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This project investigates few-shot learning for relation extraction using the FewRel dataset. We will compare Prototypical Networks, MAML, and k-NNs in different few-shot settings to see which performs best with minimal data. The goal is to improve relation extraction in NLP by effectively handling data scarcity.
The online version is temporarily unavailable because we cannot afford the key. You can clone and run it locally. Note: we set defaul openai key. If keys exceed plan and are invalid, please tell us. The response speed depends on openai. ( sometimes, the official is too crowded and slow)
This is a meta-model distilled from LLMs for information extraction. This is an intermediate checkpoint that can be well-transferred to all kinds of downstream information extraction tasks.