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


 To improve customer satisfaction, we want to automate the creation of written explanations that sound more natural and less robotic. Can you suggest a way to generate text that sounds conscious and alive?\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={})]


 Our company is growing and expanding into multiple countries. We need a solution for translating English sentences to German to ensure effective communication.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'sshleifer/tiny-marian-en-de\', \'api_call\': "pipeline(\'translation_en_to_de\', model=\'sshleifer/tiny-marian-en-de\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A tiny English to German translation model using the Marian framework in Hugging Face Transformers.\'}', metadata={})]


 We need a solution for creating Polish subtitles for YouTube videos in Spanish. The AI should provide the translation.\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 English teacher looking to brainstorm synonyms for the word \"happy.\" Can a model help me generate similar words?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Grammar Correction\', \'api_name\': \'vennify/t5-base-grammar-correction\', \'api_call\': "HappyTextToText(\'T5\', \'vennify/t5-base-grammar-correction\')", \'api_arguments\': {\'num_beams\': 5, \'min_length\': 1}, \'python_environment_requirements\': {\'package\': \'happytransformer\', \'installation\': \'pip install happytransformer\'}, \'example_code\': \'from happytransformer import HappyTextToText, TTSettings\\nhappy_tt = HappyTextToText(T5, vennify/t5-base-grammar-correction)\\nargs = TTSettings(num_beams=5, min_length=1)\\nresult = happy_tt.generate_text(grammar: This sentences has has bads grammar., args=args)\\nprint(result.text)\', \'performance\': {\'dataset\': \'jfleg\', \'accuracy\': \'Not provided\'}, \'description\': \'This model generates a revised version of inputted text with the goal of containing fewer grammatical errors. It was trained with Happy Transformer using a dataset called JFLEG.\'}', metadata={})]


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


 Can you help me find the best model to fill in the gap in my legal document? I want a smaller model with higher efficiency but maintains a high level of accuracy.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Fill-Mask\', \'api_name\': \'nlpaueb/legal-bert-small-uncased\', \'api_call\': "AutoModel.from_pretrained(\'nlpaueb/legal-bert-small-uncased\')", \'api_arguments\': {\'pretrained_model_name_or_path\': \'nlpaueb/legal-bert-small-uncased\'}, \'python_environment_requirements\': {\'transformers\': \'AutoTokenizer, AutoModel\'}, \'example_code\': "from transformers import AutoTokenizer, AutoModel\\ntokenizer = AutoTokenizer.from_pretrained(\'nlpaueb/legal-bert-small-uncased\')\\nmodel = AutoModel.from_pretrained(\'nlpaueb/legal-bert-small-uncased\')", \'performance\': {\'dataset\': \'Legal Corpora\', \'accuracy\': \'Comparable to larger models\'}, \'description\': \'LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. This is the light-weight version of BERT-BASE (33% the size of BERT-BASE) pre-trained from scratch on legal data, which achieves comparable performance to larger models, while being much more efficient (approximately 4 times faster) with a smaller environmental footprint.\'}', metadata={})]


 I am learning English literature. I plan to build a system that has a huge database of English sentences and keeps the important ones.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Flair\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'flair/ner-english-ontonotes-large\', \'api_call\': "SequenceTagger.load(\'flair/ner-english-ontonotes-large\')", \'api_arguments\': [\'sentence\'], \'python_environment_requirements\': [\'flair\'], \'example_code\': "from flair.data import Sentence\\nfrom flair.models import SequenceTagger\\n\\n# load tagger\\ntagger = SequenceTagger.load(\'flair/ner-english-ontonotes-large\')\\n\\n# make example sentence\\nsentence = Sentence(\'On September 1st George won 1 dollar while watching Game of Thrones.\')\\n\\n# predict NER tags\\ntagger.predict(sentence)\\n\\n# print sentence\\nprint(sentence)\\n\\n# print predicted NER spans\\nprint(\'The following NER tags are found:\')\\n\\n# iterate over entities and print\\nfor entity in sentence.get_spans(\'ner\'):\\n    print(entity)", \'performance\': {\'dataset\': \'Ontonotes\', \'accuracy\': 90.93}, \'description\': \'English NER in Flair (Ontonotes large model). This is the large 18-class NER model for English that ships with Flair. It predicts 18 tags such as cardinal value, date value, event name, building name, geo-political entity, language name, law name, location name, money name, affiliation, ordinal value, organization name, percent value, person name, product name, quantity value, time value, and name of work of art. The model is based on document-level XLM-R embeddings and FLERT.\'}', metadata={})]


