 Your client is a podcast aggregator website looking to transcribe episodes and display transcriptions on the site. Help them transcribe an audio file containing spoken English words.\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 app offers assistance to people with hearing problems by enhancing the clarity of speech. We need a feature to clean and enhance the audio.\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={})]


 Find a way to separate the background music and vocal from an audio file.\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={})]


 An educational software company needs an advanced method to separate voices from background noise. Develop a solution.\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={})]


 Develop a voice command security system that distinguishes between specific command phrases like \"disarm security\" or \"activate alarm\".\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Voice Activity Detection\', \'framework\': \'Hugging Face\', \'functionality\': \'Voice Activity Detection\', \'api_name\': \'d4data/Indian-voice-cloning\', \'api_call\': "pipeline(\'voice-activity-detection\', model=\'d4data/Indian-voice-cloning\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A model for detecting voice activity in Indian languages.\'}', metadata={})]


 I have an audio recording that I want to analyze to understand the emotion of the speaker.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'padmalcom/wav2vec2-large-emotion-detection-german\', \'api_call\': "pipeline(\'audio-classification\', model=\'padmalcom/wav2vec2-large-emotion-detection-german\')", \'api_arguments\': \'audio_file\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': "from transformers import pipeline\\naudio_classifier = pipeline(\'audio-classification\', model=\'padmalcom/wav2vec2-large-emotion-detection-german\')\\nresult = audio_classifier(audio_file)", \'performance\': {\'dataset\': \'emo-DB\', \'accuracy\': \'Not provided\'}, \'description\': \'This wav2vec2 based emotion detection model is trained on the emo-DB dataset. It can classify emotions in German audio files into seven classes: anger, boredom, disgust, fear, happiness, sadness, and neutral.\'}', metadata={})]


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


 Trying to implement an assistant that detects user's emotions on a given audio file to help them manage their emotions better.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'Rajaram1996/Hubert_emotion\', \'api_call\': "HubertForSpeechClassification.from_pretrained(\'Rajaram1996/Hubert_emotion\')", \'api_arguments\': {\'audio_file\': \'string\'}, \'python_environment_requirements\': [\'audio_models\', \'transformers\', \'torch\', \'numpy\', \'pydub\'], \'example_code\': "def predict_emotion_hubert(audio_file):\\n from audio_models import HubertForSpeechClassification\\n from transformers import Wav2Vec2FeatureExtractor, AutoConfig\\n import torch.nn.functional as F\\n import torch\\n import numpy as np\\n from pydub import AudioSegment\\nmodel = HubertForSpeechClassification.from_pretrained(Rajaram1996/Hubert_emotion)\\nfeature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(facebook/hubert-base-ls960)\\nsampling_rate=16000\\nconfig = AutoConfig.from_pretrained(Rajaram1996/Hubert_emotion)\\ndef speech_file_to_array(path, sampling_rate):\\n sound = AudioSegment.from_file(path)\\n sound = sound.set_frame_rate(sampling_rate)\\n sound_array = np.array(sound.get_array_of_samples())\\n return sound_array\\nsound_array = speech_file_to_array(audio_file, sampling_rate)\\ninputs = feature_extractor(sound_array, sampling_rate=sampling_rate, return_tensors=pt, padding=True)\\ninputs = {key: inputs[key].to(cpu).float() for key in inputs}\\nwith torch.no_grad():\\n logits = model(**inputs).logits\\nscores = F.softmax(logits, dim=1).detach().cpu().numpy()[0]\\noutputs = [{\\n emo: config.id2label[i],\\n score: round(score * 100, 1)}\\n for i, score in enumerate(scores)\\n]\\nreturn [row for row in sorted(outputs, key=lambda x:x[score], reverse=True) if row[\'score\'] != \'0.0%\'][:2]\\nresult = predict_emotion_hubert(male-crying.mp3)\\nresult", \'performance\': {\'dataset\': \'unknown\', \'accuracy\': \'unknown\'}, \'description\': \'A pretrained model for predicting emotion in local audio files using Hubert.\'}', metadata={})]


