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

Natural Language Processing Tutorial for Deep Learning Researchers

🏄 Embed/reason/rank images and sentences with CLIP models

🔍 Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, Falcon and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

👑 Easy-to-use and powerful NLP 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 and 🖼 Diffusion AIGC system etc.

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP

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

Official implementations for various pre-training models of ERNIE-family, covering topics of Language Understanding & Generation, Multimodal Understanding & Generation, and beyond.

Google AI 2018 BERT pytorch implementation

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

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

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

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

pycorrector is a toolkit for text error correction. 文本纠错，Kenlm，ConvSeq2Seq，BERT，MacBERT，ELECTRA，ERNIE，Transformer，T5等模型实现，开箱即用。

Transformer related optimization, including BERT, GPT

Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS, 海量中文预训练ALBERT模型

中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard

Must-read papers on prompt-based tuning for pre-trained language models.