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


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


 Our team needs to develop a tool that can translate articles from English to German in real-time.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'sshleifer/tiny-marian-en-de\', \'api_call\': "pipeline(\'translation_en_to_de\', model=\'sshleifer/tiny-marian-en-de\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A tiny English to German translation model using the Marian framework in Hugging Face Transformers.\'}', metadata={})]


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


 Our designer hired me to create an application to automatically translate colors from a design. Usually, our designer likes to work with English, to get the color in Italian.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Translation\', \'api_name\': \'Helsinki-NLP/opus-mt-it-en\', \'api_call\': "pipeline(\'translation_it_to_en\', model=\'Helsinki-NLP/opus-mt-it-en\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_it_to_en\', model=\'Helsinki-NLP/opus-mt-it-en\')(\'Ciao mondo!\')", \'performance\': {\'dataset\': \'opus\', \'accuracy\': {\'BLEU\': {\'newssyscomb2009.it.en\': 35.3, \'newstest2009.it.en\': 34.0, \'Tatoeba.it.en\': 70.9}, \'chr-F\': {\'newssyscomb2009.it.en\': 0.6, \'newstest2009.it.en\': 0.594, \'Tatoeba.it.en\': 0.808}}}, \'description\': \'A transformer model for Italian to English translation trained on the OPUS dataset. It can be used for translating Italian text to English.\'}', metadata={})]


 I am building an AI to create in work report, this model should be able to complete the sentence for the report.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Transformers\', \'functionality\': \'Sentence Correction\', \'api_name\': \'flexudy/t5-base-multi-sentence-doctor\', \'api_call\': "AutoModelWithLMHead.from_pretrained(\'flexudy/t5-base-multi-sentence-doctor\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import AutoTokenizer, AutoModelWithLMHead\\ntokenizer = AutoTokenizer.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\nmodel = AutoModelWithLMHead.from_pretrained(flexudy/t5-base-multi-sentence-doctor)\\ninput_text = repair_sentence: m a medical doct context: {That is my job I a}{or I save lives} </s>\\ninput_ids = tokenizer.encode(input_text, return_tensors=pt)\\noutputs = model.generate(input_ids, max_length=32, num_beams=1)\\nsentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\\nassert sentence == I am a medical doctor.\', \'performance\': {\'dataset\': \'tatoeba\', \'accuracy\': \'Not specified\'}, \'description\': \'Sentence doctor is a T5 model that attempts to correct the errors or mistakes found in sentences. Model works on English, German and French text.\'}', metadata={})]


 A language learning application wants to generate fill-in-the-blank questions for learners. We need to mask sentences with a keyword to create these questions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Fill-Mask\', \'framework\': \'Transformers\', \'functionality\': \'Masked Language Modeling\', \'api_name\': \'bert-base-multilingual-cased\', \'api_call\': "pipeline(\'fill-mask\', model=\'bert-base-multilingual-cased\')", \'api_arguments\': \'text\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\nunmasker = pipeline(\'fill-mask\', model=\'bert-base-multilingual-cased\')\\nunmasker(Hello I\'m a [MASK] model.)", \'performance\': {\'dataset\': \'wikipedia\', \'accuracy\': \'Not provided\'}, \'description\': \'BERT multilingual base model (cased) is pretrained on the top 104 languages with the largest Wikipedia using a masked language modeling (MLM) objective. The model is case sensitive and can be used for masked language modeling or next sentence prediction. It is intended to be fine-tuned on a downstream task, such as sequence classification, token classification, or question answering.\'}', metadata={})]


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


 I am a data scientist and need to find which among given documents is similar, provide me the example code.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Multi-class Classification\', \'api_name\': \'Alexei1/imdb\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'IMDB\', \'accuracy\': 0.487}, \'description\': \'A tabular classification model trained using AutoTrain for sentiment analysis on the IMDB dataset. The model has a CO2 emission of 0.0186 grams and an accuracy of 0.487.\'}', metadata={})]


 My company wants to analyze and cluster different movie scripts. I need a suggestion to embed the movie dialogue text into dense vector representations.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'lvwerra/distilbert-imdb\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'lvwerra/distilbert-imdb\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\', \'pytorch\'], \'example_code\': "classifier(\'I love this movie!\')", \'performance\': {\'dataset\': \'imdb\', \'accuracy\': 0.928}, \'description\': \'This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It is used for sentiment analysis on movie reviews and achieves an accuracy of 0.928 on the evaluation set.\'}', metadata={})]


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


 We are a company specializing in building automation systems for homes. We'd like to convert our written rules into an audio format for user interaction purposes.\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 developing a virtual assistant. One of its major functionalities is to convert written text to speech.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Text-to-Speech\', \'framework\': \'ESPnet\', \'functionality\': \'Text-to-Speech\', \'api_name\': \'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={})]


 I have a recorded meeting between multiple participants. Identify when two or more people are speaking simultaneously.\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={})]