 We are building a customer service analytics software. The software should recognize the voice of the customers.\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 developing a voice-controlled device. Help categorize spoken commands into specific keywords to trigger various actions.\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={})]


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


 We are a company that provides transcription services. We require voice activity detection in our audio recordings.\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 need to predict the survival of passengers on the Titanic based on certain demographics like age, gender, etc.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Hugging Face\', \'functionality\': \'Binary Classification\', \'api_name\': \'harithapliyal/autotrain-tatanic-survival-51030121311\', \'api_call\': "AutoModel.from_pretrained(\'harithapliyal/autotrain-tatanic-survival-51030121311\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'harithapliyal/autotrain-data-tatanic-survival\', \'accuracy\': 0.872}, \'description\': \'A tabular classification model trained on the Titanic survival dataset using Hugging Face AutoTrain. The model predicts whether a passenger survived or not based on features such as age, gender, and passenger class.\'}', metadata={})]


 Implement a system that predicts the carbon emissions in a given 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={})]


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


 Our robotics team needs a way to stabilize a two-wheeled self-balancing robot. Would you suggest and apply any RL algorithm for this purpose?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'LunarLander-v2\', \'api_name\': \'araffin/dqn-LunarLander-v2\', \'api_call\': "DQN.load(load_from_hub(\'araffin/dqn-LunarLander-v2\', \'dqn-LunarLander-v2.zip\'), **kwargs)", \'api_arguments\': {\'checkpoint\': \'araffin/dqn-LunarLander-v2\', \'kwargs\': {\'target_update_interval\': 30}}, \'python_environment_requirements\': [\'huggingface_sb3\', \'stable_baselines3\'], \'example_code\': {\'load_model\': \'from huggingface_sb3 import load_from_hub\\nfrom stable_baselines3 import DQN\\nfrom stable_baselines3.common.env_util import make_vec_env\\nfrom stable_baselines3.common.evaluation import evaluate_policy\\n\\ncheckpoint = load_from_hub(araffin/dqn-LunarLander-v2, dqn-LunarLander-v2.zip)\\n\\nkwargs = dict(target_update_interval=30)\\n\\nmodel = DQN.load(checkpoint, **kwargs)\\nenv = make_vec_env(LunarLander-v2, n_envs=1)\', \'evaluate\': \'mean_reward, std_reward = evaluate_policy(\\n model,\\n env,\\n n_eval_episodes=20,\\n deterministic=True,\\n)\\nprint(fMean reward = {mean_reward:.2f} +/- {std_reward:.2f})\'}, \'performance\': {\'dataset\': \'LunarLander-v2\', \'accuracy\': \'280.22 +/- 13.03\'}, \'description\': \'This is a trained model of a DQN agent playing LunarLander-v2 using the stable-baselines3 library.\'}', metadata={})]


 We have a large text dataset and want to extract some important features from it for our data analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'sentiment_analysis_generic_dataset\', \'api_call\': "pipeline(\'text-classification\', model=\'Seethal/sentiment_analysis_generic_dataset\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "sentiment_analysis(\'I love this product!\')", \'performance\': {\'dataset\': \'generic_dataset\', \'accuracy\': \'Not specified\'}, \'description\': \'This is a fine-tuned downstream version of the bert-base-uncased model for sentiment analysis, this model is not intended for further downstream fine-tuning for any other tasks. This model is trained on a classified dataset for text classification.\'}', metadata={})]


