 Design a feature for a social media website to recommend articles to users based on how similar the articles are to their previously liked articles.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'csebuetnlp/mT5_multilingual_XLSum\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'csebuetnlp/mT5_multilingual_XLSum\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers==4.11.0.dev0\'], \'example_code\': "import re\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nWHITESPACE_HANDLER = lambda k: re.sub(\'\\\\s+\', \' \', re.sub(\'\\\\n+\', \' \', k.strip()))\\narticle_text = Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people\'s scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs spill over into misinformation about vaccines in general. The new policy covers long-approved vaccines, such as those against measles or hepatitis B. We\'re expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO, the post said, referring to the World Health Organization.\\nmodel_name = csebuetnlp/mT5_multilingual_XLSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\\ninput_ids = tokenizer(\\n [WHITESPACE_HANDLER(article_text)],\\n return_tensors=pt,\\n padding=max_length,\\n truncation=True,\\n max_length=512\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n max_length=84,\\n no_repeat_ngram_size=2,\\n num_beams=4\\n)[0]\\nsummary = tokenizer.decode(\\n output_ids,\\n skip_special_tokens=True,\\n clean_up_tokenization_spaces=False\\n)\\nprint(summary)", \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 36.5, \'ROUGE-2\': 13.934, \'ROUGE-L\': 28.988, \'ROUGE-LSUM\': 28.996, \'loss\': 2.067, \'gen_len\': 26.973}}, \'description\': \'This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. It is a multilingual abstractive summarization model that supports text-to-text generation for 43 languages.\'}', metadata={})]


 The user is interested in a tool to find relationships between medical terms.\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={})]


 As a journalist, I am curious about speech sentiment analysis in a group of people in a crowd. I want to extract features from the audio to run sentiment analysis.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Sentiment Analysis\', \'api_name\': \'michellejieli/NSFW_text_classifier\', \'api_call\': "pipeline(\'sentiment-analysis\', model=\'michellejieli/NSFW_text_classification\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'classifier(I see you’ve set aside this special time to humiliate yourself in public.)\', \'performance\': {\'dataset\': \'Reddit posts\', \'accuracy\': \'Not specified\'}, \'description\': \'DistilBERT is a transformer model that performs sentiment analysis. I fine-tuned the model on Reddit posts with the purpose of classifying not safe for work (NSFW) content, specifically text that is considered inappropriate and unprofessional. The model predicts 2 classes, which are NSFW or safe for work (SFW). The model is a fine-tuned version of DistilBERT. It was fine-tuned on 14317 Reddit posts pulled from the Reddit API.\'}', metadata={})]


 A chat service needs a way to compare and cluster similar sentences from users in different languages. Find a suitable feature extraction method to achieve this.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Image Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Image Classification\', \'api_name\': \'microsoft/swinv2-tiny-patch4-window8-256\', \'api_call\': "AutoModelForImageClassification.from_pretrained(\'microsoft/swinv2-tiny-patch4-window8-256\')", \'api_arguments\': {\'image\': \'http://images.cocodataset.org/val2017/000000039769.jpg\'}, \'python_environment_requirements\': [\'transformers\', \'PIL\', \'requests\'], \'example_code\': \'from transformers import AutoImageProcessor, AutoModelForImageClassification\\nfrom PIL import Image\\nimport requests\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\nprocessor = AutoImageProcessor.from_pretrained(microsoft/swinv2-tiny-patch4-window8-256)\\nmodel = AutoModelForImageClassification.from_pretrained(microsoft/swinv2-tiny-patch4-window8-256)\\ninputs = processor(images=image, return_tensors=pt)\\noutputs = model(**inputs)\\nlogits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(Predicted class:, model.config.id2label[predicted_class_idx])\', \'performance\': {\'dataset\': \'imagenet-1k\', \'accuracy\': \'Not provided\'}, \'description\': \'Swin Transformer v2 model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in the paper Swin Transformer V2: Scaling Up Capacity and Resolution by Liu et al. and first released in this repository. The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window. Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.\'}', metadata={})]


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


 We need a product description for an image-based online store platform that will help customers understand the specifics of the product.\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={})]


 Create a program to generate a description for an image provided as input.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Image\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Image Generation\', \'api_name\': \'stabilityai/stable-diffusion-2\', \'api_call\': "StableDiffusionPipeline.from_pretrained(\'stabilityai/stable-diffusion-2\', scheduler=EulerDiscreteScheduler.from_pretrained(\'stabilityai/stable-diffusion-2\', subfolder=scheduler), torch_dtype=torch.float16)", \'api_arguments\': {\'prompt\': \'a photo of an astronaut riding a horse on mars\'}, \'python_environment_requirements\': [\'diffusers\', \'transformers\', \'accelerate\', \'scipy\', \'safetensors\'], \'example_code\': \'from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler\\nmodel_id = stabilityai/stable-diffusion-2\\nscheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=scheduler)\\npipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)\\npipe = pipe.to(cuda)\\nprompt = a photo of an astronaut riding a horse on mars\\nimage = pipe(prompt).images[0]\\nimage.save(astronaut_rides_horse.png)\', \'performance\': {\'dataset\': \'COCO2017 validation set\', \'accuracy\': \'Not optimized for FID scores\'}, \'description\': \'Stable Diffusion v2 is a diffusion-based text-to-image generation model that can generate and modify images based on text prompts. It uses a fixed, pretrained text encoder (OpenCLIP-ViT/H) and is primarily intended for research purposes, such as safe deployment of models with potential to generate harmful content, understanding limitations and biases of generative models, and generation of artworks for design and artistic processes.\'}', metadata={})]


