joisino/reeval-wmd
Code for "Re-evaluating Word Mover’s Distance" (ICML 2022)
This project helps researchers and practitioners in natural language processing to accurately evaluate text classification models. It takes document datasets as input and produces re-evaluated classification error rates, revealing the true performance of text similarity metrics like Word Mover's Distance. This is for data scientists or NLP researchers who need reliable benchmarks for text classification.
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Use this if you are evaluating document classification algorithms and need to understand the true performance of text similarity metrics like Word Mover's Distance under different normalization conditions.
Not ideal if you are looking for a ready-to-use text classification tool for general business applications rather than a research-focused evaluation framework.
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Language
Python
License
MIT
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Last pushed
Jun 15, 2022
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