jiacheng-ye/ZeroGen
[EMNLP 2022] Code for our paper “ZeroGen: Efficient Zero-shot Learning via Dataset Generation”.
This project helps natural language processing (NLP) researchers and practitioners quickly train performant text classification and question-answering models, even when human-annotated data is scarce. It takes a pre-trained language model and a target NLP task, then generates a synthetic dataset with diverse and correct examples. The output is a dataset ready to train smaller, efficient task-specific models without needing extensive manual labeling.
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Use this if you need to build a text classification or question-answering system but lack the extensive, manually labeled datasets typically required for training high-quality models.
Not ideal if your NLP task falls outside of text classification or question answering, or if you already have abundant, high-quality human-labeled data for your specific use case.
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Python
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Last pushed
Feb 18, 2022
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