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


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


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


 Help me find similarity scores for different restaurant reviews.\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\': \'nikcheerla/nooks-amd-detection-realtime\', \'api_call\': "SentenceTransformer(\'nikcheerla/nooks-amd-detection-realtime\')", \'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(\'{MODEL_NAME}\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Automated evaluation\'}, \'description\': \'This is a sentence-transformers model that maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 Our company is building an automated assistance system for the visually impaired. We need a tool to convert text into spoken instructions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'kan-bayashi_ljspeech_vits\', \'api_call\': "pipeline(\'text-to-speech\', model=\'espnet/kan-bayashi_ljspeech_vits\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'espnet/kan-bayashi_ljspeech_vits\'); tts(\'Hello World\')", \'performance\': {\'dataset\': \'ljspeech\', \'accuracy\': \'Not mentioned\'}, \'description\': \'A Text-to-Speech model trained on the ljspeech dataset using the ESPnet toolkit. This model can be used to convert text input into synthesized speech.\'}', metadata={})]


 I would like to create an application where users may listen to translations of English sentences. I need a Text-to-Speech model to support this functionality.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'mio/Artoria\', \'api_call\': "pipeline(\'text-to-speech\', model=\'mio/Artoria\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; tts = pipeline(\'text-to-speech\', model=\'mio/Artoria\'); tts(\'s\')", \'performance\': {\'dataset\': \'fate\', \'accuracy\': \'Not provided\'}, \'description\': \'This model was trained by mio using fate recipe in espnet. It is a text-to-speech model that can convert text input into speech output.\'}', metadata={})]


 We are a language learning app platform. We need a text-to-speech tool to read a sentence in multiple languages for our users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'SYSPIN/Marathi_Male_TTS\', \'api_call\': "api.load(\'ESPnet/espnet_model_zoo:SYSPIN/Marathi_Male_TTS\').", \'api_arguments\': [], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Marathi Male Text-to-Speech model using ESPnet framework.\'}', metadata={})]


 I need to create an audio output that translates the given text to speech for a French audiobook assistant.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Fairseq\', \'functionality\': \'speech-to-speech-translation\', \'api_name\': \'facebook/textless_sm_en_fr\', \'api_call\': "load_model_ensemble_and_task_from_hf_hub(\'facebook/textless_sm_en_fr\')", \'api_arguments\': [\'input_file\'], \'python_environment_requirements\': [\'huggingface_hub\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is a speech-to-speech translation model trained by Facebook. It is designed for translating English speech to French speech.\'}', metadata={})]


 We are opening a platform where users can record their own podcast and host it on our platform, can you help us to convert the audio into text automatically?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'lakahaga/novel_reading_tts\', \'api_call\': "AutoModelForTTS.from_pretrained(\'lakahaga/novel_reading_tts\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "inputs = processor(text, return_tensors=\'pt\'); generated_audio = model.generate(**inputs);", \'performance\': {\'dataset\': \'novelspeech\', \'accuracy\': None}, \'description\': \'This model was trained by lakahaga using novelspeech recipe in espnet. It is designed for Korean text-to-speech tasks.\'}', metadata={})]


 We are currently working on the development of a speech-to-text application for transcription purposes. Can you help generate the required transcription code?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 We are building an app for tour guides to transcribe what they say during the tour. Then the transcript will be translated into sign language.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 I am an English teacher. I have recently met a Hokkien speaker. I am looking to translate a conversational sentence from English to Hokkien to facilitate communication.\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-zh\', \'api_call\': "pipeline(\'translation_en_to_zh\', model=\'Helsinki-NLP/opus-mt-en-zh\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_en_to_zh\', model=\'Helsinki-NLP/opus-mt-en-zh\')\\ntranslated_text = translation(\'Hello, world!\')", \'performance\': {\'dataset\': \'Tatoeba-test.eng.zho\', \'accuracy\': {\'BLEU\': 31.4, \'chr-F\': 0.268}}, \'description\': "A translation model for English to Chinese using the Hugging Face Transformers library. It is based on the Marian NMT model and trained on the OPUS dataset. The model requires a sentence initial language token in the form of \'>>id<<\' (id = valid target language ID)."}', metadata={})]