 I am a financial analyst, and I receive report after report filled with charts helping to explain trends and data in my field. However, I also need to have this information in tabular format. Please help me extract a linearized table from this chart.\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={})]


 We are building an automatic video generation platform based on user-provided text. We need a reliable model to convert text instructions into appropriate videos.\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={})]


 How can I extract video content from a text file? Provide a code sample to generate the video based on the text.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Text-to-Video\', \'framework\': \'Hugging Face\', \'functionality\': \'Text-to-Video\', \'api_name\': \'camenduru/text2-video-zero\', \'api_call\': "pipeline(\'text-to-video\', model=\'camenduru/text2-video-zero\')", \'api_arguments\': [\'input_text\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'\', \'accuracy\': \'\'}, \'description\': \'This model is used for generating videos from text inputs. It is based on the Hugging Face framework and can be used with the transformers library. The model is trained on a variety of text and video datasets, and can be used for tasks such as video summarization, video generation from text prompts, and more.\'}', metadata={})]


 We are developing a mobile app to demonstrate the AI's ability to generate a short video from text. The app focuses on processing written stories into video.\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={})]


 Hey, I want to analyze images in my phone gallery and answer questions about them.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Visual Question Answering\', \'framework\': \'Hugging Face\', \'functionality\': \'Visual Question Answering\', \'api_name\': \'JosephusCheung/GuanacoVQAOnConsumerHardware\', \'api_call\': "pipeline(\'visual-question-answering\', model=\'JosephusCheung/GuanacoVQAOnConsumerHardware\')", \'api_arguments\': {\'model\': \'JosephusCheung/GuanacoVQAOnConsumerHardware\', \'tokenizer\': \'JosephusCheung/GuanacoVQAOnConsumerHardware\'}, \'python_environment_requirements\': {\'transformers\': \'latest\', \'torch\': \'latest\'}, \'example_code\': \'vqa(image_path, question)\', \'performance\': {\'dataset\': \'JosephusCheung/GuanacoVQADataset\', \'accuracy\': \'unknown\'}, \'description\': \'A Visual Question Answering model trained on the GuanacoVQADataset, designed to work on consumer hardware like Colab Free T4 GPU. The model can be used to answer questions about images.\'}', metadata={})]


 My company wants to develop an application that will analyze images in relation to food and answer questions about them. We want it to handle questions like \"what is in the dish\" and \"how many calories does it have\".\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'csebuetnlp/mT5_multilingual_XLSum\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'csebuetnlp/mT5_multilingual_XLSum\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers==4.11.0.dev0\'], \'example_code\': "import re\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nWHITESPACE_HANDLER = lambda k: re.sub(\'\\\\s+\', \' \', re.sub(\'\\\\n+\', \' \', k.strip()))\\narticle_text = Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people\'s scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs spill over into misinformation about vaccines in general. The new policy covers long-approved vaccines, such as those against measles or hepatitis B. We\'re expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO, the post said, referring to the World Health Organization.\\nmodel_name = csebuetnlp/mT5_multilingual_XLSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\\ninput_ids = tokenizer(\\n [WHITESPACE_HANDLER(article_text)],\\n return_tensors=pt,\\n padding=max_length,\\n truncation=True,\\n max_length=512\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n max_length=84,\\n no_repeat_ngram_size=2,\\n num_beams=4\\n)[0]\\nsummary = tokenizer.decode(\\n output_ids,\\n skip_special_tokens=True,\\n clean_up_tokenization_spaces=False\\n)\\nprint(summary)", \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 36.5, \'ROUGE-2\': 13.934, \'ROUGE-L\': 28.988, \'ROUGE-LSUM\': 28.996, \'loss\': 2.067, \'gen_len\': 26.973}}, \'description\': \'This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. It is a multilingual abstractive summarization model that supports text-to-text generation for 43 languages.\'}', metadata={})]


 We have received an invoice document, and would like to extract the total amount from it.\n###Input: {'question': 'What is the total amount?', 'context': 'Invoice information for order ABC_123\\nProduct: Widget A, Quantity: 10, Price: $5 each\\nProduct: Widget B, Quantity: 5, Price: $3 each\\nProduct: Widget C, Quantity: 15, Price: $2 each\\nSubtotal: $75, Tax: $6.38, Total Amount Due: $81.38'}\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={})]


 As a clerk in a school, you want to extract information from some student enrollment forms. These forms contain students' details such as Name, age, and address.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Text Generation\', \'api_name\': \'pygmalion-6b\', \'api_call\': "AutoModelForCausalLM.from_pretrained(\'waifu-workshop/pygmalion-6b\')", \'api_arguments\': [\'input_ids\', \'max_length\', \'num_return_sequences\'], \'python_environment_requirements\': [\'transformers\', \'torch\'], \'example_code\': "from transformers import AutoTokenizer, AutoModelForCausalLM\\n\\ntokenizer = AutoTokenizer.from_pretrained(\'waifu-workshop/pygmalion-6b\')\\nmodel = AutoModelForCausalLM.from_pretrained(\'waifu-workshop/pygmalion-6b\')\\n\\ninput_text = [CHARACTER]\'s Persona: [A few sentences about the character you want the model to play]\\\\n<START>\\\\n[DIALOGUE HISTORY]\\\\nYou: [Your input message here]\\\\n[CHARACTER]:\\ninput_ids = tokenizer.encode(input_text, return_tensors=\'pt\')\\n\\noutput = model.generate(input_ids, max_length=100, num_return_sequences=1)\\n\\noutput_text = tokenizer.decode(output[0], skip_special_tokens=True)", \'performance\': {\'dataset\': \'56MB of dialogue data gathered from multiple sources\', \'accuracy\': \'Not specified\'}, \'description\': "Pygmalion 6B is a proof-of-concept dialogue model based on EleutherAI\'s GPT-J-6B. It is fine-tuned on 56MB of dialogue data gathered from multiple sources, which includes both real and partially machine-generated conversations. The model is intended for conversational text generation and can be used to play a character in a dialogue."}', metadata={})]