 Our company specializes in providing information on similar topics. We want to find similar sentences in a text document.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Embeddings\', \'api_name\': \'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\', \'api_call\': "SentenceTransformer(\'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer\\nsentences = [This is an example sentence, Each sentence is converted]\\nmodel = SentenceTransformer(\'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Automated evaluation\'}, \'description\': \'This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


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


 I am a CEO, and I want to create an audio announcement that will be played to my customers. The text announcement is: \"Dear valued customers, we are glad to announce a 30% discount on our special offer. Hurry up!\"\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={})]


 Our user would like to develop an audiobook using a Text-to-Speech API. Convert the text of a book into an audio file.\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={})]


 In our language app, we want to include text to speech functionality for Hokkien, a dialect of Chinese, using the TAT-TTS dataset.\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={})]


 Develop an application to transcribe audio files with punctuation marks for a podcast platform.\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={})]


 Our company has an audio archive of Chinese podcasts, and we want to create transcripts for them.\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={})]


 We are a podcast platform. We have a library of multiple podcasts that are mixed with background noise. We need to enhance the podcast audio quality to improve the user experience on our platform.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Marathi_Male_TTS\', \'api_call\': "api.load(\'ESPnet/espnet_model_zoo:SYSPIN/Marathi_Male_TTS\').", \'api_arguments\': [], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Marathi Male Text-to-Speech model using ESPnet framework.\'}', metadata={})]


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


 I work at a call center, and I need to convert a recorded customer call into an audio file with a different voice without changing the content.\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={})]


 In a customer support system, we have received a recorded audio file from a customer complaining about an issue in Czech language. Translate it to English language preserving the audio format.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


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


 We are building a virtual assistant which can translate English speech input to Hokkien in real-time.\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={})]


 A language service wants to incorporate a speech-to-speech translation feature that assists users in translating Hokkien to English on an audio file.\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={})]


 Our company is building an interactive platform for children to learn random trivia quickly. We need a model to understand what is being spoken and what category it belongs to.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrase-based utterance augmentation\', \'api_name\': \'prithivida/parrot_fluency_model\', \'api_call\': "pipeline(\'text-classification\', model=\'prithivida/parrot_fluency_model\')", \'api_arguments\': \'text\', \'python_environment_requirements\': [\'transformers\'], \'example_code\': "parrot(\'your input text\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model.\'}', metadata={})]


 We are a company that develops voice assistants, and we need to verify a user's voice to authenticate them.\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={})]


 We recently launched a customer service hotline, and we want to estimate the demographics of callers.\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={})]


 Create a smart speaker that can recognize voice commands such as \"Turn on the lights,\" \"Play music,\" or \"Set a timer.\"\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={})]


 Our company's voice assistant needs to be able to detect voice activity in a conversation.\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={})]


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


 We are trying to create a solution for an HR department to predict whether a candidate would be a potential employee based on a list of background information.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Predicting Pokemon HP\', \'api_name\': \'julien-c/pokemon-predict-hp\', \'api_call\': "pipeline(\'regression\', model=\'julien-c/pokemon-predict-hp\')", \'api_arguments\': \'input_data\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'julien-c/kaggle-rounakbanik-pokemon\', \'accuracy\': {\'mean_absolute_error\': 15.909, \'model_loss\': 647.605}}, \'description\': \'A tabular regression model trained on the julien-c/kaggle-rounakbanik-pokemon dataset to predict the HP of Pokemon.\'}', metadata={})]