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


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


 We have a database of customer voices and are trying to build a voice recognition product so we can recognize customer voices when they call. How should we process and classify?\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 drone. Please identify the spoken command in the audio clip provided.\n###Input: \"audio_clip.wav\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'CTranslate2\', \'functionality\': \'Automatic Speech Recognition\', \'api_name\': \'guillaumekln/faster-whisper-large-v2\', \'api_call\': "WhisperModel(\'large-v2\')", \'api_arguments\': [\'audio.mp3\'], \'python_environment_requirements\': [\'faster_whisper\'], \'example_code\': \'from faster_whisper import WhisperModel\\nmodel = WhisperModel(large-v2)\\nsegments, info = model.transcribe(audio.mp3)\\nfor segment in segments:\\n print([%.2fs -> %.2fs] %s % (segment.start, segment.end, segment.text))\', \'performance\': {\'dataset\': \'99 languages\', \'accuracy\': \'Not provided\'}, \'description\': \'Whisper large-v2 model for CTranslate2. This model can be used in CTranslate2 or projets based on CTranslate2 such as faster-whisper.\'}', metadata={})]


 Make a summary video for our last team meeting. The audio from the video must identify who is speaking and when.\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 estimate the price of a house based on its features using this API. Please provide the code.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Joblib\', \'functionality\': \'Single Column Regression\', \'api_name\': \'jwan2021/autotrain-us-housing-prices-1771761512\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\', \'json\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'jwan2021/autotrain-data-us-housing-prices\', \'accuracy\': {\'Loss\': 122809.223, \'R2\': 0.884, \'MSE\': 15082105200.447, \'MAE\': 95586.887, \'RMSLE\': 0.13}}, \'description\': \'A model trained using AutoTrain for predicting US housing prices with single column regression. The model is based on the jwan2021/autotrain-data-us-housing-prices dataset and has a CO2 Emissions of 50.5369 grams.\'}', metadata={})]


 Our company wants to predict housing prices in the US based on given features. Help us use the trained model to predict the prices.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Joblib\', \'functionality\': \'Single Column Regression\', \'api_name\': \'jwan2021/autotrain-us-housing-prices-1771761512\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\', \'json\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'jwan2021/autotrain-data-us-housing-prices\', \'accuracy\': {\'Loss\': 122809.223, \'R2\': 0.884, \'MSE\': 15082105200.447, \'MAE\': 95586.887, \'RMSLE\': 0.13}}, \'description\': \'A model trained using AutoTrain for predicting US housing prices with single column regression. The model is based on the jwan2021/autotrain-data-us-housing-prices dataset and has a CO2 Emissions of 50.5369 grams.\'}', metadata={})]


 An environmental organization would like to use our Carbon Emissions prediction model to estimate CO2 emissions of different configurations of vehicles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'45473113800\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'samvelkoch/autotrain-data-prknsn-2\', \'accuracy\': {\'Loss\': 5.079, \'R2\': 0.109, \'MSE\': 25.795, \'MAE\': 3.78, \'RMSLE\': 0.849}}, \'description\': \'A tabular regression model trained with AutoTrain for predicting carbon emissions.\'}', metadata={})]


 I am a data analyst working in pollution detection, find a model and develop a piece of code for me for environment monitoring.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'tejas23/autotrain-amx2-1702259725\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': {\'accuracy\': 0.827}}, \'description\': \'Multi-class Classification Model for Carbon Emissions\'}', metadata={})]


 We want to develop an intelligent prosthetic leg that can improve walking. Use a decision transformer to predict actions to be taken.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'edbeeching/decision-transformer-gym-walker2d-expert\', \'api_call\': "AutoModel.from_pretrained(\'edbeeching/decision-transformer-gym-walker2d-expert\')", \'api_arguments\': {\'mean\': [1.2384834, 0.19578537, -0.10475016, -0.18579608, 0.23003316, 0.022800924, -0.37383768, 0.337791, 3.925096, -0.0047428459, 0.025267061, -0.0039287535, -0.01736751, -0.48212224, 0.00035432147, -0.0037124525, 0.0026285544], \'std\': [0.06664903, 0.16980624, 0.17309439, 0.21843709, 0.74599105, 0.02410989, 0.3729872, 0.6226182, 0.9708009, 0.72936815, 1.504065, 2.495893, 3.511518, 5.3656907, 0.79503316, 4.317483, 6.1784487]}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'See our Blog Post, Colab notebook or Example Script for usage.\', \'performance\': {\'dataset\': \'Gym Walker2d environment\', \'accuracy\': \'Not provided\'}, \'description\': \'Decision Transformer model trained on expert trajectories sampled from the Gym Walker2d environment.\'}', metadata={})]