 Find a model that can be used to predict the properties of molecules based on their graph representations.\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={})]


 Estimate the depth of a pool using computational depth estimation, given an underwater photo.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Zero-Shot Image Classification\', \'framework\': \'Hugging Face\', \'functionality\': \'Zero-Shot Image Classification\', \'api_name\': \'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup\', \'api_call\': "pipeline(\'image-classification\', model=\'laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup\')", \'api_arguments\': \'image_path, class_names\', \'python_environment_requirements\': \'transformers\', \'example_code\': "results = model(image_path, class_names=\'cat, dog, bird\')", \'performance\': {\'dataset\': \'ImageNet-1k\', \'accuracy\': \'76.9\'}, \'description\': \'A series of CLIP ConvNeXt-Large (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of LAION-5B using OpenCLIP. The models utilize the timm ConvNeXt-Large model (convnext_large) as the image tower, a MLP (fc - gelu - drop - fc) head in vision tower instead of the single projection of other CLIP models, and a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768).\'}', metadata={})]


 I need technology that can analyze images and estimate their depth in a single camera.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face\', \'functionality\': \'Conversational\', \'api_name\': \'facebook/blenderbot_small-90M\', \'api_call\': "BlenderbotForConditionalGeneration.from_pretrained(\'facebook/blenderbot_small-90M\')", \'api_arguments\': [\'message\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'Input a message to start chatting with facebook/blenderbot_small-90M.\', \'performance\': {\'dataset\': \'blended_skill_talk\', \'accuracy\': \'Not provided\'}, \'description\': \'Blenderbot is a chatbot model that provides engaging talking points and listens to their partners, both asking and answering questions, and displaying knowledge, empathy, and personality appropriately, depending on the situation.\'}', metadata={})]


 The client is a real estate company working on virtual tours. We need to help them estimate depth in images of houses.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'csebuetnlp/mT5_multilingual_XLSum\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'csebuetnlp/mT5_multilingual_XLSum\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers==4.11.0.dev0\'], \'example_code\': "import re\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nWHITESPACE_HANDLER = lambda k: re.sub(\'\\\\s+\', \' \', re.sub(\'\\\\n+\', \' \', k.strip()))\\narticle_text = Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people\'s scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs spill over into misinformation about vaccines in general. The new policy covers long-approved vaccines, such as those against measles or hepatitis B. We\'re expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO, the post said, referring to the World Health Organization.\\nmodel_name = csebuetnlp/mT5_multilingual_XLSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\\ninput_ids = tokenizer(\\n [WHITESPACE_HANDLER(article_text)],\\n return_tensors=pt,\\n padding=max_length,\\n truncation=True,\\n max_length=512\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n max_length=84,\\n no_repeat_ngram_size=2,\\n num_beams=4\\n)[0]\\nsummary = tokenizer.decode(\\n output_ids,\\n skip_special_tokens=True,\\n clean_up_tokenization_spaces=False\\n)\\nprint(summary)", \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 36.5, \'ROUGE-2\': 13.934, \'ROUGE-L\': 28.988, \'ROUGE-LSUM\': 28.996, \'loss\': 2.067, \'gen_len\': 26.973}}, \'description\': \'This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. It is a multilingual abstractive summarization model that supports text-to-text generation for 43 languages.\'}', metadata={})]


 Assist me in setting up an image classifier that can recognize objects within an image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Video Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Video Classification\', \'api_name\': \'videomae-small-finetuned-ssv2\', \'api_call\': "VideoMAEForVideoClassification.from_pretrained(\'MCG-NJU/videomae-small-finetuned-ssv2\')", \'api_arguments\': {\'model_name\': \'MCG-NJU/videomae-small-finetuned-ssv2\'}, \'python_environment_requirements\': {\'transformers\': \'from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification\', \'numpy\': \'import numpy as np\', \'torch\': \'import torch\'}, \'example_code\': \'video = list(np.random.randn(16, 3, 224, 224))\\nfeature_extractor = VideoMAEFeatureExtractor.from_pretrained(MCG-NJU/videomae-small-finetuned-ssv2)\\nmodel = VideoMAEForVideoClassification.from_pretrained(MCG-NJU/videomae-small-finetuned-ssv2)\\ninputs = feature_extractor(video, return_tensors=pt)\\nwith torch.no_grad():\\n  outputs = model(**inputs)\\n  logits = outputs.logits\\npredicted_class_idx = logits.argmax(-1).item()\\nprint(Predicted class:, model.config.id2label[predicted_class_idx])\', \'performance\': {\'dataset\': \'Something-Something V2\', \'accuracy\': {\'top-1\': 66.8, \'top-5\': 90.3}}, \'description\': \'VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches. Videos are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds fixed sinus/cosinus position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of videos that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled videos for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire video.\'}', metadata={})]