 I have a collection of audio recordings from interviews that I need transcribed into text. How can I achieve this using automatic speech recognition?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Automatic Speech Recognition\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Speech Recognition\', \'api_name\': \'jonatasgrosman/wav2vec2-large-xlsr-53-portuguese\', \'api_call\': "SpeechRecognitionModel(\'jonatasgrosman/wav2vec2-large-xlsr-53-portuguese\')", \'api_arguments\': [\'audio_paths\'], \'python_environment_requirements\': [\'huggingsound\', \'torch\', \'librosa\', \'datasets\', \'transformers\'], \'example_code\': \'from huggingsound import SpeechRecognitionModel\\nmodel = SpeechRecognitionModel(jonatasgrosman/wav2vec2-large-xlsr-53-portuguese)\\naudio_paths = [/path/to/file.mp3, /path/to/another_file.wav]\\ntranscriptions = model.transcribe(audio_paths)\', \'performance\': {\'dataset\': \'mozilla-foundation/common_voice_6_0\', \'accuracy\': {\'Test WER\': 11.31, \'Test CER\': 3.74, \'Test WER (+LM)\': 9.01, \'Test CER (+LM)\': 3.21}}, \'description\': \'Fine-tuned facebook/wav2vec2-large-xlsr-53 on Portuguese using the train and validation splits of Common Voice 6.1. When using this model, make sure that your speech input is sampled at 16kHz.\'}', metadata={})]


 A Russia-based online lesson website needs an English subtitle for the recorded lessons of the students.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation\', \'api_name\': \'opus-mt-en-ru\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'Helsinki-NLP/opus-mt-en-ru\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\', \'tensorflow\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_en_to_ru\', model=\'Helsinki-NLP/opus-mt-en-ru\')\\ntranslated_text = translation(\'Hello, how are you?\')", \'performance\': {\'dataset\': \'newstest2019-enru\', \'accuracy\': \'27.1\'}, \'description\': \'Helsinki-NLP/opus-mt-en-ru is a translation model trained on the OPUS dataset, which translates English text to Russian. It is based on the Marian NMT framework and can be used with Hugging Face Transformers.\'}', metadata={})]


 My lecture has been recorded, and I would like to transcribe the audio to create a transcript of my presentation.\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={})]


 Provide the code to enhance a single audio track, possibly containing dialogue, music and background noise, extracted from a video game.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Audio Audio-to-Audio\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Speech Enhancement\', \'api_name\': \'speechbrain/mtl-mimic-voicebank\', \'api_call\': "WaveformEnhancement.from_hparams(\'speechbrain/mtl-mimic-voicebank\', \'pretrained_models/mtl-mimic-voicebank\')", \'api_arguments\': [\'source\', \'savedir\'], \'python_environment_requirements\': \'pip install speechbrain\', \'example_code\': "import torchaudio\\nfrom speechbrain.pretrained import WaveformEnhancement\\nenhance_model = WaveformEnhancement.from_hparams(\\n source=speechbrain/mtl-mimic-voicebank,\\n savedir=pretrained_models/mtl-mimic-voicebank,\\n)\\nenhanced = enhance_model.enhance_file(speechbrain/mtl-mimic-voicebank/example.wav)\\ntorchaudio.save(\'enhanced.wav\', enhanced.unsqueeze(0).cpu(), 16000)", \'performance\': {\'dataset\': \'Voicebank\', \'accuracy\': {\'Test PESQ\': 3.05, \'Test COVL\': 3.74, \'Valid WER\': 2.89, \'Test WER\': 2.8}}, \'description\': \'This repository provides all the necessary tools to perform enhancement and\\nrobust ASR training (EN) within\\nSpeechBrain. For a better experience we encourage you to learn more about\\nSpeechBrain. The model performance is:\\nRelease\\nTest PESQ\\nTest COVL\\nValid WER\\nTest WER\\n22-06-21\\n3.05\\n3.74\\n2.89\\n2.80\\nWorks with SpeechBrain v0.5.12\'}', metadata={})]


 We're working with a voiceover company, and they're looking for a tool to help them change the voice style of voiceovers while keeping the same content.\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={})]


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


 The company needs to separate voice from background noise in a recorded podcast episode.\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={})]


 Develop a method to detect which languages are being spoken in international conference calls.\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={})]


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


 I am the director of a movie theater chain, and we are thinking of implementing a chat product to get movie goers to classify movie reviews as positive or negative through AI.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'lvwerra/distilbert-imdb\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'lvwerra/distilbert-imdb\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\', \'pytorch\'], \'example_code\': "classifier(\'I love this movie!\')", \'performance\': {\'dataset\': \'imdb\', \'accuracy\': 0.928}, \'description\': \'This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It is used for sentiment analysis on movie reviews and achieves an accuracy of 0.928 on the evaluation set.\'}', metadata={})]


 You have been provided with the dataset of plants, and your task is to identify the species of plants among Iris Setosa, Iris Versicolor, and Iris Virginica.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Transformers\', \'api_name\': \'abhishek/autotrain-iris-knn\', \'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\': \'scikit-learn/iris\', \'accuracy\': 0.9}, \'description\': \'A K-Nearest Neighbors (KNN) model trained on the Iris dataset for multi-class classification. The model is trained using AutoTrain and has an accuracy of 0.9.\'}', metadata={})]