 You want to create a bot that can play the Pong No Frameskip-v4 game with exceptional skill.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'deep-reinforcement-learning\', \'api_name\': \'ppo-PongNoFrameskip-v4\', \'api_call\': "load_from_hub(repo_id=\'sb3/ppo-PongNoFrameskip-v4\',filename=\'{MODEL FILENAME}.zip\',)", \'api_arguments\': [\'algo\', \'env\', \'f\'], \'python_environment_requirements\': [\'RL Zoo\', \'SB3\', \'SB3 Contrib\'], \'example_code\': \'python -m rl_zoo3.load_from_hub --algo ppo --env PongNoFrameskip-v4 -orga sb3 -f logs/\', \'performance\': {\'dataset\': \'PongNoFrameskip-v4\', \'accuracy\': \'21.00 +/- 0.00\'}, \'description\': \'This is a trained model of a PPO agent playing PongNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.\'}', metadata={})]


 I am a game developer working on a game project involving moving carts. I need to use reinforcement learning to improve the game experience.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Stable-Baselines3\', \'functionality\': \'CartPole-v1\', \'api_name\': \'dqn-CartPole-v1\', \'api_call\': "load_from_hub(repo_id=\'sb3/dqn-CartPole-v1\',filename=\'{MODEL FILENAME}.zip\',)", \'api_arguments\': [\'algo\', \'env\', \'logs\'], \'python_environment_requirements\': [\'rl_zoo3\', \'stable-baselines3\', \'stable-baselines3-contrib\'], \'example_code\': \'python train.py --algo dqn --env CartPole-v1 -f logs/\', \'performance\': {\'dataset\': \'CartPole-v1\', \'accuracy\': \'500.00 +/- 0.00\'}, \'description\': \'This is a trained model of a DQN agent playing CartPole-v1 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.\'}', metadata={})]


 Develop a soccer playing agent that can outperform its opponents in a 2v2 environment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'Unity ML-Agents Library\', \'functionality\': \'Train and play SoccerTwos\', \'api_name\': \'poca-SoccerTwosv2\', \'api_call\': "mlagents-load-from-hf --repo-id=\'Raiden-1001/poca-SoccerTwosv2\' --local-dir=\'./downloads\'", \'api_arguments\': [\'your_configuration_file_path.yaml\', \'run_id\'], \'python_environment_requirements\': [\'ml-agents\'], \'example_code\': \'mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume\', \'performance\': {\'dataset\': \'SoccerTwos\', \'accuracy\': \'Not provided\'}, \'description\': \'A trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library.\'}', metadata={})]


 We are tasked to analyze text for a Russian newspaper to help understand general sentiment and trends in the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'finiteautomata/beto-sentiment-analysis\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'finiteautomata/beto-sentiment-analysis\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'Hugging Face Transformers library\', \'example_code\': \'\', \'performance\': {\'dataset\': \'TASS 2020 corpus\', \'accuracy\': \'\'}, \'description\': \'Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish. Uses POS, NEG, NEU labels.\'}', metadata={})]


 We want to generate an image from a textual description for our PowerPoint presentation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Linaqruf/anything-v3.0\', \'api_call\': "Text2ImagePipeline(model=\'Linaqruf/anything-v3.0\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A text-to-image model that generates images from text descriptions.\'}', metadata={})]


 A new manga has been released and we would like to provide a manga reader app with translations. Get the text from the manga image.\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={})]


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


 There is robot in our factory which reads the image from the production line and then generate a text output based on the image.\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={})]


 I am a filmmaker, and I need to make a short video based on a scene description from a script.\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'm an author and want to create a short video based on a brief passage from my book. Can you generate a video based on this text?\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 build an AI model that can analyze images and answer questions about the content of the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 We are trying to develop an application that helps tourists get information about attractions by analyzing images they take and responding to questions.\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 have the financial documents of a company and we want to extract information about the cash flow. Modify the model so it can answer the questions related to the cash flow.\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={})]


