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


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


 We are an online platform that offers various Chinese language courses for students. Now we need to autoplay a fill-in-the-blank video game for our users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 We want to determine the similarity between two sentences, \"I love going to the park\" and \"My favorite activity is visiting the park\".\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={})]


 We have an audiobook app in the Chinese language, and we want to convert the Chinese text to speech.\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 want to build an audiobook service. We need to convert text to speech for the user to listen.\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={})]


 I want to convert English speech into different language speech. I have some audio in English language and I would like to convert that into Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Marathi_Male_TTS\', \'api_call\': "api.load(\'ESPnet/espnet_model_zoo:SYSPIN/Marathi_Male_TTS\').", \'api_arguments\': [], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Marathi Male Text-to-Speech model using ESPnet framework.\'}', metadata={})]


 We are trying to transcribe audios. Can you help with giving us an ASR model?\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 blog platform accepts voice notes, and we want to transcribe these notes to text.\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_ljspeech_vits\', \'api_call\': "pipeline(\'text-to-speech\', model=\'espnet/kan-bayashi_ljspeech_vits\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'espnet/kan-bayashi_ljspeech_vits\'); tts(\'Hello World\')", \'performance\': {\'dataset\': \'ljspeech\', \'accuracy\': \'Not mentioned\'}, \'description\': \'A Text-to-Speech model trained on the ljspeech dataset using the ESPnet toolkit. This model can be used to convert text input into synthesized speech.\'}', metadata={})]


 Create a program to separate music and vocals from an audio file using a pretrained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'SpeechBrain\', \'functionality\': \'Audio Source Separation\', \'api_name\': \'sepformer-wsj02mix\', \'api_call\': "separator.from_hparams(source=\'speechbrain/sepformer-wsj02mix\')", \'api_arguments\': [\'path\'], \'python_environment_requirements\': [\'pip install speechbrain\'], \'example_code\': [\'from speechbrain.pretrained import SepformerSeparation as separator\', \'import torchaudio\', "model = separator.from_hparams(source=speechbrain/sepformer-wsj02mix, savedir=\'pretrained_models/sepformer-wsj02mix\')", "est_sources = model.separate_file(path=\'speechbrain/sepformer-wsj02mix/test_mixture.wav\')", \'torchaudio.save(source1hat.wav, est_sources[:, :, 0].detach().cpu(), 8000)\', \'torchaudio.save(source2hat.wav, est_sources[:, :, 1].detach().cpu(), 8000)\'], \'performance\': {\'dataset\': \'WSJ0-2Mix\', \'accuracy\': \'22.4 dB\'}, \'description\': \'This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset.\'}', metadata={})]


 \nCreate an application that automatically separates vocals from a song for karaoke nights.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'SpeechBrain\', \'functionality\': \'Audio Source Separation\', \'api_name\': \'sepformer-wsj02mix\', \'api_call\': "separator.from_hparams(source=\'speechbrain/sepformer-wsj02mix\')", \'api_arguments\': [\'path\'], \'python_environment_requirements\': [\'pip install speechbrain\'], \'example_code\': [\'from speechbrain.pretrained import SepformerSeparation as separator\', \'import torchaudio\', "model = separator.from_hparams(source=speechbrain/sepformer-wsj02mix, savedir=\'pretrained_models/sepformer-wsj02mix\')", "est_sources = model.separate_file(path=\'speechbrain/sepformer-wsj02mix/test_mixture.wav\')", \'torchaudio.save(source1hat.wav, est_sources[:, :, 0].detach().cpu(), 8000)\', \'torchaudio.save(source2hat.wav, est_sources[:, :, 1].detach().cpu(), 8000)\'], \'performance\': {\'dataset\': \'WSJ0-2Mix\', \'accuracy\': \'22.4 dB\'}, \'description\': \'This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset.\'}', metadata={})]


 One of our clients need high recognition capability of denoising audio streams for improving sound quality.\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={})]


 A podcast company reached out to us wanting to change their speaker's voice in a recorded podcast.\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={})]


