lancopku/text-autoaugment
[EMNLP 2021] Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
This project helps data scientists and machine learning engineers enhance text classification models, especially when working with limited data. It takes your existing text dataset (like customer reviews or survey responses) and automatically generates diverse, high-quality augmented text samples. The output is an expanded training dataset that can be used to improve the performance and generalization of deep learning models like BERT for text classification tasks.
130 stars. No commits in the last 6 months.
Use this if you are building text classification models and struggle with low data availability or class imbalance, and want to improve model accuracy by automatically generating more training examples.
Not ideal if your primary goal is not text classification, or if you already have a very large and well-balanced text dataset for training.
Stars
130
Forks
15
Language
Python
License
MIT
Category
Last pushed
Mar 11, 2023
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