 Our energy company is trying to identify anomalies in the energy consumption data. Could you perform anomaly detection on the time series data?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Keras\', \'functionality\': \'anomaly-detection\', \'api_name\': \'keras-io/timeseries-anomaly-detection\', \'api_call\': "TFAutoModelForSequenceClassification.from_pretrained(\'keras-io/timeseries-anomaly-detection\')", \'api_arguments\': {\'optimizer\': {\'name\': \'Adam\', \'learning_rate\': 0.001, \'decay\': 0.0, \'beta_1\': 0.9, \'beta_2\': 0.999, \'epsilon\': 1e-07, \'amsgrad\': False}, \'training_precision\': \'float32\'}, \'python_environment_requirements\': [\'tensorflow\', \'keras\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Numenta Anomaly Benchmark(NAB)\', \'accuracy\': {\'Train Loss\': 0.006, \'Validation Loss\': 0.008}}, \'description\': \'This script demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data. We will use the Numenta Anomaly Benchmark(NAB) dataset. It provides artifical timeseries data containing labeled anomalous periods of behavior. Data are ordered, timestamped, single-valued metrics.\'}', metadata={})]


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


 Create a program that predicts carbon emissions for new data using the given model.\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={})]


 A company has collected data about fish measurements and weights. We need to estimate a fish's weight based on its measurements.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Scikit-learn\', \'functionality\': \'GradientBoostingRegressor\', \'api_name\': \'Fish-Weight\', \'api_call\': "load(\'path_to_folder/example.pkl\')", \'api_arguments\': {\'model_path\': \'path_to_folder/example.pkl\'}, \'python_environment_requirements\': {\'skops.hub_utils\': \'download\', \'skops.io\': \'load\'}, \'example_code\': "from skops.hub_utils import download\\nfrom skops.io import load\\ndownload(\'brendenc/Fish-Weight\', \'path_to_folder\')\\nmodel = load(\'path_to_folder/example.pkl\')", \'performance\': {\'dataset\': \'Fish dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a GradientBoostingRegressor on a fish dataset. This model is intended for educational purposes.\'}', metadata={})]


 Our company manages a restaurant. We need to analyze customers' tips and predict how much tip a new customer would give based on their total bill, sex, smoker, day, time, and party size. We should use a pre-trained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Scikit-learn\', \'functionality\': \'baseline-trainer\', \'api_name\': \'merve/tips9y0jvt5q-tip-regression\', \'api_call\': "pipeline(\'tabular-regression\', model=\'merve/tips9y0jvt5q-tip-regression\')", \'api_arguments\': \'N/A\', \'python_environment_requirements\': \'dabl\', \'example_code\': \'N/A\', \'performance\': {\'dataset\': \'tips9y0jvt5q\', \'accuracy\': {\'r2\': 0.41524, \'neg_mean_squared_error\': -1.098792}}, \'description\': \'Baseline Model trained on tips9y0jvt5q to apply regression on tip. The model uses Ridge(alpha=10) and is trained with dabl library as a baseline. For better results, use AutoTrain.\'}', metadata={})]


 This time, we want to build a player against the user with reinforcement learning. Can you help?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning\', \'framework\': \'ML-Agents\', \'functionality\': \'SoccerTwos\', \'api_name\': \'Raiden-1001/poca-Soccerv7\', \'api_call\': "mlagents-load-from-hf --repo-id=\'Raiden-1001/poca-Soccerv7.1\' --local-dir=\'./downloads\'", \'api_arguments\': [\'your_configuration_file_path.yaml\', \'run_id\'], \'python_environment_requirements\': [\'unity-ml-agents\', \'deep-reinforcement-learning\', \'ML-Agents-SoccerTwos\'], \'example_code\': \'Step 1: Write your model_id: Raiden-1001/poca-Soccerv7\\nStep 2: Select your .nn /.onnx file\\nClick on Watch the agent play 👀\', \'performance\': {\'dataset\': \'SoccerTwos\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library.\'}', metadata={})]


 We are building a robot for indoor navigation. We need to configure it to recognize its surroundings and interact with objects in the environment.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Reinforcement Learning Robotics\', \'framework\': \'Hugging Face\', \'functionality\': \'Inference API\', \'api_name\': \'Antheia/Hanna\', \'api_call\': "pipeline(\'robotics\', model=\'Antheia/Hanna\')", \'api_arguments\': \'model\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'openai/webgpt_comparisons\', \'accuracy\': \'\'}, \'description\': \'Antheia/Hanna is a reinforcement learning model for robotics tasks, trained on the openai/webgpt_comparisons dataset.\'}', metadata={})]


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


 A product is built that analyzes book reviews in order to determine how similar two examples from multiple books are to each other.\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={})]