 Our research team is focused on the analysis and separation of complex audio recordings. We need a model to be able to separate speaker voices from mixed sound.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'SpeechBrain\', \'functionality\': \'Audio Source Separation\', \'api_name\': \'sepformer-wsj02mix\', \'api_call\': "separator.from_hparams(source=\'speechbrain/sepformer-wsj02mix\')", \'api_arguments\': [\'path\'], \'python_environment_requirements\': [\'pip install speechbrain\'], \'example_code\': [\'from speechbrain.pretrained import SepformerSeparation as separator\', \'import torchaudio\', "model = separator.from_hparams(source=speechbrain/sepformer-wsj02mix, savedir=\'pretrained_models/sepformer-wsj02mix\')", "est_sources = model.separate_file(path=\'speechbrain/sepformer-wsj02mix/test_mixture.wav\')", \'torchaudio.save(source1hat.wav, est_sources[:, :, 0].detach().cpu(), 8000)\', \'torchaudio.save(source2hat.wav, est_sources[:, :, 1].detach().cpu(), 8000)\'], \'performance\': {\'dataset\': \'WSJ0-2Mix\', \'accuracy\': \'22.4 dB\'}, \'description\': \'This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WSJ0-2Mix dataset.\'}', metadata={})]


 A Romanian-speaking person wants to communicate with an English-speaking friend over the phone using our platform. Please provide a method to translate their speech in real-time.\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={})]


 Implement an application that recognizes languages by analyzing an online audio file.\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={})]


 A toy company wants to use your software to recognize spoken numbers (0-9) in English by young children for an interactive game. Please create an identifier.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'imdanboy/jets\', \'api_call\': "pipeline(\'text-to-speech\', model=\'imdanboy/jets\')", \'api_arguments\': None, \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'imdanboy/jets\'); tts(\'Hello world\')", \'performance\': {\'dataset\': \'ljspeech\', \'accuracy\': None}, \'description\': \'This model was trained by imdanboy using ljspeech recipe in espnet.\'}', metadata={})]


 Develop an automated podcast recommender system to suggest content that matches user's taste based on their favorite podcast speakers.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'pyannote.audio\', \'functionality\': \'Speaker Diarization\', \'api_name\': \'pyannote/speaker-diarization\', \'api_call\': "Pipeline.from_pretrained(\'pyannote/speaker-diarization@2.1\', use_auth_token=\'ACCESS_TOKEN_GOES_HERE\')", \'api_arguments\': {\'num_speakers\': \'int (optional)\', \'min_speakers\': \'int (optional)\', \'max_speakers\': \'int (optional)\'}, \'python_environment_requirements\': \'pyannote.audio 2.1.1\', \'example_code\': [\'from pyannote.audio import Pipeline\', \'pipeline = Pipeline.from_pretrained(pyannote/speaker-diarization@2.1, use_auth_token=ACCESS_TOKEN_GOES_HERE)\', \'diarization = pipeline(audio.wav)\', \'with open(audio.rttm, w) as rttm:\', \'  diarization.write_rttm(rttm)\'], \'performance\': {\'dataset\': \'ami\', \'accuracy\': {\'DER%\': \'18.91\', \'FA%\': \'4.48\', \'Miss%\': \'9.51\', \'Conf%\': \'4.91\'}}, \'description\': \'This API provides an automatic speaker diarization pipeline using the pyannote.audio framework. It can process audio files and output speaker diarization results in RTTM format. The pipeline can also handle cases where the number of speakers is known in advance or when providing lower and/or upper bounds on the number of speakers.\'}', metadata={})]


 We are developing an app that can help mediate virtual therapy sessions. We need a feature that can detect emotions in the voice of the user.\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 are engineering a green consulting firm. We need to predict carbon emissions with input features from customer's dataset.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'kochetkovIT/autotrain-ironhack-49741119788\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'kochetkovIT/autotrain-data-ironhack\', \'accuracy\': {\'Loss\': 2.603, \'R2\': 0.013, \'MSE\': 6.776, \'MAE\': 1.666, \'RMSLE\': 0.502}}, \'description\': \'A tabular regression model trained using AutoTrain to predict carbon emissions (in grams) with an R2 score of 0.013.\'}', metadata={})]


 Develop a system that can detect voices in a podcast to find out if guests are speaking or not. \n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'pyannote.audio\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'pyannote/voice-activity-detection\', \'api_call\': "Pipeline.from_pretrained(\'pyannote/voice-activity-detection\')", \'api_arguments\': [\'audio.wav\'], \'python_environment_requirements\': [\'pyannote.audio 2.1\'], \'example_code\': \'from pyannote.audio import Pipeline\\npipeline = Pipeline.from_pretrained(pyannote/voice-activity-detection, use_auth_token=ACCESS_TOKEN_GOES_HERE)\\noutput = pipeline(audio.wav)\\nfor speech in output.get_timeline().support():\\n  # active speech between speech.start and speech.end\', \'performance\': {\'dataset\': \'ami\', \'accuracy\': \'Not specified\'}, \'description\': \'A pretrained voice activity detection pipeline that detects active speech in audio files.\'}', metadata={})]


