xijiz/cfgen
Implementation of the EMNLP 2020 paper "Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition".
This tool helps improve the accuracy of models that identify specific entities (like names, places, or dates) in text, even when you have limited training data. It takes your existing text data with some labeled entities and generates additional, varied examples. The result is a more robust model for tasks like extracting key information from documents, and it's ideal for data scientists or NLP engineers working with specialized text.
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Use this if you need to train a highly accurate Named Entity Recognition (NER) model but have a small or biased dataset.
Not ideal if you don't work with text data, or if you already have an abundance of diverse, high-quality labeled data for your NER task.
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Language
Python
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Last pushed
Dec 28, 2020
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