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


 We need to extract useful features from Russian text for downstream tasks.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Translation\', \'api_name\': \'opus-mt-en-ru\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'Helsinki-NLP/opus-mt-en-ru\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'torch\', \'tensorflow\'], \'example_code\': "from transformers import pipeline\\ntranslation = pipeline(\'translation_en_to_ru\', model=\'Helsinki-NLP/opus-mt-en-ru\')\\ntranslated_text = translation(\'Hello, how are you?\')", \'performance\': {\'dataset\': \'newstest2019-enru\', \'accuracy\': \'27.1\'}, \'description\': \'Helsinki-NLP/opus-mt-en-ru is a translation model trained on the OPUS dataset, which translates English text to Russian. It is based on the Marian NMT framework and can be used with Hugging Face Transformers.\'}', metadata={})]


 We are working on a project to detect patterns and correlations in a dataset that contains a mix of code segments and comments. Find a suitable model to complete this task.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Regression\', \'framework\': \'Hugging Face\', \'functionality\': \'Carbon Emissions\', \'api_name\': \'kochetkovIT/autotrain-ironhack-49741119788\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'kochetkovIT/autotrain-data-ironhack\', \'accuracy\': {\'Loss\': 2.603, \'R2\': 0.013, \'MSE\': 6.776, \'MAE\': 1.666, \'RMSLE\': 0.502}}, \'description\': \'A tabular regression model trained using AutoTrain to predict carbon emissions (in grams) with an R2 score of 0.013.\'}', metadata={})]


 The marketing department wants to use AI-generated images for their next social media campaign. They want a high-resolution image of a vintage sports car racing through a desert landscape during sunset. \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={})]


 My company is working on a children's storybook. I need to generate images based on the text descriptions of scenes in the story.\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={})]


 Design a text-based interface where you can input text and get an image description from the given text.\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 am designing an e-commerce website, and I want a program to generate images based on written descriptions to be used as mock product images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image\', \'api_name\': \'Lykon/DreamShaper\', \'api_call\': "pipeline(\'text-to-image\', model=Lykon/DreamShaper)", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers, torch\', \'example_code\': \'https://huggingface.co/spaces/Lykon/DreamShaper-webui\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'Dream Shaper is a text-to-image model that generates artistic images based on the given input text. Read more about this model here: https://civitai.com/models/4384/dreamshaper\'}', metadata={})]


 In our company, we have to generate descriptive captions for photographs related to the products.\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={})]


 We are building a visual tour guide application for smartphones. The app should be able to identify landmarks and provide information about them. I want to use the BLIP-2 model for this task.\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={})]


 I need a tool to analyze a chart image and generate a summary of the information contained within the chart.\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-xsum\', \'api_call\': "pipeline(\'summarization\', model=\'google/pegasus-xsum\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': [{\'name\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 46.862, \'ROUGE-2\': 24.453, \'ROUGE-L\': 39.055, \'ROUGE-LSUM\': 39.099}}, {\'name\': \'cnn_dailymail\', \'accuracy\': {\'ROUGE-1\': 22.206, \'ROUGE-2\': 7.67, \'ROUGE-L\': 15.405, \'ROUGE-LSUM\': 19.218}}, {\'name\': \'samsum\', \'accuracy\': {\'ROUGE-1\': 21.81, \'ROUGE-2\': 4.253, \'ROUGE-L\': 17.447, \'ROUGE-LSUM\': 18.891}}]}, \'description\': \'PEGASUS is a pre-trained model for abstractive summarization, developed by Google. It is based on the Transformer architecture and trained on both C4 and HugeNews datasets. The model is designed to extract gap sentences and generate summaries by stochastically sampling important sentences.\'}', metadata={})]


 Generate a video for a cooking show intro with the text \"Chef John's Culinary Adventures.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'chavinlo/TempoFunk\', \'api_call\': "pipeline(\'text-to-video\', model=\'chavinlo/TempoFunk\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Text-to-Video model using Hugging Face Transformers library. Model is capable of generating video content based on the input text.\'}', metadata={})]


 We are doing a creative commercial. We require a video that represents a person walking along a beach.\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={})]


 Help me to build a self-learning assistant capable of answering questions about an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Image-to-Text\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'promptcap-coco-vqa\', \'api_call\': "PromptCap(\'vqascore/promptcap-coco-vqa\')", \'api_arguments\': {\'prompt\': \'string\', \'image\': \'string\'}, \'python_environment_requirements\': \'pip install promptcap\', \'example_code\': [\'import torch\', \'from promptcap import PromptCap\', \'model = PromptCap(vqascore/promptcap-coco-vqa)\', \'if torch.cuda.is_available():\', \'  model.cuda()\', \'prompt = please describe this image according to the given question: what piece of clothing is this boy putting on?\', \'image = glove_boy.jpeg\', \'print(model.caption(prompt, image))\'], \'performance\': {\'dataset\': {\'coco\': {\'accuracy\': \'150 CIDEr\'}, \'OK-VQA\': {\'accuracy\': \'60.4%\'}, \'A-OKVQA\': {\'accuracy\': \'59.6%\'}}}, \'description\': \'PromptCap is a captioning model that can be controlled by natural language instruction. The instruction may contain a question that the user is interested in. It achieves SOTA performance on COCO captioning (150 CIDEr) and knowledge-based VQA tasks when paired with GPT-3 (60.4% on OK-VQA and 59.6% on A-OKVQA).\'}', metadata={})]