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


 Our company is working on an app that allows music producers to detect beats in a sample. We want to use the Hubert-large-ll60k model for this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Feature Extraction\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'hubert-large-ll60k\', \'api_call\': "HubertModel.from_pretrained(\'facebook/hubert-large-ll60k\')", \'api_arguments\': \'pretrained model name\', \'python_environment_requirements\': \'transformers\', \'example_code\': "hubert = HubertModel.from_pretrained(\'facebook/hubert-large-ll60k\')", \'performance\': {\'dataset\': \'Libri-Light\', \'accuracy\': \'matches or improves upon the state-of-the-art wav2vec 2.0 performance\'}, \'description\': \'Hubert-Large is a self-supervised speech representation learning model pretrained on 16kHz sampled speech audio. It is designed to deal with the unique problems in speech representation learning, such as multiple sound units in each input utterance, no lexicon of input sound units during the pre-training phase, and variable lengths of sound units with no explicit segmentation. The model relies on an offline clustering step to provide aligned target labels for a BERT-like prediction loss.\'}', metadata={})]


 As an art director, generating ideas from descriptions can be difficult. Produce an image of a serene lake at sunset.\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-photoreal-2.0\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'dreamlike-art/dreamlike-photoreal-2.0\', torch_dtype=torch.float16)(prompt).images[0]", \'api_arguments\': {\'prompt\': \'photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens\'}, \'python_environment_requirements\': {\'torch\': \'torch.float16\', \'diffusers\': \'StableDiffusionPipeline\'}, \'example_code\': \'from diffusers import StableDiffusionPipeline\\nimport torch\\nmodel_id = dreamlike-art/dreamlike-photoreal-2.0\\npipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens\\nimage = pipe(prompt).images[0]\\nimage.save(./result.jpg)\', \'performance\': {\'dataset\': \'Stable Diffusion 1.5\', \'accuracy\': \'Not specified\'}, \'description\': \'Dreamlike Photoreal 2.0 is a photorealistic model based on Stable Diffusion 1.5, made by dreamlike.art. It can be used to generate photorealistic images from text prompts.\'}', metadata={})]


 There is a new small restaurant opening and they need a store sign featuring a kangaroo eating pizza.\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={})]


 Design a promotional poster for a new line of summer clothing featuring people wearing the clothes with a beach background.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'prompthero/openjourney-v4\', \'api_call\': "pipeline(\'text-to-image\', model=\'prompthero/openjourney-v4\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "generate_image(\'your text here\')", \'performance\': {\'dataset\': \'Midjourney v4 images\', \'accuracy\': \'Not provided\'}, \'description\': \'Openjourney v4 is trained on +124k Midjourney v4 images by PromptHero. It is used for generating images based on text inputs.\'}', metadata={})]


 We are developing an application for Japanese language learners. We need to extract text from Japanese manga pages for our users.\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={})]


 I am a museum curator, I need to learn more about an artwork in my archive so I can create better exhibition materials.\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 want an application that can answer questions about an image. For example, how many people are in this photo?\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 developing a home security software which can detect intruders entering the house. In case any door or object is tampered, the application will ask, \"Who entered the room?\", to assist quick analysis of the CCTV recordings.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Detect Bordered and Borderless tables in documents\', \'api_name\': \'TahaDouaji/detr-doc-table-detection\', \'api_call\': "DetrForObjectDetection.from_pretrained(\'TahaDouaji/detr-doc-table-detection\')", \'api_arguments\': [\'images\', \'return_tensors\', \'threshold\'], \'python_environment_requirements\': [\'transformers\', \'torch\', \'PIL\', \'requests\'], \'example_code\': \'from transformers import DetrImageProcessor, DetrForObjectDetection\\nimport torch\\nfrom PIL import Image\\nimport requests\\nimage = Image.open(IMAGE_PATH)\\nprocessor = DetrImageProcessor.from_pretrained(TahaDouaji/detr-doc-table-detection)\\nmodel = DetrForObjectDetection.from_pretrained(TahaDouaji/detr-doc-table-detection)\\ninputs = processor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\\ntarget_sizes = torch.tensor([image.size[::-1]])\\nresults = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]\\nfor score, label, box in zip(results[scores], results[labels], results[boxes]):\\n box = [round(i, 2) for i in box.tolist()]\\n print(\\n fDetected {model.config.id2label[label.item()]} with confidence \\n f{round(score.item(), 3)} at location {box}\\n )\', \'performance\': {\'dataset\': \'ICDAR2019 Table Dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'detr-doc-table-detection is a model trained to detect both Bordered and Borderless tables in documents, based on facebook/detr-resnet-50.\'}', metadata={})]