 Please generate a correct building plan leveraging the data given.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Joblib\', \'functionality\': \'Single Column Regression\', \'api_name\': \'jwan2021/autotrain-us-housing-prices-1771761512\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'joblib\', \'pandas\', \'json\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'jwan2021/autotrain-data-us-housing-prices\', \'accuracy\': {\'Loss\': 122809.223, \'R2\': 0.884, \'MSE\': 15082105200.447, \'MAE\': 95586.887, \'RMSLE\': 0.13}}, \'description\': \'A model trained using AutoTrain for predicting US housing prices with single column regression. The model is based on the jwan2021/autotrain-data-us-housing-prices dataset and has a CO2 Emissions of 50.5369 grams.\'}', metadata={})]


 Help our drone video analyzing app estimate the depth in drone footage.\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={})]


 As a salesperson, I need to analyze customer invoices to answer questions about the total amount, tax, and due date from an image file.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'layoutlm-invoices\', \'api_call\': "AutoModelForDocumentQuestionAnswering.from_pretrained(\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "nlp(question=\'What is the total amount?\', context=\'your_invoice_text\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'Not provided\'}, \'description\': \'A fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension. Unlike other QA models, which can only extract consecutive tokens, this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


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


 We are creating an autonomous car and need to estimate the depth of objects in a given scene.\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={})]


 Help us create an AI solution to automatically label images taken by a security camera.\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={})]


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


 Our delivery drones need to detect and avoid obstacles while flying. Develop a solution for them to detect objects in their path.\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 code to recognize objects in images using deformable-detr model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'deformable-detr\', \'api_call\': "DeformableDetrForObjectDetection.from_pretrained(\'SenseTime/deformable-detr\')", \'api_arguments\': [\'images\', \'return_tensors\'], \'python_environment_requirements\': [\'transformers\', \'torch\', \'PIL\', \'requests\'], \'example_code\': "from transformers import AutoImageProcessor, DeformableDetrForObjectDetection\\nimport torch\\nfrom PIL import Image\\nimport requests\\nurl = \'http://images.cocodataset.org/val2017/000000039769.jpg\'\\nimage = Image.open(requests.get(url, stream=True).raw)\\nprocessor = AutoImageProcessor.from_pretrained(\'SenseTime/deformable-detr\')\\nmodel = DeformableDetrForObjectDetection.from_pretrained(\'SenseTime/deformable-detr\')\\ninputs = processor(images=image, return_tensors=\'pt\')\\noutputs = model(**inputs)", \'performance\': {\'dataset\': \'COCO 2017\', \'accuracy\': \'Not provided\'}, \'description\': \'Deformable DETR model with ResNet-50 backbone trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al. and first released in this repository.\'}', metadata={})]


 I need to extract tables from a set of scanned document images to simplify data analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Table Extraction\', \'api_name\': \'keremberke/yolov8n-table-extraction\', \'api_call\': "YOLO(\'keremberke/yolov8n-table-extraction\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': [\'ultralyticsplus==0.0.23\', \'ultralytics==8.0.21\'], \'example_code\': [\'from ultralyticsplus import YOLO, render_result\', "model = YOLO(\'keremberke/yolov8n-table-extraction\')", "model.overrides[\'conf\'] = 0.25", "model.overrides[\'iou\'] = 0.45", "model.overrides[\'agnostic_nms\'] = False", "model.overrides[\'max_det\'] = 1000", "image = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'", \'results = model.predict(image)\', \'print(results[0].boxes)\', \'render = render_result(model=model, image=image, result=results[0])\', \'render.show()\'], \'performance\': {\'dataset\': \'table-extraction\', \'accuracy\': 0.967}, \'description\': "An object detection model for extracting tables from documents. Supports two label types: \'bordered\' and \'borderless\'."}', metadata={})]


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


 Create an object detector that can detect blood cells in an image, such as platelets, red blood cells, and white blood cells.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Blood Cell Detection\', \'api_name\': \'keremberke/yolov8n-blood-cell-detection\', \'api_call\': "YOLO(\'keremberke/yolov8n-blood-cell-detection\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': \'ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8n-blood-cell-detection\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'blood-cell-object-detection\', \'accuracy\': 0.893}, \'description\': \'This model detects blood cells in images, specifically Platelets, RBC, and WBC. It is based on the YOLOv8 architecture and trained on the blood-cell-object-detection dataset.\'}', metadata={})]


