "model.overrides[\'max_det\'] = 1000", "image = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model.predict(image)\', \'print(results[0].boxes)\', \'render = render_result(model=model, image=image, result=results[0])\', \'render.show()\'], \'performance\': {\'dataset\': \'forklift-object-detection\', \'accuracy\': 0.846}, \'description\': \'A YOLOv8 model for detecting forklifts and persons in images.\'}', metadata={})]


 An IoT device collects images from different locations. Create a model to detect objects in these images to analyze the surroundings.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-forklift-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-forklift-detection\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.23\', \'ultralytics==8.0.21\'], \'example_code\': [\'from ultralyticsplus import YOLO, render_result\', "model = YOLO(\'keremberke/yolov8m-forklift-detection\')", "model.overrides[\'conf\'] = 0.25", "model.overrides[\'iou\'] = 0.45", "model.overrides[\'agnostic_nms\'] = False", "model.overrides[\'max_det\'] = 1000", "image = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model.predict(image)\', \'print(results[0].boxes)\', \'render = render_result(model=model, image=image, result=results[0])\', \'render.show()\'], \'performance\': {\'dataset\': \'forklift-object-detection\', \'accuracy\': 0.846}, \'description\': \'A YOLOv8 model for detecting forklifts and persons in images.\'}', metadata={})]


 I'm building a drone surveillance system for detecting airplanes in the sky. What can I use to achieve the required object detection?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-plane-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-plane-detection\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': [\'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\'], \'example_code\': [\'from ultralyticsplus import YOLO, render_result\', "model = YOLO(\'keremberke/yolov8m-plane-detection\')", "model.overrides[\'conf\'] = 0.25", "model.overrides[\'iou\'] = 0.45", "model.overrides[\'agnostic_nms\'] = False", "model.overrides[\'max_det\'] = 1000", "image = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model.predict(image)\', \'print(results[0].boxes)\', \'render = render_result(model=model, image=image, result=results[0])\', \'render.show()\'], \'performance\': {\'dataset\': \'plane-detection\', \'accuracy\': \'0.995\'}, \'description\': \'A YOLOv8 model for plane detection trained on the keremberke/plane-detection dataset. The model is capable of detecting planes in images with high accuracy.\'}', metadata={})]


 We are building an IoT device to monitor apartment corridors for security. Please detect objects in the image and notify if anything abnormal is detected.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8n-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-csgo-player-detection\')", \'api_arguments\': {\'image\': \'URL or local path to image\'}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8n-csgo-player-detection\')\\n\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\n\\nresults = model.predict(image)\\n\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.844}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players with supported labels: [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


 Design an application for a smart drone that can detect and segment objects within the area it's flying.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-plane-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-plane-detection\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': [\'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\'], \'example_code\': [\'from ultralyticsplus import YOLO, render_result\', "model = YOLO(\'keremberke/yolov8m-plane-detection\')", "model.overrides[\'conf\'] = 0.25", "model.overrides[\'iou\'] = 0.45", "model.overrides[\'agnostic_nms\'] = False", "model.overrides[\'max_det\'] = 1000", "image = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model.predict(image)\', \'print(results[0].boxes)\', \'render = render_result(model=model, image=image, result=results[0])\', \'render.show()\'], \'performance\': {\'dataset\': \'plane-detection\', \'accuracy\': \'0.995\'}, \'description\': \'A YOLOv8 model for plane detection trained on the keremberke/plane-detection dataset. The model is capable of detecting planes in images with high accuracy.\'}', metadata={})]