 Our business is growing rapidly, and we've received an increasing number of questions related to product images. We need the model to provide answers based on images.\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 receives invoices in different formats. We need to extract specific information from these documents to process payments and keep records.\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={})]


 Design an AI algorithm to answer questions from scanned documents.\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 want to analyze the text and images in a document and extract answers to questions based on the 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={})]


 Locate specific information from an invoice image, such as total amount due, invoice number, and due date.\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={})]


 The company director needs a summary of a recent financial report. You should provide the answer of following question: What were the total revenues for the last quarter?\n###Input: In the last quarter, the company's total revenues were reported at $3.2 million with a gross profit of $1.5 million. The operating expenses during the same quarter were $1 million.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'financial-summarization-pegasus\', \'api_call\': "PegasusForConditionalGeneration.from_pretrained(\'human-centered-summarization/financial-summarization-pegasus\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration\\nmodel_name = human-centered-summarization/financial-summarization-pegasus\\ntokenizer = PegasusTokenizer.from_pretrained(model_name)\\nmodel = PegasusForConditionalGeneration.from_pretrained(model_name)\\ntext_to_summarize = National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion in the biggest banking takeover this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer 0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio the banks set when they signed an initial framework agreement in June.The offer is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24% higher than the level the shares traded at before the talks were made public. Bloomberg News first reported the merger discussions.The new bank will have total assets of more than $220 billion, creating the Gulf region’s third-largest lender. The entity’s $46 billion market capitalization nearly matches that of Qatar National Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion of assets.\\ninput_ids = tokenizer(text_to_summarize, return_tensors=pt).input_ids\\noutput = model.generate(input_ids, max_length=32, num_beams=5, early_stopping=True)\\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\', \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 35.206, \'ROUGE-2\': 16.569, \'ROUGE-L\': 30.128, \'ROUGE-LSUM\': 30.171}}, \'description\': \'This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from Bloomberg, on topics such as stock, markets, currencies, rate and cryptocurrencies. It is based on the PEGASUS model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: google/pegasus-xsum model. PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization.\'}', metadata={})]


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


 We need to analyze some pictures from nature and classify them to protect some species of animals.\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 need to detect objects in an image provided by a user and describe their locations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8n-csgo-player-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-csgo-player-detection\')", \'api_arguments\': {\'image\': \'URL or local path to image\'}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\n\\nmodel = YOLO(\'keremberke/yolov8n-csgo-player-detection\')\\n\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\n\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\n\\nresults = model.predict(image)\\n\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'csgo-object-detection\', \'accuracy\': 0.844}, \'description\': "A YOLOv8 model for detecting Counter-Strike: Global Offensive (CS:GO) players with supported labels: [\'ct\', \'cthead\', \'t\', \'thead\']."}', metadata={})]


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


 Build an AI model that can recognize the objects in a given image and draw a boundary around them.\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={})]