 I have just downloaded a dataset that measures CO2 Emissions. I want to classify this dataset using a pre-trained model.\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={})]


 We are building an app that simulates Pokemon battles. Can you help us predict the HP of a Pokemon given its input attributes?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Predicting Pokemon HP\', \'api_name\': \'julien-c/pokemon-predict-hp\', \'api_call\': "pipeline(\'regression\', model=\'julien-c/pokemon-predict-hp\')", \'api_arguments\': \'input_data\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'julien-c/kaggle-rounakbanik-pokemon\', \'accuracy\': {\'mean_absolute_error\': 15.909, \'model_loss\': 647.605}}, \'description\': \'A tabular regression model trained on the julien-c/kaggle-rounakbanik-pokemon dataset to predict the HP of Pokemon.\'}', metadata={})]


 We are a content marketing agency and we are focusing on promoting our clients' products. We want to use a tool to generate interesting marketing messages.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'it5-base-news-summarization\', \'api_call\': "pipeline(\'summarization\', model=\'it5/it5-base-news-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'newsum(Dal 31 maggio è infine partita la piattaforma ITsART, a più di un anno da quando – durante il primo lockdown – il ministro della Cultura Dario Franceschini ne aveva parlato come di «una sorta di Netflix della cultura», pensata per «offrire a tutto il mondo la cultura italiana a pagamento». È presto per dare giudizi definitivi sulla piattaforma, e di certo sarà difficile farlo anche più avanti senza numeri precisi. Al momento, l’unica cosa che si può fare è guardare com’è fatto il sito, contare quanti contenuti ci sono (circa 700 “titoli”, tra film, documentari, spettacoli teatrali e musicali e altri eventi) e provare a dare un giudizio sul loro valore e sulla loro varietà. Intanto, una cosa notata da più parti è che diversi contenuti di ITsART sono a pagamento sulla piattaforma sebbene altrove, per esempio su RaiPlay, siano invece disponibili gratuitamente.)\', \'performance\': {\'dataset\': \'NewsSum-IT\', \'accuracy\': {\'Rouge1\': 0.339, \'Rouge2\': 0.16, \'RougeL\': 0.263}}, \'description\': \'IT5 Base model fine-tuned on news summarization on the Fanpage and Il Post corpora for Italian Language Understanding and Generation.\'}', metadata={})]


 Analyze a set of sentences to find the most similar pairs.\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/distiluse-base-multilingual-cased-v1\', \'api_call\': "SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v1\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer\\nsentences = [This is an example sentence, Each sentence is converted]\\nmodel = SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v1\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'N/A\'}, \'description\': \'This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 I need a tool to extract code syntax and named entities from a text taken from StackOverflow.\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 video streaming platform wants to categorize thousands of movies into genres. Please help us classify them without any genre labels.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Multi-class Classification\', \'api_name\': \'Alexei1/imdb\', \'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\': \'IMDB\', \'accuracy\': 0.487}, \'description\': \'A tabular classification model trained using AutoTrain for sentiment analysis on the IMDB dataset. The model has a CO2 emission of 0.0186 grams and an accuracy of 0.487.\'}', metadata={})]