 I am a real-estate agent working on a project where I need to convert images of room plans to a better visual representation.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Realistic_Vision_V1.4\', \'api_call\': "pipeline(\'text-to-image\', model=SG161222/Realistic_Vision_V1.4)", \'api_arguments\': {\'prompt\': \'string\', \'negative_prompt\': \'string\'}, \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline\\n\\nmodel = pipeline(\'text-to-image\', model=\'SG161222/Realistic_Vision_V1.4\')\\n\\nprompt = \'a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3\'\\nnegative_prompt = \'(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck\'\\n\\nresult = model(prompt, negative_prompt=negative_prompt)", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'Realistic_Vision_V1.4 is a text-to-image model that generates high-quality and detailed images based on textual prompts. It can be used for various applications such as generating realistic portraits, landscapes, and other types of images.\'}', metadata={})]


 We recently received low resolution images of newly released products and need to upscale them for better 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={})]


 As a toy company, we are designing a new toy line. We'd like you to create an image of a toy robot using relevant text prompts as control input.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'prompthero/openjourney-v4\', \'api_call\': "pipeline(\'text-to-image\', model=\'prompthero/openjourney-v4\')", \'api_arguments\': {\'text\': \'string\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "generate_image(\'your text here\')", \'performance\': {\'dataset\': \'Midjourney v4 images\', \'accuracy\': \'Not provided\'}, \'description\': \'Openjourney v4 is trained on +124k Midjourney v4 images by PromptHero. It is used for generating images based on text inputs.\'}', metadata={})]


 I'm developing a game that needs more Minecraft skins for some characters. How can I generete them with this model?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Diffusers\', \'api_name\': \'Minecraft-Skin-Diffusion\', \'api_call\': "DDPMPipeline.from_pretrained(\'WiNE-iNEFF/Minecraft-Skin-Diffusion\')", \'api_arguments\': {}, \'python_environment_requirements\': [\'diffusers\'], \'example_code\': "from diffusers import DDPMPipeline\\npipeline = DDPMPipeline.from_pretrained(\'WiNE-iNEFF/Minecraft-Skin-Diffusion\')\\nimage = pipeline().images[0].convert(\'RGBA\')\\nimage", \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Unconditional Image Generation model for generating Minecraft skins using diffusion-based methods.\'}', metadata={})]


 Find me a generative model to create cat images in 256x256 resolution.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Unconditional Image Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Unconditional Image Generation\', \'api_name\': \'google/ddpm-cat-256\', \'api_call\': "DDPMPipeline.from_pretrained(\'google/ddpm-cat-256\')", \'api_arguments\': [\'model_id\'], \'python_environment_requirements\': [\'diffusers\'], \'example_code\': \'!pip install diffusers\\nfrom diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline\\nmodel_id = google/ddpm-cat-256\\nddpm = DDPMPipeline.from_pretrained(model_id)\\nimage = ddpm().images[0]\\nimage.save(ddpm_generated_image.png)\', \'performance\': {\'dataset\': \'CIFAR10\', \'accuracy\': {\'Inception_score\': 9.46, \'FID_score\': 3.17}}, \'description\': \'Denoising Diffusion Probabilistic Models (DDPM) is a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. It can generate high-quality images using discrete noise schedulers such as scheduling_ddpm, scheduling_ddim, and scheduling_pndm. The model is trained on the unconditional CIFAR10 dataset and 256x256 LSUN, obtaining an Inception score of 9.46 and a state-of-the-art FID score of 3.17.\'}', metadata={})]


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


 People in my company need an automatic solution to categorize videos based on their content. The system should be able to recognize the main theme of a video with high 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={})]


 We want to build a product to classify images of pets into different 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 developing an app to classify food images. We have a set of images and want to use a pre-trained model for classification.\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={})]