 Design a pipeline to create artistic variations of an input image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image Variations\', \'api_name\': \'lambdalabs/sd-image-variations-diffusers\', \'api_call\': "StableDiffusionImageVariationPipeline.from_pretrained(\'lambdalabs/sd-image-variations-diffusers\', revision=\'v2.0\')", \'api_arguments\': {\'revision\': \'v2.0\'}, \'python_environment_requirements\': \'Diffusers >=0.8.0\', \'example_code\': \'from diffusers import StableDiffusionImageVariationPipeline\\nfrom PIL import Image\\ndevice = cuda:0\\nsd_pipe = StableDiffusionImageVariationPipeline.from_pretrained(\\n lambdalabs/sd-image-variations-diffusers,\\n revision=v2.0,\\n)\\nsd_pipe = sd_pipe.to(device)\\nim = Image.open(path/to/image.jpg)\\ntform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Resize(\\n  (224, 224),\\n  interpolation=transforms.InterpolationMode.BICUBIC,\\n  antialias=False,\\n ),\\n transforms.Normalize(\\n  [0.48145466, 0.4578275, 0.40821073],\\n  [0.26862954, 0.26130258, 0.27577711]),\\n])\\ninp = tform(im).to(device).unsqueeze(0)\\nout = sd_pipe(inp, guidance_scale=3)\\nout[images][0].save(result.jpg)\', \'performance\': {\'dataset\': \'ChristophSchuhmann/improved_aesthetics_6plus\', \'accuracy\': \'N/A\'}, \'description\': \'This version of Stable Diffusion has been fine tuned from CompVis/stable-diffusion-v1-4-original to accept CLIP image embedding rather than text embeddings. This allows the creation of image variations similar to DALLE-2 using Stable Diffusion.\'}', metadata={})]


 A user wants to improve the quality of a small sized picture from her vacation. We need to upscale the picture to 2x its size without losing quality.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image Upscaling\', \'api_name\': \'stabilityai/sd-x2-latent-upscaler\', \'api_call\': \'StableDiffusionLatentUpscalePipeline.from_pretrained(stabilityai/sd-x2-latent-upscaler, torch_dtype=torch.float16)\', \'api_arguments\': {\'prompt\': \'text prompt\', \'image\': \'low resolution latents\', \'num_inference_steps\': 20, \'guidance_scale\': 0, \'generator\': \'torch generator\'}, \'python_environment_requirements\': [\'git+https://github.com/huggingface/diffusers.git\', \'transformers\', \'accelerate\', \'scipy\', \'safetensors\'], \'example_code\': \'from diffusers import StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline\\nimport torch\\npipeline = StableDiffusionPipeline.from_pretrained(CompVis/stable-diffusion-v1-4, torch_dtype=torch.float16)\\npipeline.to(cuda)\\nupscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(stabilityai/sd-x2-latent-upscaler, torch_dtype=torch.float16)\\nupscaler.to(cuda)\\nprompt = a photo of an astronaut high resolution, unreal engine, ultra realistic\\ngenerator = torch.manual_seed(33)\\nlow_res_latents = pipeline(prompt, generator=generator, output_type=latent).images\\nupscaled_image = upscaler(prompt=prompt, image=low_res_latents, num_inference_steps=20, guidance_scale=0, generator=generator).images[0]\\nupscaled_image.save(astronaut_1024.png)\', \'performance\': {\'dataset\': \'LAION-2B\', \'accuracy\': \'Not specified\'}, \'description\': "Stable Diffusion x2 latent upscaler is a diffusion-based upscaler model developed by Katherine Crowson in collaboration with Stability AI. It is designed to upscale Stable Diffusion\'s latent denoised image embeddings, allowing for fast text-to-image and upscaling pipelines. The model was trained on a high-resolution subset of the LAION-2B dataset and works with all Stable Diffusion checkpoints."}', metadata={})]


 I want to create a visual representation based on a short description: \"A magical forest with unicorns and a rainbow.\".\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={})]


 Explain how to use GPT-3 to create a slogan for an e-commerce website that sells eco-friendly products.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Conversational\', \'api_name\': \'ingen51/DialoGPT-medium-GPT4\', \'api_call\': "pipeline(\'conversational\', model=\'ingen51/DialoGPT-medium-GPT4\')", \'api_arguments\': [\'message\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "conversational_pipeline(\'Hello, how are you?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'A GPT-4 model for generating conversational responses in a dialogue setting.\'}', metadata={})]