 Can you generate an image of a lighthouse on a foggy island, based on my description?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'lllyasviel/control_v11p_sd15_canny\', \'api_call\': "ControlNetModel.from_pretrained(\'lllyasviel/control_v11p_sd15_canny\')", \'api_arguments\': {\'text\': \'a blue paradise bird in the jungle\', \'num_inference_steps\': 20, \'generator\': \'torch.manual_seed(33)\', \'image\': \'control_image\'}, \'python_environment_requirements\': [\'pip install opencv-contrib-python\', \'pip install diffusers transformers accelerate\'], \'example_code\': [\'import torch\', \'import os\', \'from huggingface_hub import HfApi\', \'from pathlib import Path\', \'from diffusers.utils import load_image\', \'import numpy as np\', \'import cv2\', \'from PIL import Image\', \'from diffusers import (\', \' ControlNetModel,\', \' StableDiffusionControlNetPipeline,\', \' UniPCMultistepScheduler,\', \')\', \'checkpoint = lllyasviel/control_v11p_sd15_canny\', \'image = load_image(\', \' https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/input.png\', \')\', \'image = np.array(image)\', \'low_threshold = 100\', \'high_threshold = 200\', \'image = cv2.Canny(image, low_threshold, high_threshold)\', \'image = image[:, :, None]\', \'image = np.concatenate([image, image, image], axis=2)\', \'control_image = Image.fromarray(image)\', \'control_image.save(./images/control.png)\', \'controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)\', \'pipe = StableDiffusionControlNetPipeline.from_pretrained(\', \' runwayml/stable-diffusion-v1-5, controlnet=controlnet, torch_dtype=torch.float16\', \')\', \'pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\', \'pipe.enable_model_cpu_offload()\', \'generator = torch.manual_seed(33)\', \'image = pipe(a blue paradise bird in the jungle, num_inference_steps=20, generator=generator, image=control_image).images[0]\', "image.save(\'images/image_out.png\')"], \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'Controlnet v1.1 is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Canny edges. It can be used in combination with Stable Diffusion, such as runwayml/stable-diffusion-v1-5.\'}', metadata={})]


 I am an illustrator, I want to create an appealing image based on a text description for commercial purposes.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'gsdf/Counterfeit-V2.5\', \'api_call\': "pipeline(\'text-to-image\', model=\'gsdf/Counterfeit-V2.5\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'((masterpiece,best quality)),1girl, solo, animal ears, rabbit, barefoot, knees up, dress, sitting, rabbit ears, short sleeves, looking at viewer, grass, short hair, smile, white hair, puffy sleeves, outdoors, puffy short sleeves, bangs, on ground, full body, animal, white dress, sunlight, brown eyes, dappled sunlight, day, depth of field\', \'performance\': {\'dataset\': \'EasyNegative\', \'accuracy\': \'Not provided\'}, \'description\': \'Counterfeit-V2.5 is a text-to-image model that generates anime-style images based on text prompts. It has been updated for ease of use and can be used with negative prompts to create high-quality images.\'}', metadata={})]


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


 Generate an image of a beautiful fantasy landscape based on the description provided: a peaceful scene in a lush green forest with a crystal-clear river flowing through it, under a blue sky with fluffy white clouds.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'GreeneryScenery/SheepsControlV3\', \'api_call\': "pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV3\')", \'api_arguments\': {\'image\': \'Path to image file\', \'text_guidance\': \'Optional text guidance for the model\'}, \'python_environment_requirements\': {\'transformers\': \'latest\', \'torch\': \'latest\'}, \'example_code\': [\'from transformers import pipeline\', "model = pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV3\')", "result = model({\'image\': \'path/to/image.jpg\', \'text_guidance\': \'Optional text guidance\'})"], \'performance\': {\'dataset\': \'GreeneryScenery/SheepsControlV3\', \'accuracy\': \'Not provided\'}, \'description\': \'GreeneryScenery/SheepsControlV3 is a model for image-to-image tasks. It can be used to generate images based on the input image and optional text guidance. The model has some limitations, such as the conditioning image not affecting the output image much. Improvements can be made by training for more epochs, using better prompts, and preprocessing the data.\'}', metadata={})]


 Tell me a text summary and answer a question from an image.\n###Input: img_url=\"https://example.com/image.jpg\", question=\"What is the main color of the object?\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'kha-white/manga-ocr-base\', \'api_call\': "pipeline(\'ocr\', model=\'kha-white/manga-ocr-base\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'manga109s\', \'accuracy\': \'\'}, \'description\': \'Optical character recognition for Japanese text, with the main focus being Japanese manga. It uses Vision Encoder Decoder framework. Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality text recognition, robust against various scenarios specific to manga: both vertical and horizontal text, text with furigana, text overlaid on images, wide variety of fonts and font styles, and low quality images.\'}', metadata={})]