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


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


 Our company focuses on predicting molecular properties. We need the best model to tackle this problem for our project. We heard about Graphormer and want to start with a pretrained model.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Graph Machine Learning\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'graphormer-base-pcqm4mv1\', \'api_call\': "AutoModel.from_pretrained(\'graphormer-base-pcqm4mv1\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'See the Graph Classification with Transformers tutorial\', \'performance\': {\'dataset\': \'PCQM4M-LSC\', \'accuracy\': \'1st place on the KDD CUP 2021 (quantum prediction track)\'}, \'description\': \'The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSC, and which got 1st place on the KDD CUP 2021 (quantum prediction track). Developed by Microsoft, this model should be used for graph classification tasks or graph representation tasks; the most likely associated task is molecule modeling. It can either be used as such, or finetuned on downstream tasks.\'}', metadata={})]


 We have a company that processes loan applications. Give us the answer for a question, based on a document.\n###Input: Our company policy restricts the loan applicant's eligibility to the citizens of United States. The applicant needs to be 18 years old or above and their monthly salary should at least be $4,000. FetchTypeOfYear: 2019. Can anyone with a monthly salary of $3,000 apply?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Document Question Answering\', \'api_name\': \'CQI_Visual_Question_Awnser_PT_v0\', \'api_call\': "pipeline(\'question-answering\', model=LayoutLMForQuestionAnswering.from_pretrained(\'microsoft/layoutlm-base-uncased\'))", \'api_arguments\': [\'url\', \'question\'], \'python_environment_requirements\': [\'PIL\', \'pytesseract\', \'PyTorch\', \'transformers\'], \'example_code\': ["nlp(\'https://templates.invoicehome.com/invoice-template-us-neat-750px.png\', \'What is the invoice number?\')", "nlp(\'https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg\', \'What is the purchase amount?\')", "nlp(\'https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png\', \'What are the 2020 net sales?\')"], \'performance\': {\'dataset\': [{\'accuracy\': 0.9943977}, {\'accuracy\': 0.9912159}, {\'accuracy\': 0.59147286}]}, \'description\': \'A model for visual question answering in Portuguese and English, capable of processing PDFs and images to extract information and answer questions.\'}', metadata={})]


 Our company is setting up an online shop for selling computer parts. We need a system that can identify the components in images uploaded by users.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'clip-vit-base-patch32-ko\', \'api_call\': "pipeline(\'zero-shot-image-classification\', model=\'Bingsu/clip-vit-base-patch32-ko\')", \'api_arguments\': {\'images\': \'url\', \'candidate_labels\': \'Array of strings\', \'hypothesis_template\': \'String\'}, \'python_environment_requirements\': [\'transformers\', \'torch\', \'PIL\', \'requests\'], \'example_code\': "from transformers import pipeline\\nrepo = \'Bingsu/clip-vit-base-patch32-ko\'\\npipe = pipeline(\'zero-shot-image-classification\', model=repo)\\nurl = \'http://images.cocodataset.org/val2017/000000039769.jpg\'\\nresult = pipe(images=url, candidate_labels=[], hypothesis_template=\'{}\')\\nresult", \'performance\': {\'dataset\': \'AIHUB\', \'accuracy\': \'Not provided\'}, \'description\': \'Korean CLIP model trained by Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. It is a zero-shot image classification model that can be used to classify images without any training data.\'}', metadata={})]


 I want to classify the images of houseplants to find out their type, like whether it's a cactus, fern, or succulent.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 I have few picture from my album, and I would like to find out whether it is a hotdog or not.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'julien-c/hotdog-not-hotdog\', \'api_call\': "pipeline(\'image-classification\', model=\'julien-c/hotdog-not-hotdog\')", \'api_arguments\': \'image\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': 0.825}, \'description\': \'A model that classifies images as hotdog or not hotdog.\'}', metadata={})]


 Our client is building an application that converts scanned tables into structured data. We need to detect the rows and columns in a given table image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'microsoft/table-transformer-structure-recognition\', \'api_call\': "pipeline(\'object-detection\', model=\'microsoft/table-transformer-structure-recognition\')", \'api_arguments\': \'\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'PubTables1M\', \'accuracy\': \'\'}, \'description\': \'Table Transformer (DETR) model trained on PubTables1M for detecting the structure (like rows, columns) in tables.\'}', metadata={})]


 I am hosting a vegan food event and I want to know if any of the dishes contains meat from the picture I have taken.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'GreeneryScenery/SheepsControlV5\', \'api_call\': "pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV5\')", \'api_arguments\': {\'input_image\': \'path/to/image/file\'}, \'python_environment_requirements\': {\'huggingface_hub\': \'>=0.0.17\', \'transformers\': \'>=4.13.0\', \'torch\': \'>=1.10.0\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'poloclub/diffusiondb\', \'accuracy\': \'Not provided\'}, \'description\': \'SheepsControlV5 is an image-to-image model trained on the poloclub/diffusiondb dataset. It is designed for transforming input images into a different style or representation.\'}', metadata={})]