 Develop a machine learning product to help home buyers predict house prices in the US.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Joblib\', \'functionality\': \'Single Column Regression\', \'api_name\': \'jwan2021/autotrain-us-housing-prices-1771761512\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\', \'json\'], \'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\': \'jwan2021/autotrain-data-us-housing-prices\', \'accuracy\': {\'Loss\': 122809.223, \'R2\': 0.884, \'MSE\': 15082105200.447, \'MAE\': 95586.887, \'RMSLE\': 0.13}}, \'description\': \'A model trained using AutoTrain for predicting US housing prices with single column regression. The model is based on the jwan2021/autotrain-data-us-housing-prices dataset and has a CO2 Emissions of 50.5369 grams.\'}', metadata={})]


 A game developer needs to predict the HP of a new Pokemon character based on several attributes. Provide the code to make that prediction.\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={})]


 Let's create a smart agent that can learn to play CartPole-v0 using reinforcement learning.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'CartPole-v1\', \'api_name\': \'dqn-CartPole-v1\', \'api_call\': "load_from_hub(repo_id=\'sb3/dqn-CartPole-v1\',filename=\'{MODEL FILENAME}.zip\',)", \'api_arguments\': [\'algo\', \'env\', \'logs\'], \'python_environment_requirements\': [\'rl_zoo3\', \'stable-baselines3\', \'stable-baselines3-contrib\'], \'example_code\': \'python train.py --algo dqn --env CartPole-v1 -f logs/\', \'performance\': {\'dataset\': \'CartPole-v1\', \'accuracy\': \'500.00 +/- 0.00\'}, \'description\': \'This is a trained model of a DQN agent playing CartPole-v1 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.\'}', metadata={})]


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


 We want to build a smart-cars training system for our team. I need you to solve the gym environment 'MountainCar-v0'.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'MountainCar-v0\', \'api_name\': \'sb3/dqn-MountainCar-v0\', \'api_call\': "load_from_hub(repo_id=\'sb3/dqn-MountainCar-v0\',filename=\'{MODEL FILENAME}.zip\',)", \'api_arguments\': [\'algo\', \'env\', \'f\'], \'python_environment_requirements\': [\'RL Zoo\', \'SB3\', \'SB3 Contrib\'], \'example_code\': [\'python -m rl_zoo3.load_from_hub --algo dqn --env MountainCar-v0 -orga sb3 -f logs/\', \'python train.py --algo dqn --env MountainCar-v0 -f logs/\', \'python -m rl_zoo3.push_to_hub --algo dqn --env MountainCar-v0 -f logs/ -orga sb3\'], \'performance\': {\'dataset\': \'MountainCar-v0\', \'accuracy\': \'-103.40 +/- 7.49\'}, \'description\': \'This is a trained model of a DQN agent playing MountainCar-v0 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.\'}', metadata={})]


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


 An important project is under development, and we need to know the positional relations between various keywords of a given medical text.\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={})]


 Build a system for detecting hate speech from social media comments in Korean.\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 team is working on a recommendation system for a news article app. We should understand the semantic similarity of various texts.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'google/pegasus-newsroom\', \'api_call\': "pipeline(\'summarization\', model=\'google/pegasus-newsroom\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'This model can be loaded on the Inference API on-demand.\', \'performance\': {\'dataset\': \'newsroom\', \'accuracy\': \'45.98/34.20/42.18\'}, \'description\': \'PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. The model is trained on both C4 and HugeNews datasets and is designed for summarization tasks.\'}', metadata={})]


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


 I am an artist who wants to create a new piece of artwork based on the prompt \"A futuristic city under the ocean\".\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'dreamlike-art/dreamlike-anime-1.0\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'dreamlike-art/dreamlike-anime-1.0\', torch_dtype=torch.float16)(prompt, negative_prompt=negative_prompt)", \'api_arguments\': [\'prompt\', \'negative_prompt\'], \'python_environment_requirements\': [\'diffusers\', \'torch\'], \'example_code\': "from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = dreamlike-art/dreamlike-anime-1.0\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = anime, masterpiece, high quality, 1girl, solo, long hair, looking at viewer, blush, smile, bangs, blue eyes, skirt, medium breasts, iridescent, gradient, colorful, besides a cottage, in the country\\nnegative_prompt = \'simple background, duplicate, retro style, low quality, lowest quality, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013, bad anatomy, bad proportions, extra digits, lowres, username, artist name, error, duplicate, watermark, signature, text, extra digit, fewer digits, worst quality, jpeg artifacts, blurry\'\\nimage = pipe(prompt, negative_prompt=negative_prompt).images[0]\\nimage.save(./result.jpg)", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': "Dreamlike Anime 1.0 is a high quality anime model, made by dreamlike.art. It can be used to generate anime-style images based on text prompts. The model is trained on 768x768px images and works best with prompts that include \'photo anime, masterpiece, high quality, absurdres\'. It can be used with the Stable Diffusion Pipeline from the diffusers library."}', metadata={})]