 My boss wants me to extract captions from images of people in different settings.\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={})]


 Create an AI system capable of producing short videos based on text input in Persian and English.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'ImRma/Brucelee\', \'api_call\': "pipeline(\'text-to-video\', model=\'ImRma/Brucelee\')", \'api_arguments\': [\'your_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Hugging Face model for converting Persian and English text into video.\'}', metadata={})]


 I am organizing a virtual party and want to create a short, autogenerated video based on a text description (e.g., \"cats playing with laser pointer\"). Can you accomplish this with a text-to-video generation API?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'camenduru/text2-video-zero\', \'api_call\': "pipeline(\'text-to-video\', model=\'camenduru/text2-video-zero\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is used for generating videos from text inputs. It is based on the Hugging Face framework and can be used with the transformers library. The model is trained on a variety of text and video datasets, and can be used for tasks such as video summarization, video generation from text prompts, and more.\'}', metadata={})]


 I need to create a system that can answer questions related to a document provided. The system should use a pre-trained model.\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\': \'shahrukhx01/question-vs-statement-classifier\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'shahrukhx01/question-vs-statement-classifier\')", \'api_arguments\': {\'tokenizer\': \'AutoTokenizer.from_pretrained(shahrukhx01/question-vs-statement-classifier)\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import AutoTokenizer, AutoModelForSequenceClassification\'}, \'example_code\': \'tokenizer = AutoTokenizer.from_pretrained(shahrukhx01/question-vs-statement-classifier)\\nmodel = AutoModelForSequenceClassification.from_pretrained(shahrukhx01/question-vs-statement-classifier)\', \'performance\': {\'dataset\': \'Haystack\', \'accuracy\': \'Not provided\'}, \'description\': \'Trained to add the feature for classifying queries between Question Query vs Statement Query using classification in Haystack\'}', metadata={})]


 We are developing a document management system. We want to extract the relevant information from them through OCR text scanning and answering questions.\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={})]


 The team is creating a home security system. We are currently looking at understanding the depth of objects in the video stream.\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/yolov8s-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8s-csgo-player-detection\')", \'api_arguments\': {\'image\': \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.23\', \'ultralytics==8.0.21\'], \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8s-csgo-player-detection\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.886}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players. Supports the labels [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


 I want to know the depth information of an image for a robot navigation project.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Depth Estimation\', \'api_name\': \'glpn-nyu-finetuned-diode\', \'api_call\': "pipeline(\'depth-estimation\', model=\'sayakpaul/glpn-nyu-finetuned-diode\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'diode-subset\', \'accuracy\': {\'Loss\': 0.4359, \'Rmse\': 0.4276}}, \'description\': \'This model is a fine-tuned version of vinvino02/glpn-nyu on the diode-subset dataset.\'}', metadata={})]


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


 We are building a smart security system to determine if a person is an adult\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={})]


 Design a program that can help farmers detect diseases in bean crops by analyzing images of the crop leaves.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'GreeneryScenery/SheepsControlV5\', \'api_call\': "pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV5\')", \'api_arguments\': {\'input_image\': \'path/to/image/file\'}, \'python_environment_requirements\': {\'huggingface_hub\': \'>=0.0.17\', \'transformers\': \'>=4.13.0\', \'torch\': \'>=1.10.0\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'poloclub/diffusiondb\', \'accuracy\': \'Not provided\'}, \'description\': \'SheepsControlV5 is an image-to-image model trained on the poloclub/diffusiondb dataset. It is designed for transforming input images into a different style or representation.\'}', metadata={})]


 To build an intelligent system to recognize and identify objects in submitted pictures within the household, which includes furniture, electronics and ornaments.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'microsoft/table-transformer-structure-recognition\', \'api_call\': "pipeline(\'object-detection\', model=\'microsoft/table-transformer-structure-recognition\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'PubTables1M\', \'accuracy\': \'\'}, \'description\': \'Table Transformer (DETR) model trained on PubTables1M for detecting the structure (like rows, columns) in tables.\'}', metadata={})]