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


 Our client wants to analyze videos for their marketing website. They need a quick solution to categorize video content without worrying about accuracy.\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={})]


 I want to classify images of vehicles including cars, motorcycles, trucks, and bicycles, based on their appearance.\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 need to classify images of animals into their specific categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


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


 We are receiving mixed reviews about our new product and we need to understand customers' reactions.\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={})]


 Detect if there are any harmful messages in a chat room.\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 am developing software that needs to retrieve relevant information from a collection of documents based on a user's query.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Text2Text Generation\', \'api_name\': \'castorini/doc2query-t5-base-msmarco\', \'api_call\': "T5ForConditionalGeneration.from_pretrained(\'castorini/doc2query-t5-base-msmarco\')", \'api_arguments\': \'text, max_length\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'MS MARCO\', \'accuracy\': \'Not specified\'}, \'description\': \'A T5 model trained on the MS MARCO dataset for generating queries from documents.\'}', metadata={})]


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


 Extract entities from a provided sentence mentioning various companies and their CEOs.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Transformers\', \'api_name\': \'sentence-transformers/all-roberta-large-v1\', \'api_call\': "SentenceTransformer(\'sentence-transformers/all-roberta-large-v1\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer\\nsentences = [This is an example sentence, Each sentence is converted]\\nmodel = SentenceTransformer(\'sentence-transformers/all-roberta-large-v1\')\\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 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 I am developing a food application where food keywords need to be extracted from user's input text. The model should be able to recognize food-related named entities.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'Dizex/InstaFoodRoBERTa-NER\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'Dizex/InstaFoodRoBERTa-NER\')", \'api_arguments\': {\'model\': "AutoModelForTokenClassification.from_pretrained(\'Dizex/InstaFoodRoBERTa-NER\')", \'tokenizer\': "AutoTokenizer.from_pretrained(\'Dizex/InstaFoodRoBERTa-NER\')"}, \'python_environment_requirements\': {\'transformers\': \'from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\'}, \'example_code\': "example = Today\'s meal: Fresh olive pok bowl topped with chia seeds. Very delicious!\\nner_entity_results = pipe(example, aggregation_strategy=\'simple\')\\nprint(ner_entity_results)", \'performance\': {\'dataset\': \'Dizex/InstaFoodSet\', \'accuracy\': {\'f1\': 0.91, \'precision\': 0.89, \'recall\': 0.93}}, \'description\': \'InstaFoodRoBERTa-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD). Specifically, this model is a roberta-base model that was fine-tuned on a dataset consisting of 400 English Instagram posts related to food.\'}', metadata={})]


 Create a script for an AI bot that automatically adds punctuation to users' messages in a chat app.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'Zixtrauce/BaekBot\', \'api_call\': "pipeline(\'conversational\', model=\'Zixtrauce/BaekBot\')", \'api_arguments\': \'input_message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "conversational_pipeline(\'input_message\')", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'BaekBot is a conversational model based on the GPT-2 architecture for text generation. It can be used for generating human-like responses in a chat-like environment.\'}', metadata={})]


 I am writing an article on the history of technology companies, and I want to extract the names of companies and people mentioned in the text.\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={})]


 I have a diary entry and want to identify the names of people and locations mentioned in it.\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\': \'tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa\', \'api_call\': "pipeline(\'question-answering\', model=\'tiennvcs/layoutlmv2-large-uncased-finetuned-vi-infovqa\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers==4.15.0, torch==1.8.0+cu101, datasets==1.17.0, tokenizers==0.10.3\', \'example_code\': \'\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': {\'Loss\': 8.5806}}, \'description\': \'This model is a fine-tuned version of microsoft/layoutlmv2-large-uncased on an unknown dataset.\'}', metadata={})]


