🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.

Introducing a novel tool designed to deliver concise summaries or answer specific questions from audio and video content. Either use our website to feed the content or use our extension for youtube quick support

Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.

中文nlp解决方案(大模型、数据、模型、训练、推理)

Easy and lightning fast training of 🤗 Transformers on Habana Gaudi processor (HPU)

Train transformer-based models.

Simple and Lightweight Text Classifiers with LLM Embeddings

🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

This repository contains demos I made with the Transformers library by HuggingFace.

💥 Fast State-of-the-Art Tokenizers optimized for Research and Production

End-to-End BERT-Based Coreference System

Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

Neural Network Compression Framework for enhanced OpenVINO™ inference

State of the Art Natural Language Processing

The library integrates voice-based offensive content detection in iOS apps, utilizing Apple's Speech framework and a machine learning model created with Create ML. It accurately identifies offensive language and hate speech, supporting both SwiftUI and UIKit for content moderation.

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

Automated discovery and classification of websites content through unsupervised learning approach

Text-based modeling of materials.

vits2 backbone with multilingual-bert