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


 I need an AI feature capable of extracting names of people, organizations, and locations from various news articles in different languages.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 We are a consulting firm, and we want to easily identify company names from texts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'joeddav/distilbert-base-uncased-go-emotions-student\', \'api_call\': "pipeline(\'text-classification\', model=\'joeddav/distilbert-base-uncased-go-emotions-student\')", \'api_arguments\': \'text\', \'python_environment_requirements\': [\'transformers\', \'torch\', \'tensorflow\'], \'example_code\': "from transformers import pipeline\\nnlp = pipeline(\'text-classification\', model=\'joeddav/distilbert-base-uncased-go-emotions-student\')\\nresult = nlp(\'I am so happy today!\')", \'performance\': {\'dataset\': \'go_emotions\'}, \'description\': \'This model is distilled from the zero-shot classification pipeline on the unlabeled GoEmotions dataset. It is primarily intended as a demo of how an expensive NLI-based zero-shot model can be distilled to a more efficient student, allowing a classifier to be trained with only unlabeled data.\'}', metadata={})]


 Our company is making a chatbot that needs to extract information from a paragraph. Get the named entities in the paragraph.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 A journalist is looking for historical Olympic host cities and wants to find the year when Beijing hosted the games.\n###Input: {\"table\": {\n    \"year\": [1896, 1900, 1904, 2004, 2008, 2012],\n    \"city\": [\"Athens\", \"Paris\", \"St. Louis\", \"Athens\", \"Beijing\", \"London\"]},\n    \"query\": \"Select the year when Beijing hosted the Olympic games\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'Table-based QA\', \'api_name\': \'neulab/omnitab-large-1024shot\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'neulab/omnitab-large-1024shot\')", \'api_arguments\': {\'table\': \'pd.DataFrame.from_dict(data)\', \'query\': \'str\'}, \'python_environment_requirements\': [\'transformers\', \'pandas\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nimport pandas as pd\\ntokenizer = AutoTokenizer.from_pretrained(neulab/omnitab-large-1024shot)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(neulab/omnitab-large-1024shot)\\ndata = {\\n year: [1896, 1900, 1904, 2004, 2008, 2012],\\n city: [athens, paris, st. louis, athens, beijing, london]\\n}\\ntable = pd.DataFrame.from_dict(data)\\nquery = In which year did beijing host the Olympic Games?\\nencoding = tokenizer(table=table, query=query, return_tensors=pt)\\noutputs = model.generate(**encoding)\\nprint(tokenizer.batch_decode(outputs, skip_special_tokens=True))\', \'performance\': {\'dataset\': \'wikitablequestions\', \'accuracy\': \'Not provided\'}, \'description\': \'OmniTab is a table-based QA model proposed in OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering. neulab/omnitab-large-1024shot (based on BART architecture) is initialized with microsoft/tapex-large and continuously pretrained on natural and synthetic data (SQL2NL model trained in the 1024-shot setting).\'}', metadata={})]


 I am a Korean exchange student in the United States. I am building a Korean Table Question Answering assistant to help me with my studies.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'dsba-lab/koreapas-finetuned-korwikitq\', \'api_call\': "pipeline(\'table-question-answering\', model=\'dsba-lab/koreapas-finetuned-korwikitq\')", \'api_arguments\': {}, \'python_environment_requirements\': {\'transformers\': \'>=4.0.0\'}, \'example_code\': "from transformers import pipeline; table_qa = pipeline(\'table-question-answering\', model=\'dsba-lab/koreapas-finetuned-korwikitq\'); table_qa(table=table, query=\'질문\')", \'performance\': {\'dataset\': \'korwikitq\', \'accuracy\': None}, \'description\': \'A Korean Table Question Answering model finetuned on the korwikitq dataset.\'}', metadata={})]


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


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


 We are building a memory game where a description is displayed for a few seconds and later on, questions on what is shown comes up for the user to remember.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Visual Question Answering\', \'api_name\': \'temp_vilt_vqa\', \'api_call\': "pipeline(\'visual-question-answering\', model=\'Bingsu/temp_vilt_vqa\', tokenizer=\'Bingsu/temp_vilt_vqa\')", \'api_arguments\': {\'model\': \'Bingsu/temp_vilt_vqa\', \'tokenizer\': \'Bingsu/temp_vilt_vqa\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A visual question answering model for answering questions related to images using the Hugging Face Transformers library.\'}', metadata={})]


 We are a medical research team working on a COVID-19 project. We need assistance in question answering related to the pandemic and related research papers.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'deepset/roberta-base-squad2-covid\', \'api_call\': "pipeline(\'question-answering\', model=RobertaForQuestionAnswering.from_pretrained(\'deepset/roberta-base-squad2-covid\'), tokenizer=RobertaTokenizer.from_pretrained(\'deepset/roberta-base-squad2-covid\'))", \'api_arguments\': {\'model_name\': \'deepset/roberta-base-squad2-covid\', \'tokenizer\': \'deepset/roberta-base-squad2-covid\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': {\'QA_input\': {\'question\': \'Why is model conversion important?\', \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'}, \'res\': \'nlp(QA_input)\'}, \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': {\'XVAL_EM\': 0.17890995260663506, \'XVAL_f1\': 0.49925444207319924, \'XVAL_top_3_recall\': 0.8021327014218009}}, \'description\': \'This model is a Roberta-based model fine-tuned on SQuAD-style CORD-19 annotations for the task of extractive question answering in the context of COVID-19. It can be used with the Hugging Face Transformers library for question answering tasks.\'}', metadata={})]


