yumeng5/FewGen
[ICML 2023] Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
This project helps developers and researchers working with Natural Language Understanding (NLU) tasks to improve their models when only a small amount of labeled training data is available. It takes a few existing examples for a classification task and generates a larger, synthetic dataset. The output is an enhanced training set that can be used to fine-tune NLU classifiers for better performance.
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Use this if you are a machine learning engineer or researcher dealing with NLU classification problems and have limited labeled data for training your models.
Not ideal if you have abundant labeled data, are not working on NLU tasks, or do not have access to substantial GPU resources for model tuning.
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44
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
May 10, 2023
Commits (30d)
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