 In a kitchen, as a robot chef, we need to identify different objects such as fruits and dishes.\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 need to segment clothes in photos for our online fashion store.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Segmentation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Segmentation\', \'api_name\': \'mattmdjaga/segformer_b2_clothes\', \'api_call\': "SegformerForSemanticSegmentation.from_pretrained(\'mattmdjaga/segformer_b2_clothes\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'transformers\', \'PIL\', \'requests\', \'matplotlib\', \'torch\'], \'example_code\': "from transformers import AutoFeatureExtractor, SegformerForSemanticSegmentation\\nfrom PIL import Image\\nimport requests\\nimport matplotlib.pyplot as plt\\nimport torch.nn as nn\\nextractor = AutoFeatureExtractor.from_pretrained(\'mattmdjaga/segformer_b2_clothes\')\\nmodel = SegformerForSemanticSegmentation.from_pretrained(\'mattmdjaga/segformer_b2_clothes\')\\nurl = \'https://plus.unsplash.com/premium_photo-1673210886161-bfcc40f54d1f?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8cGVyc29uJTIwc3RhbmRpbmd8ZW58MHx8MHx8&w=1000&q=80\'\\nimage = Image.open(requests.get(url, stream=True).raw)\\ninputs = extractor(images=image, return_tensors=\'pt\')\\noutputs = model(**inputs)\\nlogits = outputs.logits.cpu()\\nupsampled_logits = nn.functional.interpolate(logits, size=image.size[::-1], mode=\'bilinear\', align_corners=False)\\npred_seg = upsampled_logits.argmax(dim=1)[0]\\nplt.imshow(pred_seg)", \'performance\': {\'dataset\': \'mattmdjaga/human_parsing_dataset\', \'accuracy\': \'Not provided\'}, \'description\': \'SegFormer model fine-tuned on ATR dataset for clothes segmentation.\'}', metadata={})]


 Automate the process of creating a normal map from a given object image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Normal Map Estimation\', \'api_name\': \'lllyasviel/sd-controlnet-normal\', \'api_call\': "ControlNetModel.from_pretrained(\'lllyasviel/sd-controlnet-normal\')", \'api_arguments\': [\'image\', \'num_inference_steps\'], \'python_environment_requirements\': [\'diffusers\', \'transformers\', \'accelerate\'], \'example_code\': "from PIL import Image\\nfrom transformers import pipeline\\nimport numpy as np\\nimport cv2\\nfrom diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler\\nimport torch\\nfrom diffusers.utils import load_image\\nimage = load_image(https://huggingface.co/lllyasviel/sd-controlnet-normal/resolve/main/images/toy.png).convert(RGB)\\ndepth_estimator = pipeline(depth-estimation, model =Intel/dpt-hybrid-midas )\\nimage = depth_estimator(image)[\'predicted_depth\'][0]\\nimage = image.numpy()\\nimage_depth = image.copy()\\nimage_depth -= np.min(image_depth)\\nimage_depth /= np.max(image_depth)\\nbg_threhold = 0.4\\nx = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)\\nx[image_depth < bg_threhold] = 0\\ny = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)\\ny[image_depth < bg_threhold] = 0\\nz = np.ones_like(x) * np.pi * 2.0\\nimage = np.stack([x, y, z], axis=2)\\nimage /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5\\nimage = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)\\nimage = Image.fromarray(image)\\ncontrolnet = ControlNetModel.from_pretrained(\\n fusing/stable-diffusion-v1-5-controlnet-normal, torch_dtype=torch.float16\\n)\\npipe = StableDiffusionControlNetPipeline.from_pretrained(\\n runwayml/stable-diffusion-v1-5, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16\\n)\\npipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)\\npipe.enable_xformers_memory_efficient_attention()\\npipe.enable_model_cpu_offload()\\nimage = pipe(cute toy, image, num_inference_steps=20).images[0]\\nimage.save(\'images/toy_normal_out.png\')", \'performance\': {\'dataset\': \'DIODE\', \'accuracy\': \'Not provided\'}, \'description\': \'ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Normal Map Estimation. It can be used in combination with Stable Diffusion.\'}', metadata={})]


 We have a picture of a landscape, and we'd like to add a building and a river in this picture.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Image-to-Image\', \'api_name\': \'GreeneryScenery/SheepsControlV3\', \'api_call\': "pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV3\')", \'api_arguments\': {\'image\': \'Path to image file\', \'text_guidance\': \'Optional text guidance for the model\'}, \'python_environment_requirements\': {\'transformers\': \'latest\', \'torch\': \'latest\'}, \'example_code\': [\'from transformers import pipeline\', "model = pipeline(\'image-to-image\', model=\'GreeneryScenery/SheepsControlV3\')", "result = model({\'image\': \'path/to/image.jpg\', \'text_guidance\': \'Optional text guidance\'})"], \'performance\': {\'dataset\': \'GreeneryScenery/SheepsControlV3\', \'accuracy\': \'Not provided\'}, \'description\': \'GreeneryScenery/SheepsControlV3 is a model for image-to-image tasks. It can be used to generate images based on the input image and optional text guidance. The model has some limitations, such as the conditioning image not affecting the output image much. Improvements can be made by training for more epochs, using better prompts, and preprocessing the data.\'}', metadata={})]