 The customer is writing a book about the solar system and just needs the answer to a question: How long does it take for Mars to orbit the sun?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Asteroid\', \'api_name\': \'ConvTasNet_Libri2Mix_sepclean_16k\', \'api_call\': "Asteroid(\'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k\')", \'api_arguments\': [], \'python_environment_requirements\': [\'asteroid\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Libri2Mix\', \'accuracy\': {\'si_sdr\': 15.243671356901526, \'si_sdr_imp\': 15.243034178473609, \'sdr\': 15.668108919568112, \'sdr_imp\': 15.578229918028036, \'sir\': 25.295100756629957, \'sir_imp\': 25.205219921301754, \'sar\': 16.307682590197313, \'sar_imp\': -51.64989963759405, \'stoi\': 0.9394951175291422, \'stoi_imp\': 0.22640192740016568}}, \'description\': \'This model was trained by Joris Cosentino using the librimix recipe in Asteroid. It was trained on the sep_clean task of the Libri2Mix dataset.\'}', metadata={})]


 I am designing a quiz app that caters to blind users. The app should interpret the images and answer questions based on the image contents.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Visual Question Answering\', \'api_name\': \'blip-vqa-base\', \'api_call\': "BlipForQuestionAnswering.from_pretrained(\'Salesforce/blip-vqa-base\')", \'api_arguments\': {\'raw_image\': \'Image\', \'question\': \'String\'}, \'python_environment_requirements\': {\'transformers\': \'BlipProcessor, BlipForQuestionAnswering\', \'PIL\': \'Image\', \'requests\': \'requests\'}, \'example_code\': "import requests\\nfrom PIL import Image\\nfrom transformers import BlipProcessor, BlipForQuestionAnswering\\nprocessor = BlipProcessor.from_pretrained(Salesforce/blip-vqa-base)\\nmodel = BlipForQuestionAnswering.from_pretrained(Salesforce/blip-vqa-base)\\nimg_url = \'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg\'\\nraw_image = Image.open(requests.get(img_url, stream=True).raw).convert(\'RGB\')\\nquestion = how many dogs are in the picture?\\ninputs = processor(raw_image, question, return_tensors=pt)\\nout = model.generate(**inputs)\\nprint(processor.decode(out[0], skip_special_tokens=True))", \'performance\': {\'dataset\': \'VQA\', \'accuracy\': \'+1.6% in VQA score\'}, \'description\': \'BLIP is a Vision-Language Pre-training (VLP) framework that transfers flexibly to both vision-language understanding and generation tasks. It effectively utilizes noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. This model is trained on visual question answering with a base architecture (using ViT base backbone).\'}', metadata={})]


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


 I want to build a personal assistant to check the logical relationship between two sentences, especially when I have a lot of texts in English.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 I am reading a book and, whenever I engage with an interesting topic, I write a short sentence summarizing that topic. I would like to have an assistant that, given a sentence, automatically classifies that topic among 'technology', 'literature', and 'science'.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'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={})]


 Determine if one Russian sentence logically contradicts the information provided by another Russian sentence.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'Kirili4ik/mbart_ruDialogSum\', \'api_call\': "MBartForConditionalGeneration.from_pretrained(\'Kirili4ik/mbart_ruDialogSum\')", \'api_arguments\': {\'model_name\': \'Kirili4ik/mbart_ruDialogSum\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import MBartTokenizer, MBartForConditionalGeneration\\nmodel_name = Kirili4ik/mbart_ruDialogSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = MBartForConditionalGeneration.from_pretrained(model_name)\\nmodel.eval()\\narticle_text = ...\\ninput_ids = tokenizer(\\n [article_text],\\n max_length=600,\\n padding=max_length,\\n truncation=True,\\n return_tensors=pt,\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n top_k=0,\\n num_beams=3,\\n no_repeat_ngram_size=3\\n)[0]\\nsummary = tokenizer.decode(output_ids, skip_special_tokens=True)\\nprint(summary)\', \'performance\': {\'dataset\': [{\'name\': \'SAMSum Corpus (translated to Russian)\', \'accuracy\': {\'Validation ROGUE-1\': 34.5, \'Validation ROGUE-L\': 33, \'Test ROGUE-1\': 31, \'Test ROGUE-L\': 28}}]}, \'description\': \'MBart for Russian summarization fine-tuned for dialogues summarization. This model was firstly fine-tuned by Ilya Gusev on Gazeta dataset. We have fine tuned that model on SamSum dataset translated to Russian using GoogleTranslateAPI. Moreover! We have implemented a ! telegram bot @summarization_bot ! with the inference of this model. Add it to the chat and get summaries instead of dozens spam messages!\'}', metadata={})]