 Create a character-like artwork image based on the phrase 'anime-style girl with a guitar'.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'dreamlike-art/dreamlike-anime-1.0\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'dreamlike-art/dreamlike-anime-1.0\', torch_dtype=torch.float16)(prompt, negative_prompt=negative_prompt)", \'api_arguments\': [\'prompt\', \'negative_prompt\'], \'python_environment_requirements\': [\'diffusers\', \'torch\'], \'example_code\': "from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = dreamlike-art/dreamlike-anime-1.0\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = anime, masterpiece, high quality, 1girl, solo, long hair, looking at viewer, blush, smile, bangs, blue eyes, skirt, medium breasts, iridescent, gradient, colorful, besides a cottage, in the country\\nnegative_prompt = \'simple background, duplicate, retro style, low quality, lowest quality, 1980s, 1990s, 2000s, 2005 2006 2007 2008 2009 2010 2011 2012 2013, bad anatomy, bad proportions, extra digits, lowres, username, artist name, error, duplicate, watermark, signature, text, extra digit, fewer digits, worst quality, jpeg artifacts, blurry\'\\nimage = pipe(prompt, negative_prompt=negative_prompt).images[0]\\nimage.save(./result.jpg)", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': "Dreamlike Anime 1.0 is a high quality anime model, made by dreamlike.art. It can be used to generate anime-style images based on text prompts. The model is trained on 768x768px images and works best with prompts that include \'photo anime, masterpiece, high quality, absurdres\'. It can be used with the Stable Diffusion Pipeline from the diffusers library."}', metadata={})]


 We are designing an application that can generate images based on textual descriptions. We need to load a model capable of performing text-to-image conversion.\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={})]


 A food delivery app wants to help users understand ingredients in the food item by analyzing the images. We need a solution to process the food images and give textual information about the items.\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={})]


 The company wants to create a social media application like Pinterest that generates captions for users' images. Show us how to create this functionality.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Lykon/DreamShaper\', \'api_call\': "pipeline(\'text-to-image\', model=Lykon/DreamShaper)", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': \'https://huggingface.co/spaces/Lykon/DreamShaper-webui\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Dream Shaper is a text-to-image model that generates artistic images based on the given input text. Read more about this model here: https://civitai.com/models/4384/dreamshaper\'}', metadata={})]


 We are an analytics company that needs help in extracting data tables from plots and charts efficiently.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'kochetkovIT/autotrain-ironhack-49741119788\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'kochetkovIT/autotrain-data-ironhack\', \'accuracy\': {\'Loss\': 2.603, \'R2\': 0.013, \'MSE\': 6.776, \'MAE\': 1.666, \'RMSLE\': 0.502}}, \'description\': \'A tabular regression model trained using AutoTrain to predict carbon emissions (in grams) with an R2 score of 0.013.\'}', metadata={})]


 Our marketing team wants a video commercial for our product. They are looking for a video with a couple sitting in a cafe and laughing while using our product.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 A company is having an ad campaign with a superhero theme. They want to generate a video of Spiderman surfing to showcase as a social media ad.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 We are developing a solution where we can visually detect the medical conditions of patients. Can you write code to determine the condition based on the image provided?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\', \'api_call\': "pipeline(\'image-classification\', \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\')", \'api_arguments\': {\'model_name\': \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\'}, \'python_environment_requirements\': {\'transformers\': \'4.28.1\', \'pytorch\': \'2.0.0+cu118\', \'datasets\': \'2.11.0\', \'tokenizers\': \'0.13.3\'}, \'example_code\': "from transformers import pipeline\\nimage_classifier = pipeline(\'image-classification\', \'martinezomg/vit-base-patch16-224-diabetic-retinopathy\')\\nresult = image_classifier(\'path/to/image.jpg\')", \'performance\': {\'dataset\': \'None\', \'accuracy\': 0.7744}, \'description\': \'This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It is designed for image classification tasks, specifically for diabetic retinopathy detection.\'}', metadata={})]


 Help me to build a visual-question-answering model that takes a question and an image as input and returns an answer based on the image.\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={})]


 Implement an optical text recognition system for documents that can answer a simple question about the document's content.\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={})]


