Collection of papers and resources for data augmentation for NLP.
$ git clone https://github.com/styfeng/DataAug4NLP.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.
💫 Industrial-strength Natural Language Processing (NLP) in Python
Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
Code for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation"
This is the corresponding code for the book Transformers in Action
fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.
Data from GitHub · snapshot Sep 24, 2026