 Give me a solution to find out the license plate in the given car images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'License Plate Detection\', \'api_name\': \'keremberke/yolov5s-license-plate\', \'api_call\': "yolov5.load(\'keremberke/yolov5s-license-plate\')", \'api_arguments\': {\'img\': \'image url or path\', \'size\': \'image resize dimensions\', \'augment\': \'optional, test time augmentation\'}, \'python_environment_requirements\': \'pip install -U yolov5\', \'example_code\': [\'import yolov5\', "model = yolov5.load(\'keremberke/yolov5s-license-plate\')", \'model.conf = 0.25\', \'model.iou = 0.45\', \'model.agnostic = False\', \'model.multi_label = False\', \'model.max_det = 1000\', "img = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model(img, size=640)\', \'results = model(img, augment=True)\', \'predictions = results.pred[0]\', \'boxes = predictions[:, :4]\', \'scores = predictions[:, 4]\', \'categories = predictions[:, 5]\', \'results.show()\', "results.save(save_dir=\'results/\')"], \'performance\': {\'dataset\': \'keremberke/license-plate-object-detection\', \'accuracy\': 0.985}, \'description\': \'A YOLOv5 based license plate detection model trained on a custom dataset.\'}', metadata={})]


 I want to build a social media app for outdoor enthusiasts, and I need to identify objects related to outdoor activities in images. Come up with a proper API call to identify these objects.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8n-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-csgo-player-detection\')", \'api_arguments\': {\'image\': \'URL or local path to image\'}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8n-csgo-player-detection\')\\n\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\n\\nresults = model.predict(image)\\n\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.844}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players with supported labels: [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


 I want an AI that can separate objects in an image based on semantic segmentation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Semantic Segmentation\', \'api_name\': \'nvidia/segformer-b5-finetuned-cityscapes-1024-1024\', \'api_call\': "SegformerForSemanticSegmentation.from_pretrained(\'nvidia/segformer-b5-finetuned-cityscapes-1024-1024\')", \'api_arguments\': {\'images\': \'image\', \'return_tensors\': \'pt\'}, \'python_environment_requirements\': {\'packages\': [\'transformers\', \'PIL\', \'requests\']}, \'example_code\': \'from transformers import SegformerFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nfeature_extractor = SegformerFeatureExtractor.from_pretrained(nvidia/segformer-b5-finetuned-cityscapes-1024-1024)\\nmodel = SegformerForSemanticSegmentation.from_pretrained(nvidia/segformer-b5-finetuned-cityscapes-1024-1024)\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = feature_extractor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\\nlogits = outputs.logits\', \'performance\': {\'dataset\': \'CityScapes\', \'accuracy\': \'Not provided\'}, \'description\': \'SegFormer model fine-tuned on CityScapes at resolution 1024x1024. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository.\'}', metadata={})]


 Our image-processing service has to extract contextual information from images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Linaqruf/anything-v3.0\', \'api_call\': "Text2ImagePipeline(model=\'Linaqruf/anything-v3.0\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A text-to-image model that generates images from text descriptions.\'}', metadata={})]


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


 We are building a product which can identify birds in the images. Design the model which can help us segment the birds in an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrase-based utterance augmentation\', \'api_name\': \'prithivida/parrot_fluency_model\', \'api_call\': "pipeline(\'text-classification\', model=\'prithivida/parrot_fluency_model\')", \'api_arguments\': \'text\', \'python_environment_requirements\': [\'transformers\'], \'example_code\': "parrot(\'your input text\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'Parrot is a paraphrase-based utterance augmentation framework purpose-built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model.\'}', metadata={})]