 Our company deals with insurance claims. We need a smart assistant who can retrieve information from documents, especially invoices, such as total amount, date of invoice, and name of the service provider.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'layoutlm-invoices\', \'api_call\': "AutoModelForDocumentQuestionAnswering.from_pretrained(\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "nlp(question=\'What is the total amount?\', context=\'your_invoice_text\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'Not provided\'}, \'description\': \'A fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens, this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


 We have an AI-powered document management system that needs to answer questions based on the content of a given document.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 Take a look at this document image and tell me the answer to my question: \"What is the total amount due?\".\n###Input: {\"image_url\": \"https://example.com/document_invoice.jpg\", \"question\": \"What is the total amount due?\"}\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={})]


 I am designing a parking spot detector for a car parking management solution. I want the model to estimage the depth of the car park.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'glpn-nyu-finetuned-diode-221215-092352\', \'api_call\': "AutoModel.from_pretrained(\'sayakpaul/glpn-nyu-finetuned-diode-221215-092352\')", \'api_arguments\': {}, \'python_environment_requirements\': {\'huggingface_transformers\': \'4.13.0\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'DIODE\', \'accuracy\': \'\'}, \'description\': \'A depth estimation model fine-tuned on the DIODE dataset.\'}', metadata={})]


 We are researching multi-view 3D scanning, and we would like to improve our depth estimation with a pretrained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Depth Estimation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'dpt-large-redesign\', \'api_call\': "AutoModelForDepthEstimation.from_pretrained(\'nielsr/dpt-large-redesign\')", \'api_arguments\': [], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A depth estimation model based on the DPT architecture.\'}', metadata={})]


 I am building an app to detect objects in an image from a URL. How can I experiment with object detection models using transformers?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'facebook/detr-resnet-101\', \'api_call\': "DetrForObjectDetection.from_pretrained(\'facebook/detr-resnet-101\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'transformers\', \'torch\', \'PIL\', \'requests\'], \'example_code\': \'from transformers import DetrImageProcessor, DetrForObjectDetection\\nimport torch\\nfrom PIL import Image\\nimport requests\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nprocessor = DetrImageProcessor.from_pretrained(facebook/detr-resnet-101)\\nmodel = DetrForObjectDetection.from_pretrained(facebook/detr-resnet-101)\\ninputs = processor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\', \'performance\': {\'dataset\': \'COCO 2017\', \'accuracy\': \'43.5 AP\'}, \'description\': \'DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository.\'}', metadata={})]


 I have a picture of my backyard, but I am not sure if a cat sneaked in. Can you help me find out if it is a cat in the picture?\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-ema-cat-256\', \'api_call\': "DDPMPipeline.from_pretrained(\'google/ddpm-ema-cat-256\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'!pip install diffusers\'], \'example_code\': \'from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\\nmodel_id = google/ddpm-ema-cat-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\': \'Denoising Diffusion Probabilistic Models (DDPM) is a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. It can generate high-quality images, and supports different noise schedulers such as scheduling_ddpm, scheduling_ddim, and scheduling_pndm. On the unconditional CIFAR10 dataset, it achieves an Inception score of 9.46 and a state-of-the-art FID score of 3.17.\'}', metadata={})]


 Our hospital needs to analyze digital blood samples in order to detect and count platelets, red blood cells, and white blood cells.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Blood Cell Detection\', \'api_name\': \'keremberke/yolov8n-blood-cell-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-blood-cell-detection\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': \'ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8n-blood-cell-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\': \'blood-cell-object-detection\', \'accuracy\': 0.893}, \'description\': \'This model detects blood cells in images, specifically Platelets, RBC, and WBC. It is based on the YOLOv8 architecture and trained on the blood-cell-object-detection dataset.\'}', metadata={})]


 Develop a script that extracts a table from a given document and then visualize the results.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-large-finetuned-wikisql-supervised\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-large-finetuned-wikisql-supervised\')", \'api_arguments\': {\'model\': \'google/tapas-large-finetuned-wikisql-supervised\'}, \'python_environment_requirements\': [\'transformers\', \'torch\', \'tensorflow\'], \'example_code\': "from transformers import pipeline\\n\\nqa_pipeline = pipeline(\'table-question-answering\', model=\'google/tapas-large-finetuned-wikisql-supervised\')\\n\\nresult = qa_pipeline(question=\'What is the capital of France?\', table=table)\\nprint(result)", \'performance\': {\'dataset\': \'wikisql\', \'accuracy\': \'Not provided\'}, \'description\': \'TAPAS is a BERT-like transformers model pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion. It can be used for answering questions related to a table.\'}', metadata={})]