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


 To improve customer service, the company is developing an AI-based chatbot that can help generate multiple paraphrases of a given customer query. We want to know if the generated paraphrases are adequate.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Paraphrasing\', \'api_name\': \'prithivida/parrot_paraphraser_on_T5\', \'api_call\': "Parrot(model_tag=\'prithivida/parrot_paraphraser_on_T5\', use_gpu=False)", \'api_arguments\': [\'input_phrase\', \'diversity_ranker\', \'do_diverse\', \'max_return_phrases\', \'max_length\', \'adequacy_threshold\', \'fluency_threshold\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': \'from parrot import Parrot\\nimport torch\\nimport warnings\\nwarnings.filterwarnings(ignore)\\n\\nparrot = Parrot(model_tag=prithivida/parrot_paraphraser_on_T5, use_gpu=False)\\nphrases = [Can you recommed some upscale restaurants in Newyork?,\\n What are the famous places we should not miss in Russia?\\n]\\nfor phrase in phrases:\\n print(-*100)\\n print(Input_phrase: , phrase)\\n print(-*100)\\n para_phrases = parrot.augment(input_phrase=phrase)\\n for para_phrase in para_phrases:\\n  print(para_phrase)\', \'performance\': {\'dataset\': \'Not mentioned\', \'accuracy\': \'Not mentioned\'}, \'description\': \'Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. It offers knobs to control Adequacy, Fluency, and Diversity as per your needs. It mainly focuses on augmenting texts typed-into or spoken-to conversational interfaces for building robust NLU models.\'}', metadata={})]


 We need to analyze user reviews for our app to determine whether they are positive, negative, or neutral.\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={})]


 Give me a tool to detect named entities in multiple languages for my news portal analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'Davlan/bert-base-multilingual-cased-ner-hrl\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'Davlan/bert-base-multilingual-cased-ner-hrl\')", \'api_arguments\': {\'model\': \'AutoModelForTokenClassification.from_pretrained(Davlan/bert-base-multilingual-cased-ner-hrl)\', \'tokenizer\': \'AutoTokenizer.from_pretrained(Davlan/bert-base-multilingual-cased-ner-hrl)\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline\'}, \'example_code\': \'example = Nader Jokhadar had given Syria the lead with a well-struck header in the seventh minute.; ner_results = nlp(example); print(ner_results)\', \'performance\': {\'dataset\': {\'Arabic\': \'ANERcorp\', \'German\': \'conll 2003\', \'English\': \'conll 2003\', \'Spanish\': \'conll 2002\', \'French\': \'Europeana Newspapers\', \'Italian\': \'Italian I-CAB\', \'Latvian\': \'Latvian NER\', \'Dutch\': \'conll 2002\', \'Portuguese\': \'Paramopama + Second Harem\', \'Chinese\': \'MSRA\'}, \'accuracy\': \'Not provided\'}, \'description\': \'bert-base-multilingual-cased-ner-hrl is a Named Entity Recognition model for 10 high resourced languages (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese and Chinese) based on a fine-tuned mBERT base model. It has been trained to recognize three types of entities: location (LOC), organizations (ORG), and person (PER).\'}', metadata={})]


 We are building a fiction-sharing platform for multiple languages. Extract named entities from the given text.\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={})]


 Someone asked the question \"What is the capital of Sweden?\" in the context \"Stockholm is the beautiful capital of Sweden, which is known for its high living standards and great attractions.\"\n###Input: {\"question\": \"What is the capital of Sweden?\", \"context\": \"Stockholm is the beautiful capital of Sweden, which is known for its high living standards and great attractions.\"}\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Question Answering\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'deepset/roberta-large-squad2\', \'api_call\': "pipeline(\'question-answering\', model=\'deepset/roberta-large-squad2\')", \'api_arguments\': [\'question\', \'context\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline; nlp = pipeline(\'question-answering\', model=\'deepset/roberta-large-squad2\'); nlp({\'question\': \'What is the capital of Germany?\', \'context\': \'Berlin is the capital of Germany.\'})", \'performance\': {\'dataset\': \'squad_v2\', \'accuracy\': \'Not provided\'}, \'description\': \'A pre-trained RoBERTa model for question answering tasks, specifically trained on the SQuAD v2 dataset. It can be used to answer questions based on a given context.\'}', metadata={})]