 Identify an object within an image based on textual description. For example, find a dog in the image.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Transformers\', \'functionality\': \'Text Classification\', \'api_name\': \'distilbert-base-uncased-finetuned-sst-2-english\', \'api_call\': "DistilBertForSequenceClassification.from_pretrained(\'distilbert-base-uncased-finetuned-sst-2-english\')", \'api_arguments\': [\'inputs\'], \'python_environment_requirements\': [\'torch\', \'transformers\'], \'example_code\': "import torch\\nfrom transformers import DistilBertTokenizer, DistilBertForSequenceClassification\\ntokenizer = DistilBertTokenizer.from_pretrained(\'distilbert-base-uncased\')\\nmodel = DistilBertForSequenceClassification.from_pretrained(\'distilbert-base-uncased-finetuned-sst-2-english\')\\ninputs = tokenizer(\'Hello, my dog is cute\', return_tensors=\'pt\')\\nwith torch.no_grad():\\n    logits = model(**inputs).logits\\npredicted_class_id = logits.argmax().item()\\nmodel.config.id2label[predicted_class_id]", \'performance\': {\'dataset\': \'glue\', \'accuracy\': 0.911}, \'description\': \'This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. It reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). This model can be used for topic classification.\'}', metadata={})]


 Our client is an AI gaming company and we need to develop a bot for the game Valorant. The bot should detect objects like dropped spike, enemy, planted spike, and teammate within the game.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Object Detection\', \'api_name\': \'keremberke/yolov8m-valorant-detection\', \'api_call\': "YOLO(\'keremberke/yolov8m-valorant-detection\')", \'api_arguments\': {\'conf\': 0.25, \'iou\': 0.45, \'agnostic_nms\': False, \'max_det\': 1000}, \'python_environment_requirements\': \'pip install ultralyticsplus==0.0.23 ultralytics==8.0.21\', \'example_code\': "from ultralyticsplus import YOLO, render_result\\nmodel = YOLO(\'keremberke/yolov8m-valorant-detection\')\\nmodel.overrides[\'conf\'] = 0.25\\nmodel.overrides[\'iou\'] = 0.45\\nmodel.overrides[\'agnostic_nms\'] = False\\nmodel.overrides[\'max_det\'] = 1000\\nimage = \'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg\'\\nresults = model.predict(image)\\nprint(results[0].boxes)\\nrender = render_result(model=model, image=image, result=results[0])\\nrender.show()", \'performance\': {\'dataset\': \'valorant-object-detection\', \'accuracy\': 0.965}, \'description\': \'A YOLOv8 model for object detection in Valorant game, trained on a custom dataset. It detects dropped spike, enemy, planted spike, and teammate objects.\'}', metadata={})]


 A client from real estate agency needs to get a list of objects present in a series of pictures to prepare their property listings.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Computer Vision Object Detection\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'zero-shot-object-detection\', \'api_name\': \'google/owlvit-base-patch32\', \'api_call\': "OwlViTForObjectDetection.from_pretrained(\'google/owlvit-base-patch32\')", \'api_arguments\': {\'texts\': \'List of text queries\', \'images\': \'Image to be processed\'}, \'python_environment_requirements\': \'transformers\', \'example_code\': \'import requests\\nfrom PIL import Image\\nimport torch\\nfrom transformers import OwlViTProcessor, OwlViTForObjectDetection\\nprocessor = OwlViTProcessor.from_pretrained(google/owlvit-base-patch32)\\nmodel = OwlViTForObjectDetection.from_pretrained(google/owlvit-base-patch32)\\nurl = http://images.cocodataset.org/val2017/000000039769.jpg\\nimage = Image.open(requests.get(url, stream=True).raw)\\ntexts = [[a photo of a cat, a photo of a dog]]\\ninputs = processor(text=texts, images=image, return_tensors=pt)\\noutputs = model(**inputs)\\ntarget_sizes = torch.Tensor([image.size[::-1]])\\nresults = processor.post_process(outputs=outputs, target_sizes=target_sizes)\', \'performance\': {\'dataset\': \'COCO and OpenImages\', \'accuracy\': \'Not specified\'}, \'description\': \'OWL-ViT is a zero-shot text-conditioned object detection model that uses CLIP as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. The model can be used to query an image with one or multiple text queries.\'}', metadata={})]


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


 I have a picture of a room demonstrating a mixture of objects. The model needs to seperate the objects and label them accordingly.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Translation\', \'framework\': \'Transformers\', \'functionality\': \'Text-to-Text Generation\', \'api_name\': \'optimum/t5-small\', \'api_call\': "ORTModelForSeq2SeqLM.from_pretrained(\'optimum/t5-small\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'transformers\', \'optimum.onnxruntime\'], \'example_code\': \'from transformers import AutoTokenizer, pipeline\\nfrom optimum.onnxruntime import ORTModelForSeq2SeqLM\\ntokenizer = AutoTokenizer.from_pretrained(optimum/t5-small)\\nmodel = ORTModelForSeq2SeqLM.from_pretrained(optimum/t5-small)\\ntranslator = pipeline(translation_en_to_fr, model=model, tokenizer=tokenizer)\\nresults = translator(My name is Eustache and I have a pet raccoon)\\nprint(results)\', \'performance\': {\'dataset\': \'c4\', \'accuracy\': \'N/A\'}, \'description\': \'T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. It can be used for translation, text-to-text generation, and summarization.\'}', metadata={})]