 We are now working on an architectural image generation project to generate different images of architecture.\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\': \'Apocalypse-19/shoe-generator\', \'api_call\': "DDPMPipeline.from_pretrained(\'Apocalypse-19/shoe-generator\')", \'api_arguments\': [], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'Apocalypse-19/shoe-generator\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'custom dataset\', \'accuracy\': \'128x128 resolution\'}, \'description\': \'This model is a diffusion model for unconditional image generation of shoes trained on a custom dataset at 128x128 resolution.\'}', metadata={})]


 We are designing a book cover where the book is about love and roses. We have the idea of \"A head full of roses.\"\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={})]


 Help me generate a realistic bedroom interior image that can be used as reference for a 3D model being created for a virtual reality game.\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={})]


 You feel lonely and need to generate a picture of a butterfly. How would you proceed?\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\': \'ntrant7/sd-class-butterflies-32\', \'api_call\': "DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')", \'api_arguments\': [], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'Not specified\', \'accuracy\': \'Not specified\'}, \'description\': \'This model is a diffusion model for unconditional image generation of cute butterflies.\'}', metadata={})]


 We need an AI-generated insect image for our biology article about African habitats.\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\': \'ntrant7/sd-class-butterflies-32\', \'api_call\': "DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')", \'api_arguments\': [], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'ntrant7/sd-class-butterflies-32\')\\nimage = pipeline().images[0]\\nimage", \'performance\': {\'dataset\': \'Not specified\', \'accuracy\': \'Not specified\'}, \'description\': \'This model is a diffusion model for unconditional image generation of cute butterflies.\'}', metadata={})]


 Our client is involved in a sports organization, and they require a solution to classify sports videos efficiently.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'facebook/timesformer-base-finetuned-ssv2\', \'api_call\': "TimesformerForVideoClassification.from_pretrained(\'facebook/timesformer-base-finetuned-ssv2\')", \'api_arguments\': [\'images\', \'return_tensors\'], \'python_environment_requirements\': [\'transformers\', \'numpy\', \'torch\'], \'example_code\': \'from transformers import AutoImageProcessor, TimesformerForVideoClassification\\nimport numpy as np\\nimport torch\\nvideo = list(np.random.randn(8, 3, 224, 224))\\nprocessor = AutoImageProcessor.from_pretrained(facebook/timesformer-base-finetuned-ssv2)\\nmodel = TimesformerForVideoClassification.from_pretrained(facebook/timesformer-base-finetuned-ssv2)\\ninputs = processor(images=video, return_tensors=pt)\\nwith torch.no_grad():\\n outputs = model(**inputs)\\n logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(Predicted class:, model.config.id2label[predicted_class_idx])\', \'performance\': {\'dataset\': \'Something Something v2\', \'accuracy\': \'Not provided\'}, \'description\': \'TimeSformer model pre-trained on Something Something v2. It was introduced in the paper TimeSformer: Is Space-Time Attention All You Need for Video Understanding? by Tong et al. and first released in this repository.\'}', metadata={})]


 We need to create a content filter for images submitted by users in our online community. We want to detect and filter out adult content and offensive images.\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 an animal rescue organization, and we are looking for a way to automatically identify if images uploaded to our site contain cats, dogs, or birds.\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={})]


 Create a machine learning-based image recognition tool that can identify whether an animal in an image is a cat or a dog.\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={})]


 I want to find out whether a given review is positive or negative. I need an API that can help me with sentiment analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'bert-base-multilingual-uncased-sentiment\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'nlptown/bert-base-multilingual-uncased-sentiment\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "result = sentiment_pipeline(\'I love this product!\')", \'performance\': {\'dataset\': [{\'language\': \'English\', \'accuracy\': {\'exact\': \'67%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Dutch\', \'accuracy\': {\'exact\': \'57%\', \'off-by-1\': \'93%\'}}, {\'language\': \'German\', \'accuracy\': {\'exact\': \'61%\', \'off-by-1\': \'94%\'}}, {\'language\': \'French\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'94%\'}}, {\'language\': \'Italian\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Spanish\', \'accuracy\': {\'exact\': \'58%\', \'off-by-1\': \'95%\'}}]}, \'description\': \'This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).\'}', metadata={})]