 I want to build a tool to recognize urban landscapes and identify different objects in the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Geolocalization\', \'api_name\': \'geolocal/StreetCLIP\', \'api_call\': "CLIPModel.from_pretrained(\'geolocal/StreetCLIP\')", \'api_arguments\': {\'pretrained_model_name_or_path\': \'geolocal/StreetCLIP\'}, \'python_environment_requirements\': [\'transformers\', \'PIL\', \'requests\'], \'example_code\': \'from PIL import Image\\nimport requests\\nfrom transformers import CLIPProcessor, CLIPModel\\nmodel = CLIPModel.from_pretrained(geolocal/StreetCLIP)\\nprocessor = CLIPProcessor.from_pretrained(geolocal/StreetCLIP)\\nurl = https://huggingface.co/geolocal/StreetCLIP/resolve/main/sanfrancisco.jpeg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nchoices = [San Jose, San Diego, Los Angeles, Las Vegas, San Francisco]\\ninputs = processor(text=choices, images=image, return_tensors=pt, padding=True)\\noutputs = model(**inputs)\\nlogits_per_image = outputs.logits_per_image\\nprobs = logits_per_image.softmax(dim=1)\', \'performance\': {\'dataset\': [{\'name\': \'IM2GPS\', \'accuracy\': {\'25km\': 28.3, \'200km\': 45.1, \'750km\': 74.7, \'2500km\': 88.2}}, {\'name\': \'IM2GPS3K\', \'accuracy\': {\'25km\': 22.4, \'200km\': 37.4, \'750km\': 61.3, \'2500km\': 80.4}}]}, \'description\': \'StreetCLIP is a robust foundation model for open-domain image geolocalization and other geographic and climate-related tasks. Trained on an original dataset of 1.1 million street-level urban and rural geo-tagged images, it achieves state-of-the-art performance on multiple open-domain image geolocalization benchmarks in zero-shot, outperforming supervised models trained on millions of images.\'}', metadata={})]


 Show me how to detect defects of PCB boards from an image in real-time.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'keremberke/yolov8s-pcb-defect-segmentation\', \'api_call\': "YOLO(\'keremberke/yolov8s-pcb-defect-segmentation\')", \'api_arguments\': {\'image\': \'URL or local path to image\'}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.23\', \'ultralytics==8.0.21\'], \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8s-pcb-defect-segmentation\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(results[0].masks)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'pcb-defect-segmentation\', \'accuracy\': {\'mAP@0.5(box)\': 0.515, \'mAP@0.5(mask)\': 0.491}}, \'description\': \'YOLOv8s model for PCB defect segmentation. The model is trained to detect and segment PCB defects such as Dry_joint, Incorrect_installation, PCB_damage, and Short_circuit.\'}', metadata={})]


 I want to assess the condition of roads in a city by analyzing drone footage. Create an image segmentation model to identify potholes in images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'keremberke/yolov8s-pothole-segmentation\', \'api_call\': "YOLO(\'keremberke/yolov8s-pothole-segmentation\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': {\'ultralyticsplus\': \'0.0.23\', \'ultralytics\': \'8.0.21\'}, \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8s-pothole-segmentation\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(results[0].masks)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'pothole-segmentation\', \'accuracy\': {\'mAP@0.5(box)\': 0.928, \'mAP@0.5(mask)\': 0.928}}, \'description\': \'A YOLOv8 model for pothole segmentation. This model detects potholes in images and outputs bounding boxes and masks for the detected potholes.\'}', metadata={})]


 We are building a road maintenance reporting application. We need to use images to identify and segment road potholes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'keremberke/yolov8s-pothole-segmentation\', \'api_call\': "YOLO(\'keremberke/yolov8s-pothole-segmentation\')", \'api_arguments\': {\'image\': \'URL or local path to the image\'}, \'python_environment_requirements\': {\'ultralyticsplus\': \'0.0.23\', \'ultralytics\': \'8.0.21\'}, \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8s-pothole-segmentation\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nprint(results[0].masks)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'pothole-segmentation\', \'accuracy\': {\'mAP@0.5(box)\': 0.928, \'mAP@0.5(mask)\': 0.928}}, \'description\': \'A YOLOv8 model for pothole segmentation. This model detects potholes in images and outputs bounding boxes and masks for the detected potholes.\'}', metadata={})]