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


Generate a new image based on the online database of bedroom art.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Conversational\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'ToddGoldfarb/Cadet-Tiny\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'ToddGoldfarb/Cadet-Tiny\', low_cpu_mem_usage=True)", \'api_arguments\': {\'pretrained_model\': \'t5-small\', \'model_max_length\': 512}, \'python_environment_requirements\': {\'torch\': \'\', \'transformers\': \'\', \'colorful\': \'\'}, \'example_code\': "import torch\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nimport colorful as cf\\ncf.use_true_colors()\\ncf.use_style(\'monokai\')\\nclass CadetTinyAgent:\\n def init(self):\\n  print(cf.bold | cf.purple(Waking up Cadet-Tiny...))\\n  self.device = torch.device(cuda if torch.cuda.is_available() else cpu)\\n  self.tokenizer = AutoTokenizer.from_pretrained(t5-small, model_max_length=512)\\n  self.model = AutoModelForSeq2SeqLM.from_pretrained(ToddGoldfarb/Cadet-Tiny, low_cpu_mem_usage=True).to(self.device)\\n  self.conversation_history = \\ndef observe(self, observation):\\n self.conversation_history = self.conversation_history + observation\\n # The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.\\n if len(self.conversation_history) > 400:\\n self.conversation_history = self.conversation_history[112:]\\ndef set_input(self, situation_narrative=, role_instruction=):\\n input_text = dialogue: \\n if situation_narrative != :\\n input_text = input_text + situation_narrative\\n if role_instruction != :\\n input_text = input_text +  <SEP>  + role_instruction\\n input_text = input_text +  <TURN>  + self.conversation_history\\n # Uncomment the line below to see what is fed to the model.\\n # print(input_text)\\n return input_text\\ndef generate(self, situation_narrative, role_instruction, user_response):\\n user_response = user_response +  <TURN> \\n self.observe(user_response)\\n input_text = self.set_input(situation_narrative, role_instruction)\\n inputs = self.tokenizer([input_text], return_tensors=pt).to(self.device)\\n # I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.\\n outputs = self.model.generate(inputs[input_ids], max_new_tokens=512, temperature=1, top_p=.95,\\n do_sample=True)\\n cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)\\n added_turn = cadet_response +  <TURN> \\n self.observe(added_turn)\\n return cadet_response\\ndef reset_history(self):\\n self.conversation_history = []\\ndef run(self):\\n def get_valid_input(prompt, default):\\n  while True:\\n   user_input = input(prompt)\\n   if user_input in [Y, N, y, n]:\\n    return user_input\\n   if user_input == :\\n    return default\\n  while True:\\n   continue_chat = \\n   # MODIFY THESE STRINGS TO YOUR LIKING :)\\n   situation_narrative = Imagine you are Cadet-Tiny talking to ???.\\n   role_instruction = You are Cadet-Tiny, and you are talking to ???.\\n   self.chat(situation_narrative, role_instruction)\\n   continue_chat = get_valid_input(cf.purple(Start a new conversation with new setup? [Y/N]:), Y)\\n   if continue_chat in [N, n]:\\n    break\\n   print(cf.blue(CT: See you!))\\ndef chat(self, situation_narrative, role_instruction):\\n print(cf.green(\\n  Cadet-Tiny is running! Input [RESET] to reset the conversation history and [END] to end the conversation.))\\n while True:\\n  user_input = input(You: )\\n  if user_input == [RESET]:\\n   self.reset_history()\\n   print(cf.green([Conversation history cleared. Chat with Cadet-Tiny!]))\\n   continue\\n  if user_input == [END]:\\n   break\\n  response = self.generate(situation_narrative, role_instruction, user_input)\\n  print(cf.blue(CT:  + response))\\ndef main():\\n print(cf.bold | cf.blue(LOADING MODEL))\\nCadetTiny = CadetTinyAgent()\\nCadetTiny.run()\\nif name == \'main\':\\n main()", \'performance\': {\'dataset\': \'allenai/soda\', \'accuracy\': \'\'}, \'description\': \'Cadet-Tiny is a very small conversational model trained off of the SODA dataset. Cadet-Tiny is intended for inference at the edge (on something as small as a 2GB RAM Raspberry Pi). Cadet-Tiny is trained off of the t5-small pretrained model from Google, and is, as a result, is about 2% of the size of the Cosmo-3B model.\'}', metadata={})]


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


 We need a video-based AI model for security purposes. We want the AI to check and categorize footage based on existing security guidelines.\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={})]