 Write an overview for astronomers about how Jupiter became the largest planet in our solar system.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Summarization\', \'api_name\': \'google/pegasus-large\', \'api_call\': "pipeline(\'summarization\', model=\'google/pegasus-large\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nsummarizer = pipeline(\'summarization\', model=\'google/pegasus-large\')\\nsummary = summarizer(\'your_text_here\')", \'performance\': {\'dataset\': [{\'name\': \'xsum\', \'accuracy\': \'47.60/24.83/39.64\'}, {\'name\': \'cnn_dailymail\', \'accuracy\': \'44.16/21.56/41.30\'}, {\'name\': \'newsroom\', \'accuracy\': \'45.98/34.20/42.18\'}, {\'name\': \'multi_news\', \'accuracy\': \'47.65/18.75/24.95\'}, {\'name\': \'gigaword\', \'accuracy\': \'39.65/20.47/36.76\'}, {\'name\': \'wikihow\', \'accuracy\': \'46.39/22.12/38.41\'}, {\'name\': \'reddit_tifu\', \'accuracy\': \'27.99/9.81/22.94\'}, {\'name\': \'big_patent\', \'accuracy\': \'52.29/33.08/41.66\'}, {\'name\': \'arxiv\', \'accuracy\': \'44.21/16.95/25.67\'}, {\'name\': \'pubmed\', \'accuracy\': \'45.97/20.15/28.25\'}, {\'name\': \'aeslc\', \'accuracy\': \'37.68/21.25/36.51\'}, {\'name\': \'billsum\', \'accuracy\': \'59.67/41.58/47.59\'}]}, \'description\': \'google/pegasus-large is a pre-trained model for abstractive text summarization based on the PEGASUS architecture. It is trained on a mixture of C4 and HugeNews datasets and uses a sentencepiece tokenizer that can encode newline characters. The model has been fine-tuned for various summarization tasks and achieves state-of-the-art performance on multiple benchmarks.\'}', metadata={})]


 Create a virtual assistant that can provide answers to trivia questions about history.\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={})]


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


 Our customer support team needs an AI assistant to handle customer inquiries. It should be able to understand and evaluate if the answer provided by the assistant is contradictory, neutral or entails the customer's question.\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={})]


 Use a zero-shot classifier to classify news headlines into three categories: sports, technology, and politics.\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\': \'cross-encoder/nli-deberta-v3-xsmall\', \'api_call\': "pipeline(\'zero-shot-classification\', model=\'cross-encoder/nli-deberta-v3-xsmall\')", \'api_arguments\': [\'sent\', \'candidate_labels\'], \'python_environment_requirements\': [\'sentence_transformers\', \'transformers\'], \'example_code\': "from transformers import pipeline\\nclassifier = pipeline(\'zero-shot-classification\', model=\'cross-encoder/nli-deberta-v3-xsmall\')\\nsent = \'Apple just announced the newest iPhone X\'\\ncandidate_labels = [\'technology\', \'sports\', \'politics\']\\nres = classifier(sent, candidate_labels)\\nprint(res)", \'performance\': {\'dataset\': {\'SNLI-test\': \'91.64\', \'MNLI_mismatched\': \'87.77\'}}, \'description\': \'This model is a Cross-Encoder for Natural Language Inference, trained on the SNLI and MultiNLI datasets. It can be used for zero-shot classification tasks.\'}', metadata={})]


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


 Create a tool to determine the logical relationship between two given sentences.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Sentence Similarity\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentence Transformers\', \'api_name\': \'sentence-transformers/distiluse-base-multilingual-cased-v2\', \'api_call\': "SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v2\')", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': \'pip install -U sentence-transformers\', \'example_code\': "from sentence_transformers import SentenceTransformer\\nsentences = [This is an example sentence, Each sentence is converted]\\nmodel = SentenceTransformer(\'sentence-transformers/distiluse-base-multilingual-cased-v2\')\\nembeddings = model.encode(sentences)\\nprint(embeddings)", \'performance\': {\'dataset\': \'https://seb.sbert.net\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\'}', metadata={})]


 A media company needs to summarize a news article in order to make it easy for their audience to understand the main points quickly.\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 want to implement a customer service chatbot to engage with website visitors and provide support.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


 As the project manager of a company who receives long emails, my goal is to summarize them and extract the most important information.\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 am the user and I want to start a multi-turn conversation with this model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


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


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


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


 The company wants to create a chatbot to help answer customer questions regarding the chatbot's consciousness. We need to be able to generate sensible responses.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'mywateriswet/ShuanBot\', \'api_call\': "pipeline(\'conversational\', model=\'mywateriswet/ShuanBot\')", \'api_arguments\': \'message\', \'python_environment_requirements\': \'transformers\', \'example_code\': "response = chatbot(\'What is your name?\')", \'performance\': {\'dataset\': \'N/A\', \'accuracy\': \'N/A\'}, \'description\': \'ShuanBot is a conversational chatbot model based on the GPT-2 architecture. It can be used for generating human-like responses in a chat context.\'}', metadata={})]


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