 Our company has a deal with international clients, and it's important to detect the proper locations of meetings from multilingual texts provided to us.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'facebook/nllb-200-distilled-600M\', \'api_call\': "pipeline(\'translation_xx_to_yy\', model=\'facebook/nllb-200-distilled-600M\')", \'api_arguments\': [\'model\', \'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline; translator = pipeline(\'translation_xx_to_yy\', model=\'facebook/nllb-200-distilled-600M\'); translator(\'Hello World\')", \'performance\': {\'dataset\': \'Flores-200\', \'accuracy\': \'BLEU, spBLEU, chrF++\'}, \'description\': \'NLLB-200 is a machine translation model primarily intended for research in machine translation, especially for low-resource languages. It allows for single sentence translation among 200 languages. The model was trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation.\'}', metadata={})]


 We need to extract information from a table to answer a user's question regarding a company's revenue.\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-small-finetuned-sqa\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-small-finetuned-sqa\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6155}, \'description\': \'TAPAS small model fine-tuned on Sequential Question Answering (SQA). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).\'}', metadata={})]


 Develop a table-based question answering system that can answer queries based on the input data.\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-small-finetuned-sqa\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-small-finetuned-sqa\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6155}, \'description\': \'TAPAS small model fine-tuned on Sequential Question Answering (SQA). It uses relative position embeddings (i.e. resetting the position index at every cell of the table).\'}', metadata={})]


 Create a tool that receives a table and a question in natural language, and returns an answer to the question based on the inputted table.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'lysandre/tapas-temporary-repo\', \'api_call\': "TapasForQuestionAnswering.from_pretrained(\'lysandre/tapas-temporary-repo\')", \'api_arguments\': [\'model_name\', \'question\', \'table\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "tokenizer = TapasTokenizer.from_pretrained(\'lysandre/tapas-temporary-repo\')\\nmodel = TapasForQuestionAnswering.from_pretrained(\'lysandre/tapas-temporary-repo\')\\ninputs = tokenizer(table=table, queries=question, return_tensors=\'pt\')\\noutputs = model(**inputs)\\npredicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach(), outputs.logits_aggregation.detach())", \'performance\': {\'dataset\': \'SQA\', \'accuracy\': \'Not provided\'}, \'description\': \'TAPAS base model fine-tuned on Sequential Question Answering (SQA). This model is pretrained on a large corpus of English data from Wikipedia in a self-supervised fashion and can be used for answering questions related to a table in a conversational set-up.\'}', metadata={})]


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


 A group of students are doing a project on European capitals. They need to know the capital city of Germany.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'svalabs/gbert-large-zeroshot-nli\', \'api_call\': "pipeline(\'zero-shot-classification\', model=\'svalabs/gbert-large-zeroshot-nli\')", \'api_arguments\': [\'sequence\', \'labels\', \'hypothesis_template\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import pipeline\\nzershot_pipeline = pipeline(zero-shot-classification, model=svalabs/gbert-large-zeroshot-nli)\\nsequence = Ich habe ein Problem mit meinem Iphone das so schnell wie möglich gelöst werden muss\\nlabels = [Computer, Handy, Tablet, dringend, nicht dringend]\\nhypothesis_template = In diesem Satz geht es um das Thema {}. \', \'performance\': {\'dataset\': \'XNLI TEST-Set\', \'accuracy\': \'85.6%\'}, \'description\': \'A German zeroshot classification model based on the German BERT large model from deepset.ai and finetuned for natural language inference using machine-translated nli sentence pairs from mnli, anli, and snli datasets.\'}', metadata={})]