 I am a traveler and I want to know the best time to visit a Chinese historical site. Take a look at an image and tell me whether it is a good time to visit the site or not.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'OFA-Sys/chinese-clip-vit-large-patch14-336px\', \'api_call\': "ChineseCLIPModel.from_pretrained(\'OFA-Sys/chinese-clip-vit-large-patch14-336px\')", \'api_arguments\': {\'images\': \'image\', \'text\': \'texts\', \'return_tensors\': \'pt\', \'padding\': \'True\'}, \'python_environment_requirements\': [\'PIL\', \'requests\', \'transformers\'], \'example_code\': \'from PIL import Image\\nimport requests\\nfrom transformers import ChineseCLIPProcessor, ChineseCLIPModel\\nmodel = ChineseCLIPModel.from_pretrained(OFA-Sys/chinese-clip-vit-large-patch14-336px)\\nprocessor = ChineseCLIPProcessor.from_pretrained(OFA-Sys/chinese-clip-vit-large-patch14-336px)\\nurl = https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ntexts = []\\ninputs = processor(images=image, return_tensors=pt)\\nimage_features = model.get_image_features(**inputs)\\nimage_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True)\\ninputs = processor(text=texts, padding=True, return_tensors=pt)\\ntext_features = model.get_text_features(**inputs)\\ntext_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True)\\ninputs = processor(text=texts, 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\': {\'CIFAR10\': 96.0, \'CIFAR100\': 79.75, \'DTD\': 51.2, \'EuroSAT\': 52.0, \'FER\': 55.1, \'FGVC\': 26.2, \'KITTI\': 49.9, \'MNIST\': 79.4, \'PC\': 63.5, \'VOC\': 84.9}, \'accuracy\': \'various\'}, \'description\': \'Chinese CLIP is a simple implementation of CLIP on a large-scale dataset of around 200 million Chinese image-text pairs. It uses ViT-L/14@336px as the image encoder and RoBERTa-wwm-base as the text encoder.\'}', metadata={})]


 Organizing a special event and want to identify the place where street pictures were taken for invitations.\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 client wants us to monitor consumer sentiment on their e-commerce platform. We must identify low-rated product reviews in six languages: English, Dutch, German, French, Italian, and Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'bert-base-multilingual-uncased-sentiment\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'nlptown/bert-base-multilingual-uncased-sentiment\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "result = sentiment_pipeline(\'I love this product!\')", \'performance\': {\'dataset\': [{\'language\': \'English\', \'accuracy\': {\'exact\': \'67%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Dutch\', \'accuracy\': {\'exact\': \'57%\', \'off-by-1\': \'93%\'}}, {\'language\': \'German\', \'accuracy\': {\'exact\': \'61%\', \'off-by-1\': \'94%\'}}, {\'language\': \'French\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'94%\'}}, {\'language\': \'Italian\', \'accuracy\': {\'exact\': \'59%\', \'off-by-1\': \'95%\'}}, {\'language\': \'Spanish\', \'accuracy\': {\'exact\': \'58%\', \'off-by-1\': \'95%\'}}]}, \'description\': \'This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).\'}', metadata={})]


 Write a script for an online forum moderator that will help them to detect gibberish text in a post.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'madhurjindal/autonlp-Gibberish-Detector-492513457\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'madhurjindal/autonlp-Gibberish-Detector-492513457\')", \'api_arguments\': {\'inputs\': \'I love AutoNLP\'}, \'python_environment_requirements\': {\'transformers\': \'AutoModelForSequenceClassification\', \'AutoTokenizer\': \'from_pretrained\'}, \'example_code\': \'from transformers import AutoModelForSequenceClassification, AutoTokenizer\\nmodel = AutoModelForSequenceClassification.from_pretrained(madhurjindal/autonlp-Gibberish-Detector-492513457, use_auth_token=True)\\ntokenizer = AutoTokenizer.from_pretrained(madhurjindal/autonlp-Gibberish-Detector-492513457, use_auth_token=True)\\ninputs = tokenizer(I love AutoNLP, return_tensors=pt)\\noutputs = model(**inputs)\', \'performance\': {\'dataset\': \'madhurjindal/autonlp-data-Gibberish-Detector\', \'accuracy\': 0.9735624586913417}, \'description\': \'A multi-class text classification model for detecting gibberish text. Trained using AutoNLP and DistilBERT.\'}', metadata={})]


 As a restaurant owner, I want to know if the total revenue for last week met our target revenue.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


 We are a sports analysis company that processes data from spreadsheets with game statistics. We need to identify the player who has scored the maximum goals in a given match.\n###Input: \"What player scored the most goals?\", \"Player,Goals\\nA,2\\nB,3\\nC,1\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