 Our client, an architecture firm, needs a rendering of a \"luxury living room with a fireplace\" as a visualization for a prestigious project.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Document Question Answer\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Question Answering\', \'api_name\': \'impira/layoutlm-invoices\', \'api_call\': "pipeline(\'question-answering\', model=\'impira/layoutlm-invoices\')", \'api_arguments\': \'question, context\', \'python_environment_requirements\': \'transformers\', \'example_code\': "qa_pipeline(question=\'your question\', context=\'your document context\')", \'performance\': {\'dataset\': \'proprietary dataset of invoices, SQuAD2.0, and DocVQA\', \'accuracy\': \'not provided\'}, \'description\': \'This is 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 (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional classifier head.\'}', metadata={})]


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


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


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


 Recently, my company is asked to produce new portraits for the entire team. Therefore, I am looking for an AI which can generate human faces from scratch.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Conversational\', \'api_name\': \'facebook/blenderbot-3B\', \'api_call\': "BlenderbotForConditionalGeneration.from_pretrained(\'facebook/blenderbot-3B\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'Input a message to start chatting with facebook/blenderbot-3B.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'BlenderBot-3B is a large-scale neural model designed for open-domain chatbot applications. It is trained on the blended_skill_talk dataset and can engage in multi-turn conversations, providing engaging talking points, asking and answering questions, and displaying knowledge, empathy, and personality. The model is available through the Hugging Face Transformers library.\'}', metadata={})]


 Our team wants to make a cat-themed video game. We would like to design various cartoon cat characters through AI-generated images.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'saltacc/anime-ai-detect\', \'api_call\': "pipeline(\'image-classification\', model=\'saltacc/anime-ai-detect\')", \'api_arguments\': [\'image\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'aibooru and imageboard sites\', \'accuracy\': \'96%\'}, \'description\': \'A BEiT classifier to see if anime art was made by an AI or a human.\'}', metadata={})]


 Can you teach me to build a program that will analyze video and describe what's happening in natural language?\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'ImRma/Brucelee\', \'api_call\': "pipeline(\'text-to-video\', model=\'ImRma/Brucelee\')", \'api_arguments\': [\'your_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'A Hugging Face model for converting Persian and English text into video.\'}', metadata={})]


 In a security context, the company is starting a project to detect any signs of violence in video streams from its CCTV cameras. It's essential to analyze the video content for any violent behaviors.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'videomae-base-finetuned-RealLifeViolenceSituations-subset\', \'api_call\': "AutoModelForVideoClassification.from_pretrained(\'dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset\')", \'api_arguments\': {\'model_name\': \'dangle124/videomae-base-finetuned-RealLifeViolenceSituations-subset\'}, \'python_environment_requirements\': {\'transformers\': \'4.27.2\', \'pytorch\': \'1.13.1\', \'datasets\': \'2.10.1\', \'tokenizers\': \'0.13.2\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'unknown\', \'accuracy\': 0.9533}, \'description\': \'This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It is trained for video classification task, specifically for RealLifeViolenceSituations.\'}', metadata={})]


 Create a sports highlight generator that takes video clips as input and identifies the category of the sports activity happening in the video.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'patrickjohncyh/fashion-clip\', \'api_call\': "CLIPModel.from_pretrained(\'patrickjohncyh/fashion-clip\')", \'api_arguments\': {\'image\': \'File\', \'class_names\': \'String (comma-separated)\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import CLIPProcessor, CLIPModel; model = CLIPModel.from_pretrained(\'patrickjohncyh/fashion-clip\'); processor = CLIPProcessor.from_pretrained(\'patrickjohncyh/fashion-clip\'); inputs = processor(text=\'blue shoes\', images=image, return_tensors=\'pt\', padding=True); logits_per_image = model(**inputs).logits_per_image; probs = logits_per_image.softmax(dim=-1).tolist()[0]", \'performance\': {\'dataset\': [{\'name\': \'FMNIST\', \'accuracy\': 0.83}, {\'name\': \'KAGL\', \'accuracy\': 0.73}, {\'name\': \'DEEP\', \'accuracy\': 0.62}]}, \'description\': \'FashionCLIP is a CLIP-based model developed to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by OpenAI, it is trained on a large, high-quality novel fashion dataset to study whether domain specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks.\'}', metadata={})]


 I am a movie director and I need to detect the genre of a movie based on its actions.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Tabular Tabular Classification\', \'framework\': \'Joblib\', \'functionality\': \'Multi-class Classification\', \'api_name\': \'Alexei1/imdb\', \'api_call\': "joblib.load(\'model.joblib\')", \'api_arguments\': [\'data\'], \'python_environment_requirements\': [\'json\', \'joblib\', \'pandas\'], \'example_code\': "import json\\nimport joblib\\nimport pandas as pd\\nmodel = joblib.load(\'model.joblib\')\\nconfig = json.load(open(\'config.json\'))\\nfeatures = config[\'features\']\\ndata = pd.read_csv(\'data.csv\')\\ndata = data[features]\\ndata.columns = [\'feat_\' + str(col) for col in data.columns]\\npredictions = model.predict(data)", \'performance\': {\'dataset\': \'IMDB\', \'accuracy\': 0.487}, \'description\': \'A tabular classification model trained using AutoTrain for sentiment analysis on the IMDB dataset. The model has a CO2 emission of 0.0186 grams and an accuracy of 0.487.\'}', metadata={})]


