RishiHazra/Actively-reducing-redundancies-in-Active-Learning-for-Sequence-Tagging
Active Learning for sequence tagging
This project helps machine learning engineers and NLP researchers to more efficiently train models for sequence tagging tasks, such as named entity recognition or part-of-speech tagging. It takes your existing Active Learning setup, where you are selecting data for manual annotation, and outputs a refined set of data points that are more diverse and less redundant, thus accelerating model training. This is for professionals building and optimizing NLP models who need to reduce the amount of expensive human-labeled data.
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Use this if you are developing sequence tagging models and want to achieve strong performance with less manually labeled data than traditional Active Learning methods.
Not ideal if you are not working with sequence tagging problems or if you already have abundant labeled data and are not concerned with reducing annotation costs.
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Python
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May 30, 2021
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