 The assignment is to develop a software for translating sentences from Spanish to English at a near-human performance.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-es-en\', \'api_call\': "pipeline(\'translation_es_to_en\', model=\'Helsinki-NLP/opus-mt-es-en\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_es_to_en\', model=\'Helsinki-NLP/opus-mt-es-en\')(\'Hola, ¿cómo estás?\')", \'performance\': {\'dataset\': [{\'name\': \'newssyscomb2009-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 30.6, \'chr-F\': 0.57}}, {\'name\': \'news-test2008-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 27.9, \'chr-F\': 0.553}}, {\'name\': \'newstest2009-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 30.4, \'chr-F\': 0.572}}, {\'name\': \'newstest2010-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 36.1, \'chr-F\': 0.614}}, {\'name\': \'newstest2011-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 34.2, \'chr-F\': 0.599}}, {\'name\': \'newstest2012-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 37.9, \'chr-F\': 0.624}}, {\'name\': \'newstest2013-spaeng.spa.eng\', \'accuracy\': {\'BLEU\': 35.3, \'chr-F\': 0.609}}, {\'name\': \'Tatoeba-test.spa.eng\', \'accuracy\': {\'BLEU\': 59.6, \'chr-F\': 0.739}}]}, \'description\': \'Helsinki-NLP/opus-mt-es-en is a machine translation model trained to translate from Spanish to English using the Hugging Face Transformers library. The model is based on the Marian framework and was trained on the OPUS dataset.\'}', metadata={})]


 I am an author. I have a new book that needs a summary. I want the summary to resemble the style of SparkNotes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'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={})]


 An editor wants to summarize his article in French.\n###Input: \"Selon un rapport r\u00e9cent, les constructeurs automobiles pr\u00e9voient d'acc\u00e9l\u00e9rer la production de voitures \u00e9lectriques et de r\u00e9duire la production de voitures \u00e0 moteur \u00e0 combustion interne. Les constructeurs pr\u00e9voient de consacrer davantage de ressources \u00e0 la recherche et au d\u00e9veloppement de technologies pour am\u00e9liorer l'efficacit\u00e9 des batteries, r\u00e9duire les co\u00fbts de production et \u00e9tendre le r\u00e9seau de recharge. Les gouvernements du monde entier continuent de soutenir l'adoption de la mobilit\u00e9 \u00e9lectrique \u00e0 travers des incitations financi\u00e8res et des objectifs ambitieux de r\u00e9duction des \u00e9missions de CO2.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Abstractive Text Summarization\', \'api_name\': \'plguillou/t5-base-fr-sum-cnndm\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'plguillou/t5-base-fr-sum-cnndm\')", \'api_arguments\': {\'input_text\': \'summarize: ARTICLE\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import T5Tokenizer, T5ForConditionalGeneration\'}, \'example_code\': \'tokenizer = T5Tokenizer.from_pretrained(plguillou/t5-base-fr-sum-cnndm)\\nmodel = T5ForConditionalGeneration.from_pretrained(plguillou/t5-base-fr-sum-cnndm)\', \'performance\': {\'dataset\': \'cnn_dailymail\', \'ROUGE-1\': 44.5252, \'ROUGE-2\': 22.652, \'ROUGE-L\': 29.8866}, \'description\': \'This model is a T5 Transformers model (JDBN/t5-base-fr-qg-fquad) that was fine-tuned in French for abstractive text summarization.\'}', metadata={})]


 Design a chatbot for a school website, which can answer queries related to admissions, classes, teachers, and extracurriculars.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


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


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


 Can you give me advice on how to choose the best video game for me?\n###Input: {\"instruction\": \"what is the best way to choose a video game?\", \"knowledge\": \"Some factors to consider when choosing a video game are personal preferences, genre, graphics, gameplay, storyline, platform, and reviews from other players or gaming websites.\", \"dialog\": [\"What type of video games do you prefer playing?\", \"I enjoy action-adventure games and a decent storyline.\"]}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal 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={})]


 Write a script to translate the following French sentence into English: \"Je t\u2019aime.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation, Summarization, Question Answering, Sentiment Analysis\', \'api_name\': \'t5-3b\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'t5-3b\')", \'api_arguments\': \'input_text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "input_text = \'translate English to French: The quick brown fox jumps over the lazy dog\'; inputs = tokenizer.encode(input_text, return_tensors=\'pt\'); outputs = model.generate(inputs); translated_text = tokenizer.decode(outputs[0])", \'performance\': {\'dataset\': \'c4\', \'accuracy\': \'See research paper, Table 14\'}, \'description\': \'T5-3B is a Text-To-Text Transfer Transformer (T5) model with 3 billion parameters. It is designed for various NLP tasks such as translation, summarization, question answering, and sentiment analysis. The model is pre-trained on the Colossal Clean Crawled Corpus (C4) and fine-tuned on multiple supervised and unsupervised tasks.\'}', metadata={})]


 Your company is developing a chatbot and requires accurate summaries of lengthy dialogues without losing context.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'hyunwoongko/blenderbot-9B\', \'api_call\': "pipeline(\'conversational\', model=\'hyunwoongko/blenderbot-9B\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'Input a message to start chatting with hyunwoongko/blenderbot-9B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.\'}', metadata={})]


 I want a system that can translate an article from English to French automatically.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


 We want our System to generate possible user queries for a document provided as a text input.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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={})]