 Bob is designing an app for his city. He needs to quickly identify if the provided image has a bike or a car. Design a model that recognizes a bike or car and provides classification output for the given image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'abhishek/autotrain-dog-vs-food\', \'api_call\': "pipeline(\'image-classification\', model=\'abhishek/autotrain-dog-vs-food\')", \'api_arguments\': \'image_path\', \'python_environment_requirements\': \'transformers\', \'example_code\': \'\', \'performance\': {\'dataset\': \'sasha/dog-food\', \'accuracy\': 0.998}, \'description\': "A pre-trained model for classifying images as either dog or food using Hugging Face\'s AutoTrain framework."}', metadata={})]


 I need to analyze the sentiment of tweets to find out which products are getting positive customer feedback.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'cardiffnlp/twitter-roberta-base-sentiment-latest\', \'api_call\': "pipeline(sentiment-analysis, model=AutoModel.from_pretrained(\'cardiffnlp/twitter-roberta-base-sentiment-latest\'), tokenizer=AutoTokenizer.from_pretrained(\'cardiffnlp/twitter-roberta-base-sentiment-latest\'))", \'api_arguments\': {\'model\': \'model_path\', \'tokenizer\': \'model_path\'}, \'python_environment_requirements\': [\'transformers\', \'numpy\', \'scipy\'], \'example_code\': \'from transformers import pipeline\\nsentiment_task = pipeline(sentiment-analysis, model=model_path, tokenizer=model_path)\\nsentiment_task(Covid cases are increasing fast!)\', \'performance\': {\'dataset\': \'tweet_eval\', \'accuracy\': \'Not provided\'}, \'description\': \'This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark. The model is suitable for English.\'}', metadata={})]


 We are building a content moderation system. Our clients upload the content, it can be generated by human or AI. We want to have a filtering API to advise on the original text if it is generated by GPT-2.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'gpt2\', \'api_call\': "pipeline(\'text-generation\', model=\'gpt2\')", \'api_arguments\': [\'text\', \'max_length\', \'num_return_sequences\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import pipeline, set_seed\\ngenerator = pipeline(\'text-generation\', model=\'gpt2\')\\nset_seed(42)\\ngenerator(Hello, I\'m a language model,, max_length=30, num_return_sequences=5)", \'performance\': {\'dataset\': {\'LAMBADA\': {\'accuracy\': \'35.13\'}, \'CBT-CN\': {\'accuracy\': \'45.99\'}, \'CBT-NE\': {\'accuracy\': \'87.65\'}, \'WikiText2\': {\'accuracy\': \'83.4\'}, \'PTB\': {\'accuracy\': \'29.41\'}, \'enwiki8\': {\'accuracy\': \'65.85\'}, \'text8\': {\'accuracy\': \'1.16\'}, \'WikiText103\': {\'accuracy\': \'1.17\'}, \'1BW\': {\'accuracy\': \'37.50\'}}}, \'description\': \'GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences.\'}', metadata={})]


 I have a list of comments related to stocks. Check the overall sentiment towards each stock.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Sentiment Inferencing for stock-related comments\', \'api_name\': \'zhayunduo/roberta-base-stocktwits-finetuned\', \'api_call\': "RobertaForSequenceClassification.from_pretrained(\'zhayunduo/roberta-base-stocktwits-finetuned\')", \'api_arguments\': {\'model\': \'RobertaForSequenceClassification\', \'tokenizer\': \'RobertaTokenizer\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import RobertaForSequenceClassification, RobertaTokenizer\\nfrom transformers import pipeline\\nimport pandas as pd\\nimport emoji\\ntokenizer_loaded = RobertaTokenizer.from_pretrained(\'zhayunduo/roberta-base-stocktwits-finetuned\')\\nmodel_loaded = RobertaForSequenceClassification.from_pretrained(\'zhayunduo/roberta-base-stocktwits-finetuned\')\\nnlp = pipeline(text-classification, model=model_loaded, tokenizer=tokenizer_loaded)\\nsentences = pd.Series([\'just buy\',\'just sell it\',\'entity rocket to the sky!\',\'go down\',\'even though it is going up, I still think it will not keep this trend in the near future\'])\\nsentences = list(sentences)\\nresults = nlp(sentences)\\nprint(results)", \'performance\': {\'dataset\': \'stocktwits\', \'accuracy\': 0.9343}, \'description\': "This model is fine-tuned with roberta-base model on 3,200,000 comments from stocktwits, with the user-labeled tags \'Bullish\' or \'Bearish\'."}', metadata={})]


 I'd like to give a potted plant to my friend. Write me a short and simple plant care instruction.\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={})]