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


 I am a drone maker that is building a navigation module for my drones. I need to sharpen the images captured from the drone in real-time.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Keras\', \'functionality\': \'Image Deblurring\', \'api_name\': \'google/maxim-s3-deblurring-gopro\', \'api_call\': "from_pretrained_keras(\'google/maxim-s3-deblurring-gopro\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'huggingface_hub\', \'PIL\', \'tensorflow\', \'numpy\', \'requests\'], \'example_code\': \'from huggingface_hub import from_pretrained_keras\\nfrom PIL import Image\\nimport tensorflow as tf\\nimport numpy as np\\nimport requests\\nurl = https://github.com/sayakpaul/maxim-tf/raw/main/images/Deblurring/input/1fromGOPR0950.png\\nimage = Image.open(requests.get(url, stream=True).raw)\\nimage = np.array(image)\\nimage = tf.convert_to_tensor(image)\\nimage = tf.image.resize(image, (256, 256))\\nmodel = from_pretrained_keras(google/maxim-s3-deblurring-gopro)\\npredictions = model.predict(tf.expand_dims(image, 0))\', \'performance\': {\'dataset\': \'GoPro\', \'accuracy\': {\'PSNR\': 32.86, \'SSIM\': 0.961}}, \'description\': \'MAXIM model pre-trained for image deblurring. It was introduced in the paper MAXIM: Multi-Axis MLP for Image Processing by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released in this repository.\'}', metadata={})]


 Develop a method to generate images of realistic-looking churches.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Unconditional Image Generation\', \'api_name\': \'google/ncsnpp-church-256\', \'api_call\': "DiffusionPipeline.from_pretrained(\'google/ncsnpp-church-256\')", \'api_arguments\': \'model_id\', \'python_environment_requirements\': \'diffusers\', \'example_code\': \'!pip install diffusers\\nfrom diffusers import DiffusionPipeline\\nmodel_id = google/ncsnpp-church-256\\nsde_ve = DiffusionPipeline.from_pretrained(model_id)\\nimage = sde_ve()[sample]\\nimage[0].save(sde_ve_generated_image.png)\', \'performance\': {\'dataset\': \'CIFAR-10\', \'accuracy\': {\'Inception_score\': 9.89, \'FID\': 2.2, \'likelihood\': 2.99}}, \'description\': \'Score-Based Generative Modeling through Stochastic Differential Equations (SDE) for unconditional image generation. This model achieves record-breaking performance on CIFAR-10 and can generate high fidelity images of size 1024 x 1024.\'}', metadata={})]


 Generate a classical image by using Diffusion Model\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Denoising Diffusion Probabilistic Models (DDPM)\', \'api_name\': \'google/ddpm-bedroom-256\', \'api_call\': "DDPMPipeline.from_pretrained(\'google/ddpm-bedroom-256\')", \'api_arguments\': \'None\', \'python_environment_requirements\': \'diffusers\', \'example_code\': \'!pip install diffusers\\nfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\\nmodel_id = google/ddpm-bedroom-256\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save(ddpm_generated_image.png)\', \'performance\': {\'dataset\': \'CIFAR10\', \'accuracy\': {\'Inception score\': 9.46, \'FID score\': 3.17}}, \'description\': \'We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.\'}', metadata={})]


 We need a picture that has a nostalgic look in high quality for the cover of our upcoming magazine.\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={})]


 We are an e-learning provider who wants to classify the content of a video lecture automatically.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'MCG-NJU/videomae-base-short-finetuned-kinetics\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')", \'api_arguments\': [\'video\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')\\nmodel = VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')\\ninputs = processor(video, return_tensors=\'pt\')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\'Predicted class:\', model.config.id2label[predicted_class_idx])", \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': {\'top-1\': 79.4, \'top-5\': 94.1}}, \'description\': \'VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository.\'}', metadata={})]


 We need to classify actions of athletes in sports videos. Can you help us to analyze and classify these videos?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'MCG-NJU/videomae-base-short-finetuned-kinetics\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')", \'api_arguments\': [\'video\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(16, 3, 224, 224))\\nprocessor = VideoMAEImageProcessor.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')\\nmodel = VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-base-short-finetuned-kinetics\')\\ninputs = processor(video, return_tensors=\'pt\')\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(\'Predicted class:\', model.config.id2label[predicted_class_idx])", \'performance\': {\'dataset\': \'Kinetics-400\', \'accuracy\': {\'top-1\': 79.4, \'top-5\': 94.1}}, \'description\': \'VideoMAE model pre-trained for 800 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository.\'}', metadata={})]


