unveiled-the-red-hat/SEE-Few
Code for "SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition", accepted at COLING 2022.
This project helps natural language processing (NLP) researchers and data scientists accurately identify specific entities like names, locations, or organizations in text, even when very few examples are available for training. It takes raw text data as input and outputs annotations identifying these named entities. This is useful for anyone working on information extraction or text analysis with limited labeled data.
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Use this if you need to perform named entity recognition on text data but have only a small number of labeled examples to train your model.
Not ideal if you have ample labeled data for named entity recognition, as more traditional methods might offer simpler or more established solutions.
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Language
Python
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Last pushed
Nov 25, 2022
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