 Our client wants to create marketing slogans. Help them by completing this slogan \"Customer satisfaction is our top <mask>.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'camembert-base\', \'api_call\': "pipeline(\'fill-mask\', model=\'camembert-base\', tokenizer=\'camembert-base\')", \'api_arguments\': [\'model\', \'tokenizer\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline; camembert_fill_mask = pipeline(\'fill-mask\', model=\'camembert-base\', tokenizer=\'camembert-base\'); results = camembert_fill_mask(\'Le camembert est <mask> :)\')", \'performance\': {\'dataset\': \'oscar\', \'accuracy\': \'N/A\'}, \'description\': \'CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data, and pretraining data source domains. It can be used for Fill-Mask tasks.\'}', metadata={})]


 I need a French language model to help me fill in the blanks in given sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'facebook/textless_sm_en_fr\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/textless_sm_en_fr\')", \'api_arguments\': [\'input_file\'], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is a speech-to-speech translation model trained by Facebook. It is designed for translating English speech to French speech.\'}', metadata={})]


 We would like to build a semantic text search system that can find similar documents in a repository based on a given description.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing 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 want to create a playlist of similar songs based on lyrics. How can I compare the sentences and find similar ones?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Transformers\', \'api_name\': \'sentence-transformers/multi-qa-mpnet-base-dot-v1\', \'api_call\': "SentenceTransformer(\'sentence-transformers/multi-qa-mpnet-base-dot-v1\')", \'api_arguments\': [\'query\', \'docs\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer, util\\nquery = How many people live in London?\\ndocs = [Around 9 Million people live in London, London is known for its financial district]\\nmodel = SentenceTransformer(\'sentence-transformers/multi-qa-mpnet-base-dot-v1\')\\nquery_emb = model.encode(query)\\ndoc_emb = model.encode(docs)\\nscores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()\\ndoc_score_pairs = list(zip(docs, scores))\\ndoc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)\\nfor doc, score in doc_score_pairs:\\n print(score, doc)", \'performance\': {\'dataset\': [{\'name\': \'WikiAnswers\', \'accuracy\': 77427422}, {\'name\': \'PAQ\', \'accuracy\': 64371441}, {\'name\': \'Stack Exchange\', \'accuracy\': 25316456}]}, \'description\': \'This is a sentence-transformers model that maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources.\'}', metadata={})]


 I\u2019m putting together a dating site where users can submit questions they'd like the matching algorithm to ask. I want to suggest questions like the ones they have already submitted. Can you provide me with a model to do that?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'deepset/roberta-base-squad2-covid\', \'api_call\': "pipeline(\'question-answering\', model=RobertaForQuestionAnswering.from_pretrained(\'deepset/roberta-base-squad2-covid\'), tokenizer=RobertaTokenizer.from_pretrained(\'deepset/roberta-base-squad2-covid\'))", \'api_arguments\': {\'model_name\': \'deepset/roberta-base-squad2-covid\', \'tokenizer\': \'deepset/roberta-base-squad2-covid\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': {\'QA_input\': {\'question\': \'Why is model conversion important?\', \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'}, \'res\': \'nlp(QA_input)\'}, \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': {\'XVAL_EM\': 0.17890995260663506, \'XVAL_f1\': 0.49925444207319924, \'XVAL_top_3_recall\': 0.8021327014218009}}, \'description\': \'This model is a Roberta-based model fine-tuned on SQuAD-style CORD-19 annotations for the task of extractive question answering in the context of COVID-19. It can be used with the Hugging Face Transformers library for question answering tasks.\'}', metadata={})]


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


 Our customer wants to have this Chinese text \"<Chinese_text>\" read aloud as an audio file in female voice.\n###Input: \"<Chinese_text>\": \"\u4f60\u597d\uff0c\u6b22\u8fce\u6765\u5230\u6570\u5b57\u4e16\u754c\u3002\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best\', \'api_call\': "Text2Speech.from_pretrained(\'espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'torch\', \'espnet_model_zoo\'], \'example_code\': \'import soundfile\\nfrom espnet2.bin.tts_inference import Text2Speech\\ntext2speech = Text2Speech.from_pretrained(espnet/kan-bayashi_csmsc_tts_train_tacotron2_raw_phn_pypinyin_g2p_phone_train.loss.best)\\ntext = 春江潮水连海平，海上明月共潮生\\nspeech = text2speech(text)[wav]\\nsoundfile.write(out.wav, speech.numpy(), text2speech.fs, PCM_16)\', \'performance\': {\'dataset\': \'csmsc\', \'accuracy\': \'Not specified\'}, \'description\': \'A pre-trained Text-to-Speech model for Chinese language using ESPnet framework. It can be used to convert text input into speech output in Chinese.\'}', metadata={})]


 Develop a text-to-speech model for our mobile app to read news articles for our users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'lakahaga/novel_reading_tts\', \'api_call\': "AutoModelForTTS.from_pretrained(\'lakahaga/novel_reading_tts\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "inputs = processor(text, return_tensors=\'pt\'); generated_audio = model.generate(**inputs);", \'performance\': {\'dataset\': \'novelspeech\', \'accuracy\': None}, \'description\': \'This model was trained by lakahaga using novelspeech recipe in espnet. It is designed for Korean text-to-speech tasks.\'}', metadata={})]