 We want to classify images supplied by our users into categories such as cats, dogs, birds, and more.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 Identify which smartphone brand is featured in an image and predict the intensity of luxury level.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Keras\', \'functionality\': \'Image Deblurring\', \'api_name\': \'google/maxim-s3-deblurring-gopro\', \'api_call\': "from_pretrained_keras(\'google/maxim-s3-deblurring-gopro\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'huggingface_hub\', \'PIL\', \'tensorflow\', \'numpy\', \'requests\'], \'example_code\': \'from huggingface_hub import from_pretrained_keras\\nfrom PIL import Image\\nimport tensorflow as tf\\nimport numpy as np\\nimport requests\\nurl = https://github.com/sayakpaul/maxim-tf/raw/main/images/Deblurring/input/1fromGOPR0950.png\\nimage = Image.open(requests.get(url, stream=True).raw)\\nimage = np.array(image)\\nimage = tf.convert_to_tensor(image)\\nimage = tf.image.resize(image, (256, 256))\\nmodel = from_pretrained_keras(google/maxim-s3-deblurring-gopro)\\npredictions = model.predict(tf.expand_dims(image, 0))\', \'performance\': {\'dataset\': \'GoPro\', \'accuracy\': {\'PSNR\': 32.86, \'SSIM\': 0.961}}, \'description\': \'MAXIM model pre-trained for image deblurring. It was introduced in the paper MAXIM: Multi-Axis MLP for Image Processing by Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li and first released in this repository.\'}', metadata={})]


 A Chinese social media company has approached us for help in content moderation. We recorded an API to classify images which don't belong to suitable content.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


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


 Our e-commerce platform needs automatic classification of product images without retraining. Develop a classifier for the e-commerce platform.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 We are developing a customer support platform for our telecommunication company in Spain. We want to know if they are happy or unhappy with our services, based on the content of their message.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 I need a function to find the most relevent passage given a question and several candidate passages.\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={})]


 As a community manager, I would like to monitor my forum's comment section for toxic or harmful content. I want to find a solution that can flag these types of comments automatically, so I can address them promptly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'martin-ha/toxic-comment-model\', \'api_call\': "pipeline(model=\'martin-ha/toxic-comment-model\')", \'api_arguments\': {\'model_path\': \'martin-ha/toxic-comment-model\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline\\nmodel_path = martin-ha/toxic-comment-model\\ntokenizer = AutoTokenizer.from_pretrained(model_path)\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_path)\\npipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)\\nprint(pipeline(\'This is a test text.\'))", \'performance\': {\'dataset\': \'held-out test set\', \'accuracy\': 0.94, \'f1-score\': 0.59}, \'description\': \'This model is a fine-tuned version of the DistilBERT model to classify toxic comments.\'}', metadata={})]


 I am making a keyword search engine that ranks text passages based on their importance regarding a given keyword.\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={})]


 Identify the type of emotion in a movie review.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Emotion Classification\', \'api_name\': \'j-hartmann/emotion-english-distilroberta-base\', \'api_call\': "pipeline(\'text-classification\', model=\'j-hartmann/emotion-english-distilroberta-base\', return_all_scores=True)", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': {\'transformers\': \'latest\'}, \'example_code\': \'from transformers import pipeline\\nclassifier = pipeline(text-classification, model=j-hartmann/emotion-english-distilroberta-base, return_all_scores=True)\\nclassifier(I love this!)\', \'performance\': {\'dataset\': \'Balanced subset from 6 diverse datasets\', \'accuracy\': \'66%\'}, \'description\': "This model classifies emotions in English text data. It predicts Ekman\'s 6 basic emotions, plus a neutral class: anger, disgust, fear, joy, neutral, sadness, and surprise. The model is a fine-tuned checkpoint of DistilRoBERTa-base."}', metadata={})]