 A new project demands to classify videos for a social media platform. Let us create a video classification pipeline.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'csebuetnlp/mT5_multilingual_XLSum\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'csebuetnlp/mT5_multilingual_XLSum\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers==4.11.0.dev0\'], \'example_code\': "import re\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nWHITESPACE_HANDLER = lambda k: re.sub(\'\\\\s+\', \' \', re.sub(\'\\\\n+\', \' \', k.strip()))\\narticle_text = Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people\'s scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs spill over into misinformation about vaccines in general. The new policy covers long-approved vaccines, such as those against measles or hepatitis B. We\'re expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO, the post said, referring to the World Health Organization.\\nmodel_name = csebuetnlp/mT5_multilingual_XLSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\\ninput_ids = tokenizer(\\n [WHITESPACE_HANDLER(article_text)],\\n return_tensors=pt,\\n padding=max_length,\\n truncation=True,\\n max_length=512\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n max_length=84,\\n no_repeat_ngram_size=2,\\n num_beams=4\\n)[0]\\nsummary = tokenizer.decode(\\n output_ids,\\n skip_special_tokens=True,\\n clean_up_tokenization_spaces=False\\n)\\nprint(summary)", \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 36.5, \'ROUGE-2\': 13.934, \'ROUGE-L\': 28.988, \'ROUGE-LSUM\': 28.996, \'loss\': 2.067, \'gen_len\': 26.973}}, \'description\': \'This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. It is a multilingual abstractive summarization model that supports text-to-text generation for 43 languages.\'}', metadata={})]


 I am an insurance adjustor. I need a zero-shot image classifier that will tell me whether a car has been involved in a major accident or had minor damages.\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={})]


 I want to analyze a medical image to find out if it's an X-ray, an MRI scan, or a CT scan.\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={})]


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


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


 As a book store owner, I want to classify customer reviews into positive and negative sentiments.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'results-yelp\', \'api_call\': "AutoTokenizer.from_pretrained(\'bert-base-uncased\')", \'api_arguments\': {\'tokenizer\': "AutoTokenizer.from_pretrained(\'bert-base-uncased\')", \'config\': "AutoConfig.from_pretrained(\'potatobunny/results-yelp\')"}, \'python_environment_requirements\': {\'Transformers\': \'4.18.0\', \'Pytorch\': \'1.10.0+cu111\', \'Datasets\': \'2.0.0\', \'Tokenizers\': \'0.12.1\'}, \'example_code\': \'\', \'performance\': {\'dataset\': \'Yelp\', \'accuracy\': 0.9302}, \'description\': \'This model is a fine-tuned version of textattack/bert-base-uncased-yelp-polarity on a filtered and manually reviewed Yelp dataset containing restaurant reviews only. It is intended to perform text classification, specifically sentiment analysis, on text data obtained from restaurant reviews to determine if the particular review is positive or negative.\'}', metadata={})]


 I am the owner of a news website. I have several consumers' comments about our publishing news. I want to analyze the sentiments of these comments.\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 business is expanding to international markets. Analyze the sentiment of the following customer review to better understand their satisfaction with our product: \"\u00a1Esto es maravilloso! Me encanta.\"\n###Input: \"\u00a1Esto es maravilloso! Me encanta.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Zero-Shot Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Zero-Shot Classification\', \'api_name\': \'Recognai/bert-base-spanish-wwm-cased-xnli\', \'api_call\': "AutoModelForSequenceClassification.from_pretrained(\'Recognai/bert-base-spanish-wwm-cased-xnli\')", \'api_arguments\': [\'sequence\', \'candidate_labels\', \'hypothesis_template\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'from transformers import pipeline\\nclassifier = pipeline(zero-shot-classification, model=Recognai/bert-base-spanish-wwm-cased-xnli)\\nclassifier(\\nEl autor se perfila, a los 50 años de su muerte, como uno de los grandes de su siglo,\\ncandidate_labels=[cultura, sociedad, economia, salud, deportes],\\nhypothesis_template=Este ejemplo es {}. \\n)\', \'performance\': {\'dataset\': \'XNLI-es\', \'accuracy\': \'79.9%\'}, \'description\': \'This model is a fine-tuned version of the spanish BERT model with the Spanish portion of the XNLI dataset. You can have a look at the training script for details of the training.\'}', metadata={})]


 We are a forum moderator team looking for a solution to classify comments into toxic or non-toxic categories.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'martin-ha/toxic-comment-model\', \'api_call\': "pipeline(model=\'martin-ha/toxic-comment-model\')", \'api_arguments\': {\'model_path\': \'martin-ha/toxic-comment-model\'}, \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline\\nmodel_path = martin-ha/toxic-comment-model\\ntokenizer = AutoTokenizer.from_pretrained(model_path)\\nmodel = AutoModelForSequenceClassification.from_pretrained(model_path)\\npipeline = TextClassificationPipeline(model=model, tokenizer=tokenizer)\\nprint(pipeline(\'This is a test text.\'))", \'performance\': {\'dataset\': \'held-out test set\', \'accuracy\': 0.94, \'f1-score\': 0.59}, \'description\': \'This model is a fine-tuned version of the DistilBERT model to classify toxic comments.\'}', metadata={})]