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


 We are a French company creating an interactive voice response system for our call centers, and we need to convert text to speech for our telephony prompts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'Fairseq\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'facebook/tts_transformer-fr-cv7_css10\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/tts_transformer-fr-cv7_css10\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'fairseq\'], \'example_code\': \'from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.text_to_speech.hub_interface import TTSHubInterface\\nimport IPython.display as ipd\\nmodels, cfg, task = load_model_ensemble_and_task_from_hf_hub(\\n facebook/tts_transformer-fr-cv7_css10,\\n arg_overrides={vocoder: hifigan, fp16: False}\\n)\\nmodel = models[0]\\nTTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)\\ngenerator = task.build_generator(model, cfg)\\ntext = Bonjour, ceci est un test.\\nsample = TTSHubInterface.get_model_input(task, text)\\nwav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)\\nipd.Audio(wav, rate=rate)\', \'performance\': {\'dataset\': \'common_voice\', \'accuracy\': \'N/A\'}, \'description\': \'Transformer text-to-speech model from fairseq S^2. French, single-speaker male voice. Pre-trained on Common Voice v7, fine-tuned on CSS10.\'}', metadata={})]


 Create Japanese audio from the following text: \"\u3053\u3093\u306b\u3061\u306f\u3001\u79c1\u305f\u3061\u306f\u3042\u306a\u305f\u306e\u52a9\u3051\u304c\u5fc5\u8981\u3067\u3059\u3002\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804\', \'api_call\': "AutoModelForCausalLM.from_pretrained(\'espnet/kan-bayashi_jvs_tts_finetune_jvs001_jsut_vits_raw_phn_jaconv_pyopenjta-truncated-178804\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Japanese text-to-speech model trained using the ESPnet framework. It is designed to convert text input into natural-sounding speech.\'}', metadata={})]


 We need to create automated transcripts from recorded podcasts that include punctuation for better readability.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Natural Language Processing 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={})]


 Convert the audio file of a phone interview to text for further analysis.\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_ro_en\', \'api_call\': "pipeline(\'audio-to-audio\', model=\'facebook/textless_sm_ro_en\')", \'api_arguments\': \'audio file or recording\', \'python_environment_requirements\': \'fairseq, huggingface_hub\', \'example_code\': \'https://huggingface.co/facebook/textless_sm_cs_en\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': \'unknown\'}, \'description\': \'A speech-to-speech translation model for Romanian to English developed by Facebook AI\'}', metadata={})]


 I want to use speech enhancement for an audio call of a coworker.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Telugu_Male_TTS\', \'api_call\': "pipeline(\'text-to-speech\', model=\'SYSPIN/Telugu_Male_TTS\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Telugu Male Text-to-Speech model using the ESPnet framework, provided by Hugging Face.\'}', metadata={})]


 How do I separate the speakers from an audio file using the pre-trained ConvTasNet_Libri2Mix_sepclean_8k model from Hugging Face?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Asteroid\', \'api_name\': \'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k\', \'api_call\': "BaseModel.from_pretrained(\'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k\')", \'api_arguments\': \'audio\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'Libri2Mix\', \'accuracy\': {\'si_sdr\': 10.617130949793383, \'si_sdr_imp\': 12.551811412989263, \'sdr\': 11.231867464482065, \'sdr_imp\': 13.059765009747343, \'sir\': 24.461138352988346, \'sir_imp\': 24.371856452307703, \'sar\': 11.5649982725426, \'sar_imp\': 4.662525705768228, \'stoi\': 0.8701085138712695, \'stoi_imp\': 0.2245418019822898}}, \'description\': \'This model was trained by Joris Cosentino using the librimix recipe in Asteroid. It was trained on the sep_noisy task of the Libri2Mix dataset.\'}', metadata={})]


 A travel app we work on can translate language of a guide in real-time. We are targeting Spanish-speaking tourists.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 Design an audio description system for a user who needs help translating spoken English audio to spoken Hokkien audio.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'Fairseq\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'unit_hifigan_HK_layer12.km2500_frame_TAT-TTS\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TT\')", \'api_arguments\': {\'unit\': \'Text input for the TTS model\'}, \'python_environment_requirements\': [\'fairseq\', \'huggingface_hub\', \'torchaudio\'], \'example_code\': "import json\\nimport os\\nfrom pathlib import Path\\nimport IPython.display as ipd\\nfrom fairseq import hub_utils\\nfrom fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\\nfrom fairseq.models.speech_to_text.hub_interface import S2THubInterface\\nfrom fairseq.models.text_to_speech import CodeHiFiGANVocoder\\nfrom fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\\nfrom huggingface_hub import snapshot_download\\nimport torchaudio\\ncache_dir = os.getenv(HUGGINGFACE_HUB_CACHE)\\nlibrary_name = fairseq\\ncache_dir = (\\n cache_dir or (Path.home() / .cache / library_name).as_posix()\\n)\\ncache_dir = snapshot_download(\\n ffacebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS, cache_dir=cache_dir, library_name=library_name\\n)\\nx = hub_utils.from_pretrained(\\n cache_dir,\\n model.pt,\\n .,\\n archive_map=CodeHiFiGANVocoder.hub_models(),\\n config_yaml=config.json,\\n fp16=False,\\n is_vocoder=True,\\n)\\nwith open(f{x[\'args\'][\'data\']}/config.json) as f:\\n vocoder_cfg = json.load(f)\\nassert (\\n len(x[args][model_path]) == 1\\n), Too many vocoder models in the input\\nvocoder = CodeHiFiGANVocoder(x[args][model_path][0], vocoder_cfg)\\ntts_model = VocoderHubInterface(vocoder_cfg, vocoder)\\ntts_sample = tts_model.get_model_input(unit)\\nwav, sr = tts_model.get_prediction(tts_sample)\\nipd.Audio(wav, rate=sr)", \'performance\': {\'dataset\': \'TAT-TTS\', \'accuracy\': \'Not provided\'}, \'description\': \'Hokkien unit HiFiGAN based vocoder from fairseq. Trained with TAT-TTS data with 4 speakers in Taiwanese Hokkien accent.\'}', metadata={})]


