yumeng5/SuperGen
[NeurIPS 2022] Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
This project helps Natural Language Understanding (NLU) researchers and practitioners quickly develop and evaluate NLU models for new tasks without needing extensive labeled datasets. It takes a description of your NLU task labels and generates synthetic training data. You then use this data to fine-tune a language model, producing a model ready for zero-shot language understanding.
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Use this if you need to build NLU models for tasks where collecting real-world labeled training data is time-consuming or expensive.
Not ideal if you already have a large, high-quality labeled dataset for your NLU task, as traditional supervised learning might be more straightforward.
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69
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Language
Python
License
Apache-2.0
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Last pushed
Sep 18, 2022
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