 My company is launching a social media campaign. We need an AI-based system that would automatically analyze the sentiment of any user-generated reviews or tweets concerning our product.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Summarization\', \'framework\': \'Transformers\', \'functionality\': \'text2text-generation\', \'api_name\': \'csebuetnlp/mT5_multilingual_XLSum\', \'api_call\': "AutoModelForSeq2SeqLM.from_pretrained(\'csebuetnlp/mT5_multilingual_XLSum\')", \'api_arguments\': [\'model_name\'], \'python_environment_requirements\': [\'transformers==4.11.0.dev0\'], \'example_code\': "import re\\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\\nWHITESPACE_HANDLER = lambda k: re.sub(\'\\\\s+\', \' \', re.sub(\'\\\\n+\', \' \', k.strip()))\\narticle_text = Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people\'s scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs spill over into misinformation about vaccines in general. The new policy covers long-approved vaccines, such as those against measles or hepatitis B. We\'re expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO, the post said, referring to the World Health Organization.\\nmodel_name = csebuetnlp/mT5_multilingual_XLSum\\ntokenizer = AutoTokenizer.from_pretrained(model_name)\\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_name)\\ninput_ids = tokenizer(\\n [WHITESPACE_HANDLER(article_text)],\\n return_tensors=pt,\\n padding=max_length,\\n truncation=True,\\n max_length=512\\n)[input_ids]\\noutput_ids = model.generate(\\n input_ids=input_ids,\\n max_length=84,\\n no_repeat_ngram_size=2,\\n num_beams=4\\n)[0]\\nsummary = tokenizer.decode(\\n output_ids,\\n skip_special_tokens=True,\\n clean_up_tokenization_spaces=False\\n)\\nprint(summary)", \'performance\': {\'dataset\': \'xsum\', \'accuracy\': {\'ROUGE-1\': 36.5, \'ROUGE-2\': 13.934, \'ROUGE-L\': 28.988, \'ROUGE-LSUM\': 28.996, \'loss\': 2.067, \'gen_len\': 26.973}}, \'description\': \'This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. It is a multilingual abstractive summarization model that supports text-to-text generation for 43 languages.\'}', metadata={})]


 I have jobs descriptions in French for sales manager, please highlight names of organizations or cities within the text.\n###Input: \"La soci\u00e9t\u00e9 de Paris est sp\u00e9cialis\u00e9e dans la vente de v\u00e9hicules \u00e9lectriques. Responsable des ventes, vous travaillerez au sein d'une \u00e9quipe dynamique dans l'agence de Lyon. Vous \u00eates charg\u00e9(e) de d\u00e9velopper le portefeuille client et d'assurer la satisfaction des clients existants. Dans ce contexte, vous devrez travailler en lien \u00e9troit avec le directeur commercial et les autres \u00e9quipes de l'entreprise. Une exp\u00e9rience pr\u00e9alable chez Renault est un atout.\"\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Token Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Named Entity Recognition\', \'api_name\': \'Jean-Baptiste/camembert-ner\', \'api_call\': "AutoModelForTokenClassification.from_pretrained(\'Jean-Baptiste/camembert-ner\')", \'api_arguments\': {\'model\': \'model\', \'tokenizer\': \'tokenizer\', \'aggregation_strategy\': \'simple\'}, \'python_environment_requirements\': {\'transformers\': \'AutoTokenizer, AutoModelForTokenClassification, pipeline\'}, \'example_code\': "from transformers import AutoTokenizer, AutoModelForTokenClassification\\ntokenizer = AutoTokenizer.from_pretrained(Jean-Baptiste/camembert-ner)\\nmodel = AutoModelForTokenClassification.from_pretrained(Jean-Baptiste/camembert-ner)\\n\\nfrom transformers import pipeline\\nnlp = pipeline(\'ner\', model=model, tokenizer=tokenizer, aggregation_strategy=simple)\\nnlp(Apple est cre le 1er avril 1976 dans le garage de la maison d\'enfance de Steve Jobs  Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constitue sous forme de socit le 3 janvier 1977  l\'origine sous le nom d\'Apple Computer, mais pour ses 30 ans et pour reflter la diversification de ses produits, le mot « computer  est retir le 9 janvier 2015.)", \'performance\': {\'dataset\': \'wikiner-fr\', \'accuracy\': {\'overall_f1\': 0.8914, \'PER_f1\': 0.9483, \'ORG_f1\': 0.8181, \'LOC_f1\': 0.8955, \'MISC_f1\': 0.8146}}, \'description\': \'camembert-ner is a Named Entity Recognition (NER) model fine-tuned from camemBERT on the wikiner-fr dataset. It can recognize entities such as persons, organizations, locations, and miscellaneous entities.\'}', metadata={})]


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


 I am building a social media app that requires people to write fascinating stories rather than boring sentences. Detect named entities in a sentence by using an NER model.\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={})]


 We have a large dataset of customer orders in the form of a table. Help us answer questions about this data.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'PyTorch Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'table-question-answering-tapas\', \'api_call\': "pipeline(\'table-question-answering\', model=\'Meena/table-question-answering-tapas\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'This model can be loaded on the Inference API on-demand.\', \'performance\': {\'dataset\': [{\'name\': \'SQA (Sequential Question Answering by Microsoft)\', \'accuracy\': None}, {\'name\': \'WTQ (Wiki Table Questions by Stanford University)\', \'accuracy\': None}, {\'name\': \'WikiSQL (by Salesforce)\', \'accuracy\': None}]}, \'description\': \'TAPAS, the model learns an inner representation of the English language used in tables and associated texts, which can then be used to extract features useful for downstream tasks such as answering questions about a table, or determining whether a sentence is entailed or refuted by the contents of a table. It is a BERT-based model specifically designed (and pre-trained) for answering questions about tabular data. TAPAS uses relative position embeddings and has 7 token types that encode tabular structure. It is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising millions of tables from English Wikipedia and corresponding texts.\'}', metadata={})]


 Gather information about annual income and age demographics of employees to predict retirement patterns. Make sure to identify top employees for potential promotions.\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-japanese\', \'api_call\': "SpeechRecognitionModel(\'jonatasgrosman/wav2vec2-large-xlsr-53-japanese\')", \'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-japanese)\\naudio_paths = [/path/to/file.mp3, /path/to/another_file.wav]\\ntranscriptions = model.transcribe(audio_paths)\', \'performance\': {\'dataset\': \'common_voice\', \'accuracy\': {\'WER\': 81.8, \'CER\': 20.16}}, \'description\': \'Fine-tuned XLSR-53 large model for speech recognition in Japanese. Trained on Common Voice 6.1, CSS10, and JSUT datasets. Make sure your speech input is sampled at 16kHz.\'}', metadata={})]


