Code for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation"
$ git clone https://github.com/hiyouga/Dual-Contrastive-Learning.gitBERT classification model for processing texts longer than 512 tokens. Text is first divided into smaller chunks and after feeding them to BERT, intermediate results are pooled. The implementation allows fine-tuning.
Collection of papers and resources for data augmentation for NLP.
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.
This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification.
Data from GitHub · snapshot Sep 24, 2026