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...
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{"id":"facebook/detr-resnet-50","reason":"The model has the highest number of likes, and the description sounds promising with end-to-end detection using transformers. Also, it has a local inference endpoint for faster access."} 2.3 模型执行 上面模型已经选择,输入也有,接下来就是执行模型,...
Hugging Face is most notable for its Transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets.This connector is available in the following products and regions:Développer le tableau ServiceClassRegions Logic ...
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
Document question-answering Visual question-answeringThese tasks entail not only state-of-the-art Transformer-based architectures such as ViT, Swin, DETR but also pure convolutional architectures like ConvNeXt, ResNet, RegNet, and more! Architectures like ResNets are still very much relevant f...
Using Hugging Face Hub Embeddings with Langchain document loaders to do some query answering - ToxyBorg/Hugging-Face-Hub-Langchain-Document-Embeddings
In this post, we walk you through an example of how to build and deploy a custom Hugging Face text summarizer on SageMaker. We use Pegasus [1] for this purpose, the first Transformer-based model specifically pre-trained on an objective tailored for abstractive text summarization. B...
Last year, AWS announced a partnership with Hugging Face to help bring natural language processing (NLP) models to production faster. Hugging Face is an open-source AI community, focused on NLP. Their Python-based library (Transformers) provides tools to easily use popular stat...
This is the 3rd article in a series on using large language models (LLMs) in practice. Here I will give a beginner-friendly guide to the Hugging Face Transformers library.