 To track our sales data, we need to find total sales of a specific product based on a table containing sales information per week.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text Classification\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'financial-sentiment-analysis\', \'api_name\': \'yiyanghkust/finbert-tone\', \'api_call\': "BertForSequenceClassification.from_pretrained(\'yiyanghkust/finbert-tone\',num_labels=3)", \'api_arguments\': [\'sentences\'], \'python_environment_requirements\': [\'transformers\'], \'example_code\': "from transformers import BertTokenizer, BertForSequenceClassification\\nfrom transformers import pipeline\\nfinbert = BertForSequenceClassification.from_pretrained(\'yiyanghkust/finbert-tone\',num_labels=3)\\ntokenizer = BertTokenizer.from_pretrained(\'yiyanghkust/finbert-tone\')\\nnlp = pipeline(sentiment-analysis, model=finbert, tokenizer=tokenizer)\\nsentences = [there is a shortage of capital, and we need extra financing,\\n growth is strong and we have plenty of liquidity,\\n there are doubts about our finances,\\n profits are flat]\\nresults = nlp(sentences)\\nprint(results)", \'performance\': {\'dataset\': \'10,000 manually annotated sentences from analyst reports\', \'accuracy\': \'superior performance on financial tone analysis task\'}, \'description\': \'FinBERT is a BERT model pre-trained on financial communication text. It is trained on the following three financial communication corpus: Corporate Reports 10-K & 10-Q, Earnings Call Transcripts, and Analyst Reports. This released finbert-tone model is the FinBERT model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task.\'}', metadata={})]


 I have a table containing information about various animals and their important characteristics. I need the system to answer a query to provide information about the tallest animal in the table.\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={})]


 You are building an app that allows users to find quick answers to textbook questions. Users will send a message with the question, and the answer should be detected directly from the textbook content.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Text2Text Generation\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Transformers\', \'api_name\': \'valhalla/t5-base-e2e-qg\', \'api_call\': "pipeline(\'e2e-qg\', model=\'valhalla/t5-base-e2e-qg\')", \'api_arguments\': [\'text\'], \'python_environment_requirements\': [\'Hugging Face Transformers\'], \'example_code\': "from pipelines import pipeline\\n\\ntext = Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python\'s design philosophy emphasizes code readability with its notable use of significant whitespace.\\n\\nnlp = pipeline(e2e-qg, model=valhalla/t5-base-e2e-qg)\\n\\nnlp(text)", \'performance\': {\'dataset\': \'squad\', \'accuracy\': \'N/A\'}, \'description\': \'This is a T5-base model trained for end-to-end question generation task. Simply input the text and the model will generate multiple questions. You can play with the model using the inference API, just put the text and see the results!\'}', metadata={})]


 We want to make sure clarify some questions about the legal implications of a new partnership contract for a real estate development project.\n###Input: We hereby grant the Licensee the exclusive right to develop, construct, operate and promote the Project, as well as to manage the daily operations of the Licensed Facilities during the Term. In consideration for the grant of the License, the Licensee shall pay to the Licensor the full amount of Ten Million (10,000,000) Dollars within thirty (30) days after the execution hereof.\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={})]


 Help me setup a tinyroberta model from deepset for Question and Answer. Provide a sample input and output.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Natural Language Processing Table Question Answering\', \'framework\': \'Transformers\', \'functionality\': \'Table Question Answering\', \'api_name\': \'google/tapas-medium-finetuned-sqa\', \'api_call\': "pipeline(\'table-question-answering\', model=\'google/tapas-medium-finetuned-sqa\')", \'api_arguments\': \'table, query\', \'python_environment_requirements\': \'transformers\', \'example_code\': "from transformers import pipeline\\n\\n# Initialize the pipeline\\ntable_qa_pipeline = pipeline(\'table-question-answering\', model=\'google/tapas-medium-finetuned-sqa\')\\n\\n# Provide the table and query\\nresult = table_qa_pipeline(table=table, query=\'What is the total revenue?\')", \'performance\': {\'dataset\': \'msr_sqa\', \'accuracy\': 0.6561}, \'description\': \'TAPAS medium model fine-tuned on Sequential Question Answering (SQA). This model is pretrained on a large corpus of English data from Wikipedia and uses relative position embeddings. It can be used for answering questions related to a table in a conversational set-up.\'}', metadata={})]


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


 We have a French news agency and we want to categorize the news articles based on sports, politics, and science.\n
 Use this API documentation for reference: [Document(page_content='{\'domain\': \'Multimodal Feature Extraction\', \'framework\': \'Hugging Face Transformers\', \'functionality\': \'Feature Extraction\', \'api_name\': \'DeepPavlov/rubert-base-cased\', \'api_call\': "AutoModel.from_pretrained(\'DeepPavlov/rubert-base-cased\')", \'api_arguments\': [], \'python_environment_requirements\': [\'transformers\'], \'example_code\': \'\', \'performance\': {\'dataset\': \'Russian part of Wikipedia and news data\', \'accuracy\': \'\'}, \'description\': \'RuBERT (Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT[1].\'}', metadata={})]