 Implement a method to perform speech-to-speech translation between Hokkien and English using the xm_transformer_s2ut_hk-en model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'xm_transformer_s2ut_en-hk\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/xm_transformer_s2ut_en-hk\')", \'api_arguments\': {\'arg_overrides\': {\'config_yaml\': \'config.yaml\', \'task\': \'speech_to_text\'}, \'cache_dir\': \'cache_dir\'}, \'python_environment_requirements\': [\'fairseq\', \'huggingface_hub\', \'torchaudio\'], \'example_code\': {\'import_modules\': [\'import json\', \'import os\', \'from pathlib import Path\', \'import IPython.display as ipd\', \'from fairseq import hub_utils\', \'from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub\', \'from fairseq.models.speech_to_text.hub_interface import S2THubInterface\', \'from fairseq.models.text_to_speech import CodeHiFiGANVocoder\', \'from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface\', \'from huggingface_hub import snapshot_download\', \'import torchaudio\'], \'load_model\': ["cache_dir = os.getenv(\'HUGGINGFACE_HUB_CACHE\')", "models, cfg, task = load_model_ensemble_and_task_from_hf_hub(\'facebook/xm_transformer_s2ut_en-hk\', arg_overrides={\'config_yaml\': \'config.yaml\', \'task\': \'speech_to_text\'}, cache_dir=cache_dir)", \'model = models[0].cpu()\', "cfg[\'task\'].cpu = True"], \'generate_prediction\': [\'generator = task.build_generator([model], cfg)\', "audio, _ = torchaudio.load(\'/path/to/an/audio/file\')", \'sample = S2THubInterface.get_model_input(task, audio)\', \'unit = S2THubInterface.get_prediction(task, model, generator, sample)\'], \'speech_synthesis\': ["library_name = \'fairseq\'", "cache_dir = (cache_dir or (Path.home() / \'.cache\' / library_name).as_posix())", "cache_dir = snapshot_download(\'facebook/unit_hifigan_HK_layer12.km2500_frame_TAT-TTS\', cache_dir=cache_dir, library_name=library_name)", "x = hub_utils.from_pretrained(cache_dir, \'model.pt\', \'.\', archive_map=CodeHiFiGANVocoder.hub_models(), config_yaml=\'config.json\', fp16=False, is_vocoder=True)", "with open(f\'{x[\'args\'][\'data\']}/config.json\') as f:", \'  vocoder_cfg = json.load(f)\', "assert (len(x[\'args\'][\'model_path\']) == 1), \'Too many vocoder models in the input\'", "vocoder = CodeHiFiGANVocoder(x[\'args\'][\'model_path\'][0], vocoder_cfg)", \'tts_model = VocoderHubInterface(vocoder_cfg, vocoder)\', \'tts_sample = tts_model.get_model_input(unit)\', \'wav, sr = tts_model.get_prediction(tts_sample)\', \'ipd.Audio(wav, rate=sr)\']}, \'performance\': {\'dataset\': \'MuST-C\', \'accuracy\': \'Not specified\'}, \'description\': \'Speech-to-speech translation model with single-pass decoder (S2UT) from fairseq: English-Hokkien. Trained with supervised data in TED domain, and weakly supervised data in TED and Audiobook domain.\'}', metadata={})]


 A company wants to analyze the sentiment of the customer feedback in their Spanish-speaking call center. Generate a script for this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'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={})]


 Our company is working on an AI-powered language learning app for German. We need to build a feature that classifies emotions in German speech.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Natural Language Processing Text Classification', 'framework': 'Hugging Face Transformers', 'functionality': 'German Sentiment Classification', 'api_name': 'oliverguhr/german-sentiment-bert', 'api_call': 'SentimentModel()', 'api_arguments': ['texts'], 'python_environment_requirements': 'pip install germansentiment', 'example_code': ['from germansentiment import SentimentModel', 'model = SentimentModel()', 'texts = [', ' Mit keinem guten Ergebniss,Das ist gar nicht mal so gut,', ' Total awesome!,nicht so schlecht wie erwartet,', ' Der Test verlief positiv.,Sie fhrt ein grnes Auto.]', 'result = model.predict_sentiment(texts)', 'print(result)'], 'performance': {'dataset': ['holidaycheck', 'scare', 'filmstarts', 'germeval', 'PotTS', 'emotions', 'sb10k', 'Leipzig Wikipedia Corpus 2016', 'all'], 'accuracy': [0.9568, 0.9418, 0.9021, 0.7536, 0.678, 0.9649, 0.7376, 0.9967, 0.9639]}, 'description': 'This model was trained for sentiment classification of German language texts. The model uses the Googles Bert architecture and was trained on 1.834 million German-language samples. The training data contains texts from various domains like Twitter, Facebook and movie, app and hotel reviews.'}", metadata={})]