 Assist us in creating a question answering system to provide quick answers for customer inquiries.\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 worried about price inflation in our country. Can you answer our questions on price inflation using the BERT large cased whole word masking finetuned model on SQuAD?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'bert-large-uncased-whole-word-masking-squad2\', \'api_call\': "pipeline(\'question-answering\', model=AutoModel.from_pretrained(\'deepset/bert-large-uncased-whole-word-masking-squad2\'), tokenizer=AutoTokenizer.from_pretrained(\'deepset/bert-large-uncased-whole-word-masking-squad2\'))", \'api_arguments\': {\'model_name\': \'deepset/bert-large-uncased-whole-word-masking-squad2\', \'tokenizer\': \'deepset/bert-large-uncased-whole-word-masking-squad2\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "QA_input = {\\n \'question\': \'Why is model conversion important?\',\\n \'context\': \'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.\'\\n}\\nres = nlp(QA_input)", \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': {\'Exact Match\': 80.885, \'F1\': 83.876}}, \'description\': \'This is a bert-large model, fine-tuned using the SQuAD2.0 dataset for the task of question answering. It is designed for extractive question answering and supports English language.\'}', metadata={})]


 I am interested in developing a system that can categorize my text messages into different subjects like finances, health, and entertainment.\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={})]


 An online magazine editor is trying to clujster articles into topics. We want to classify articles into categories like sport, politics, health, and technology written in French.\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={})]


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


 We need a classifier that can sort movie synopses in German into three categories: crime, tragedy, and theft.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Transformers\', \'functionality\': \'Zero-Shot Classification\', \'api_name\': "AutoModelForSequenceClassification.from_pretrained(\'Sahajtomar/German_Zeroshot\')", \'api_call\': \'classifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)\', \'api_arguments\': {\'sequence\': \'string\', \'candidate_labels\': \'list of strings\', \'hypothesis_template\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import pipeline\\nclassifier = pipeline(zero-shot-classification, model=Sahajtomar/German_Zeroshot)\\nsequence = Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie\\ncandidate_labels = [Verbrechen,Tragödie,Stehlen]\\nhypothesis_template = In deisem geht es um {}. ## Since monolingual model,its sensitive to hypothesis template. This can be experimented\\nclassifier(sequence, candidate_labels, hypothesis_template=hypothesis_template)\', \'performance\': {\'dataset\': {\'XNLI DEV (german)\': {\'accuracy\': 85.5}, \'XNLI TEST (german)\': {\'accuracy\': 83.6}}}, \'description\': \'This model has GBERT Large as base model and fine-tuned it on xnli de dataset. The default hypothesis template is in English: This text is {}. While using this model, change it to In deisem geht es um {}. or something different. While inferencing through huggingface api may give poor results as it uses by default english template. Since model is monolingual and not multilingual, hypothesis template needs to be changed accordingly.\'}', metadata={})]


 Write an abstract about the impacts of social media on mental health, summarizing key findings from previous studies.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'moussaKam/barthez-orangesum-abstract\', \'api_call\': "BarthezModel.from_pretrained(\'moussaKam/barthez-orangesum-abstract\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'orangeSum\', \'accuracy\': \'\'}, \'description\': \'Barthez model finetuned on orangeSum for abstract generation in French language\'}', metadata={})]


 Help me to translate a Spanish text to English. The Spanish text is: \"Lo siento, pero no puedo ir a la reuni\u00f3n debido a una emergencia personal. Avisar\u00e9 al equipo y nos pondremos en contacto para reprogramar la reuni\u00f3n.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-en-es\', \'api_call\': "pipeline(\'translation_en_to_es\', model=\'Helsinki-NLP/opus-mt-en-es\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_en_to_es\', model=\'Helsinki-NLP/opus-mt-en-es\')\\ntranslated_text = translation(\'Hello, how are you?\')", \'performance\': {\'dataset\': \'Tatoeba-test.eng.spa\', \'accuracy\': 54.9}, \'description\': \'This model is a translation model from English to Spanish using the Hugging Face Transformers library. It is based on the Marian framework and trained on the OPUS dataset. The model achieves a BLEU score of 54.9 on the Tatoeba test set.\'}', metadata={})]


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


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


 We have a new platform that offers various services related to digital art, and we want it to be able to produce creative story ideas based on a short description. Can you help me providing suggestions?\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={})]