最终,Hugging Face团队迎来了一个转折点,将公司从一家不太赚钱的AI聊天机器人初创公司转变为未来估值十亿美元的独角兽。 在接下来的几年里,Hugging Face 团队继续专注于产品建设和社区发展,并取得了令人瞩目的成就: Hugging Face 已成为扩展最快的社区和使用最广泛的机器学习平台!平台上有 10 万个预训练模型和 1 万个数据
Hugging-Face – 大语言模型界的 Github Hugging Face 专门开发用于构建机器学习应用的工具。该公司的代表产品是其为自然语言处理应用构建的 transformers 库,以及允许用户共享机器学习模型和数据集的平台 大模型平台 hugging face 国内对标 – 百度千帆 百度智能云千帆大模型平台(以下简称千帆或千帆大模型平台)是面向企业...
Run a question answering taskOperation ID: AnswerPost Retrieve an answer to your question. Parameters 展开表 NameKeyRequiredTypeDescription Question question True string The question. Context context True string The context. Returns 展开表 NamePathTypeDescription Score score float The score. Start ...
Visual question answering fromtransformersimportpipeline pipeline = pipeline(task="visual-question-answering", model="Salesforce/blip-vqa-base") pipeline( image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg", question="What is in t...
Gmail + Hugging Face + Google Sheets Triggers Actions Triggers & Actions Document Question Answering Use a compatible model from the Hugging Face Hub to get an answer from an image of a document. Action This is an event a Zap performs. ...
Hugging Face Components Benefits of the Hugging Face Platform Core Tasks Supported by Hugging Face Hugging Face Task Implementation Workflow Open Source AI with Krasamo Open source AIplays a crucial role in advancing the field of artificial intelligence by promoting accessibility, collaboration, and tran...
multimodal document-question-answering multimodal feature-extraction multimodal image-segmentation multimodal image-to-text multimodal mask-generation multimodal object-detection multimodal visual-question-answering multimodal zero-shot-audio-classification
visualQuestionAnswering( image: data, question: "How many animals are in this image?", model: "dandelin/vilt-b32-finetuned-vqa" )Document Question AnsweringDocument question answering models take a (document, question) pair as input and return an answer in natural language....
input: document, query output: two integers (s, e) example: extraction-based question answering (QA) BERT简介 BERT是一种预训练语言模型(pre-trained language model, PLM),其全称是Bidirectional Encoder Representations from Transformers。 语言模型:对于任意的词序列,它能够计算出这个序列是一句话的概率。比...
Using Hugging Face Hub Embeddings with Langchain document loaders to do some query answering - ToxyBorg/Hugging-Face-Hub-Langchain-Document-Embeddings