 We are a voice assistant service, and we need to verify the speaker identity for enhanced security when users access the system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 I run a call center and I need a system that can identify the person on the other end of the line by analyzing their voice.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 A transcription service wants to identify which speaker said which words in an audio file. Help them with this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'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 running a chain of wine stores and I want to categorize wines and recommend them based on their quality.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 I own an e-commerce platform and I need to predict which customers will make a purchase based on their browsing behavior.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


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


 A travel agency needs a system to predict whether a client's vacation will be successful based on their chosen destination, accommodation, and travel style. We want to provide suggestions for clients who may need additional support.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'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={})]


 A new city planning company wants to estimate carbon emissions for different types of buildings to provide sustainable living solutions. We need a model to classify the carbon emissions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'tejas23/autotrain-amx2-1702259725\', \'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\': {\'accuracy\': 0.827}}, \'description\': \'Multi-class Classification Model for Carbon Emissions\'}', metadata={})]


 We want to evaluate the carbon footprint of a construction project based on the tabular data of material consumption.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'45473113800\', \'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\': \'samvelkoch/autotrain-data-prknsn-2\', \'accuracy\': {\'Loss\': 5.079, \'R2\': 0.109, \'MSE\': 25.795, \'MAE\': 3.78, \'RMSLE\': 0.849}}, \'description\': \'A tabular regression model trained with AutoTrain for predicting carbon emissions.\'}', metadata={})]


 Our company is an environmental consultancy firm. Determine the carbon emissions of different facilities based on the provided data.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'jwan2021/autotrain-jwan-autotrain1-1768961489\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'Validation Metrics\', \'accuracy\': 0.983}, \'description\': \'Binary Classification model for Carbon Emissions prediction\'}', metadata={})]


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


 We have some clients asking for estimating CO2 emissions based on their historic data, which is in a CSV file.\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={})]


 Determine the electricity consumption of a residential area based on historical data.\n
 Use this API documentation for reference: [Document(page_content="{'domain': 'Tabular Tabular Regression', 'framework': 'Scikit-learn', 'functionality': 'skops', 'api_name': 'rajistics/MAPIE-TS-Electricity', 'api_call': 'RandomForestRegressor(max_depth=10, n_estimators=50, random_state=59)', 'api_arguments': {'bootstrap': 'True', 'ccp_alpha': '0.0', 'criterion': 'squared_error', 'max_depth': '10', 'max_features': '1.0', 'max_leaf_nodes': '', 'max_samples': '', 'min_impurity_decrease': '0.0', 'min_samples_leaf': '1', 'min_samples_split': '2', 'min_weight_fraction_leaf': '0.0', 'n_estimators': '50', 'n_jobs': '', 'oob_score': 'False', 'random_state': '59', 'verbose': '0', 'warm_start': 'False'}, 'python_environment_requirements': '', 'example_code': '', 'performance': {'dataset': '', 'accuracy': ''}, 'description': 'A RandomForestRegressor model for electricity consumption prediction.'}", metadata={})]


 A soccer simulation company wants to use a reinforcement learning agent that can play SoccerTwos effectively.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Unity ML-Agents Library\', \'functionality\': \'Train and play SoccerTwos\', \'api_name\': \'poca-SoccerTwosv2\', \'api_call\': "mlagents-load-from-hf --repo-id=\'Raiden-1001/poca-SoccerTwosv2\' --local-dir=\'./downloads\'", \'api_arguments\': [\'your_configuration_file_path.yaml\', \'run_id\'], \'python_environment_requirements\': [\'ml-agents\'], \'example_code\': \'mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume\', \'performance\': {\'dataset\': \'SoccerTwos\', \'accuracy\': \'Not provided\'}, \'description\': \'A trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library.\'}', metadata={})]


 Our company is developing a game, and they want to include an AI agent to play the game. We need to evaluate its performance on the CartPole-v1 environment for consistency.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'CartPole-v1\', \'api_name\': \'dqn-CartPole-v1\', \'api_call\': "load_from_hub(repo_id=\'sb3/dqn-CartPole-v1\',filename=\'{MODEL FILENAME}.zip\',)", \'api_arguments\': [\'algo\', \'env\', \'logs\'], \'python_environment_requirements\': [\'rl_zoo3\', \'stable-baselines3\', \'stable-baselines3-contrib\'], \'example_code\': \'python train.py --algo dqn --env CartPole-v1 -f logs/\', \'performance\': {\'dataset\': \'CartPole-v1\', \'accuracy\': \'500.00 +/- 0.00\'}, \'description\': \'This is a trained model of a DQN agent playing CartPole-v1 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.\'}', metadata={})]