 A manager in our company requires an aggregated answer of the highest and lowest sales numbers for a given period to evaluate the perfomance.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'tuner007/pegasus_summarizer\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'tuner007/pegasus_summarizer\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'pip install sentencepiece\'], \'example_code\': "context = \\nIndia wicket-keeper batsman Rishabh Pant has said someone from the crowd threw a ball on pacer Mohammed Siraj while he was fielding in the ongoing third Test against England on Wednesday. Pant revealed the incident made India skipper Virat Kohli upset. I think, somebody threw a ball inside, at Siraj, so he [Kohli] was upset, said Pant in a virtual press conference after the close of the first day\'s play.You can say whatever you want to chant, but don\'t throw things at the fielders and all those things. It is not good for cricket, I guess, he added.In the third session of the opening day of the third Test, a section of spectators seemed to have asked Siraj the score of the match to tease the pacer. The India pacer however came with a brilliant reply as he gestured 1-0 (India leading the Test series) towards the crowd.Earlier this month, during the second Test match, there was some bad crowd behaviour on a show as some unruly fans threw champagne corks at India batsman KL Rahul.Kohli also intervened and he was seen gesturing towards the opening batsman to know more about the incident. An over later, the TV visuals showed that many champagne corks were thrown inside the playing field, and the Indian players were visibly left frustrated.Coming back to the game, after bundling out India for 78, openers Rory Burns and Haseeb Hameed ensured that England took the honours on the opening day of the ongoing third Test.At stumps, England\'s score reads 120/0 and the hosts have extended their lead to 42 runs. For the Three Lions, Burns (52) and Hameed (60) are currently unbeaten at the crease.Talking about the pitch on opening day, Pant said, They took the heavy roller, the wicket was much more settled down, and they batted nicely also, he said. But when we batted, the wicket was slightly soft, and they bowled in good areas, but we could have applied [ourselves] much better.Both England batsmen managed to see off the final session and the hosts concluded the opening day with all ten wickets intact, extending the lead to 42.(ANI)\\n\\nget_response(context)", \'performance\': {\'dataset\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 36.604, \'ROUGE-2\': 14.64, \'ROUGE-L\': 23.884, \'ROUGE-LSUM\': 32.902, \'loss\': 2.576, \'gen_len\': 76.398}}, \'description\': \'PEGASUS fine-tuned for summarization\'}', metadata={})]


 A doctor needs to find medical information in a large document. We are building an AI model to help them extract answers to their 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={})]


 We are building a news classification system, and for Spanish articles, we have to categorize them into different sections.\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={})]


 In my project, I need to classify news articles into their respective categories, such as technology, sports, and politics, without providing any labeled training data.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


 I have a Catalan language text and I need to publish it as a blog article in Spanish, so I want to translate my Catalan text into Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'mrm8488/bert2bert_shared-spanish-finetuned-summarization\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'mrm8488/bert2bert_shared-spanish-finetuned-summarization\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'def generate_summary(text):\\n  inputs = tokenizer([text], padding=max_length, truncation=True, max_length=512, return_tensors=pt)\\n  input_ids = inputs.input_ids.to(device)\\n  attention_mask = inputs.attention_mask.to(device)\\n  output = model.generate(input_ids, attention_mask=attention_mask)\\n  return tokenizer.decode(output[0], skip_special_tokens=True)\\ntext = Your text here...\\ngenerate_summary(text)\', \'performance\': {\'dataset\': \'mlsum\', \'accuracy\': {\'Rouge1\': 26.24, \'Rouge2\': 8.9, \'RougeL\': 21.01, \'RougeLsum\': 21.02}}, \'description\': \'Spanish BERT2BERT (BETO) fine-tuned on MLSUM ES for summarization\'}', metadata={})]


 Generate an automatic summarization of an executive's meeting notes without sharing too much details.\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={})]


 A customer needs to translate a French email they received into Spanish.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-fr\', \'api_call\': "translate(\'input_text\', model=\'Helsinki-NLP/opus-mt-en-fr\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newsdiscussdev2015-enfr.en.fr\': 33.8, \'newsdiscusstest2015-enfr.en.fr\': 40.0, \'newssyscomb2009.en.fr\': 29.8, \'news-test2008.en.fr\': 27.5, \'newstest2009.en.fr\': 29.4, \'newstest2010.en.fr\': 32.7, \'newstest2011.en.fr\': 34.3, \'newstest2012.en.fr\': 31.8, \'newstest2013.en.fr\': 33.2, \'Tatoeba.en.fr\': 50.5}}}, \'description\': \'Helsinki-NLP/opus-mt-en-fr is a translation model that translates English text to French using the Hugging Face Transformers library. It is based on the OPUS dataset and uses a transformer-align architecture with normalization and SentencePiece pre-processing.\'}', metadata={})]


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


 Your company has launched a new chatbot service. We need to create conversations with the customers to provide information about our products and answer their queries.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 We are a travel company trying to offer personalized experiences to our customers. We want to implement a conversational AI to help users get information about travel destinations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]