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


 We are a news agency that wants to extract useful features from Korean news articles for a content recommendation service.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'google/pegasus-newsroom\', \'api_call\': "pipeline(\'summarization\', model=\'google/pegasus-newsroom\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'This model can be loaded on the Inference API on-demand.\', \'performance\': {\'dataset\': \'newsroom\', \'accuracy\': \'45.98/34.20/42.18\'}, \'description\': \'PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. The model is trained on both C4 and HugeNews datasets and is designed for summarization tasks.\'}', metadata={})]


 We want to automatically generate hashtags for the provided image URL to improve social media post performance.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Transformers\', \'functionality\': \'Image Captioning\', \'api_name\': \'blip-image-captioning-large\', \'api_call\': \'BlipForConditionalGeneration.from_pretrained(Salesforce/blip-image-captioning-large)\', \'api_arguments\': {\'raw_image\': \'Image\', \'text\': \'Optional Text\'}, \'python_environment_requirements\': {\'transformers\': \'BlipProcessor, BlipForConditionalGeneration\', \'PIL\': \'Image\', \'requests\': \'requests\'}, \'example_code\': {\'import_requests\': \'import requests\', \'import_PIL\': \'from PIL import Image\', \'import_transformers\': \'from transformers import BlipProcessor, BlipForConditionalGeneration\', \'load_processor\': \'processor = BlipProcessor.from_pretrained(Salesforce/blip-image-captioning-large)\', \'load_model\': \'model = BlipForConditionalGeneration.from_pretrained(Salesforce/blip-image-captioning-large)\', \'load_image\': "img_url = \'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg\'\\nraw_image = Image.open(requests.get(img_url, stream=True).raw).convert(\'RGB\')", \'conditional_captioning\': \'text = a photography of\\ninputs = processor(raw_image, text, return_tensors=pt)\\nout = model.generate(**inputs)\\nprint(processor.decode(out[0], skip_special_tokens=True))\', \'unconditional_captioning\': \'inputs = processor(raw_image, return_tensors=pt)\\nout = model.generate(**inputs)\\nprint(processor.decode(out[0], skip_special_tokens=True))\'}, \'performance\': {\'dataset\': \'COCO\', \'accuracy\': {\'image-text retrieval\': \'+2.7% recall@1\', \'image captioning\': \'+2.8% CIDEr\', \'VQA\': \'+1.6% VQA score\'}}, \'description\': \'BLIP is a Vision-Language Pre-training (VLP) framework that achieves state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval, image captioning, and VQA. It effectively utilizes noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones.\'}', metadata={})]


 I am building a recommendation engine for TV shows. Can you evaluate the TV shows using a BERT-based model trained on sentence embedding to find the most similar TV shows based on description?\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 want to create a solution that can answer questions related to an image of my pet dogs.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'julien-c/hotdog-not-hotdog\', \'api_call\': "pipeline(\'image-classification\', model=\'julien-c/hotdog-not-hotdog\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': 0.825}, \'description\': \'A model that classifies images as hotdog or not hotdog.\'}', metadata={})]


 Design an explanation on how to use Pix2Struct to analyze and generate text based on visuals such as graphs and charts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'google/pix2struct-chartqa-base\', \'api_call\': "Pix2StructForConditionalGeneration.from_pretrained(\'google/pix2struct-chartqa-base\')", \'api_arguments\': [\'t5x_checkpoint_path\', \'pytorch_dump_path\', \'use-large\'], \'python_environment_requirements\': \'transformers\', \'example_code\': \'python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE\', \'performance\': {\'dataset\': \'ChartQA\', \'accuracy\': \'Not provided\'}, \'description\': \'Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The model is pretrained by learning to parse masked screenshots of web pages into simplified HTML. It can achieve state-of-the-art results in six out of nine tasks across four domains: documents, illustrations, user interfaces, and natural images.\'}', metadata={})]


 Design a product that allows users to read street signs in a foreign language.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'facebook/nllb-200-distilled-600M\', \'api_call\': "pipeline(\'translation_xx_to_yy\', model=\'facebook/nllb-200-distilled-600M\')", \'api_arguments\': [\'model\', \'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline; translator = pipeline(\'translation_xx_to_yy\', model=\'facebook/nllb-200-distilled-600M\'); translator(\'Hello World\')", \'performance\': {\'dataset\': \'Flores-200\', \'accuracy\': \'BLEU, spBLEU, chrF++\'}, \'description\': \'NLLB-200 is a machine translation model primarily intended for research in machine translation, especially for low-resource languages. It allows for single sentence translation among 200 languages. The model was trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation.\'}', metadata={})]


 I have a text question about an image, and I would like to receive an appropriate answer.\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={})]


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


 We received a scanned document with a lot of information. We need to go through it and find answers to specific questions. Create a program to extract information from the document.\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={})]


 In a pharmaceutical company, we are working on developing a new drug, and I want you to help classify the molecular structures of my dataset.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'sentiment_analysis_generic_dataset\', \'api_call\': "pipeline(\'text-classification\', model=\'Seethal/sentiment_analysis_generic_dataset\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "sentiment_analysis(\'I love this product!\')", \'performance\': {\'dataset\': \'generic_dataset\', \'accuracy\': \'Not specified\'}, \'description\': \'This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text classification.\'}', metadata={})]


 Calculate the distance between the objects in an image for an autonomous vehicle driving in a parking lot.\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={})]