ZihanWangKi/CrossWeigh
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
This project helps researchers and data scientists improve the accuracy of Named Entity Recognition (NER) models when their training data contains human annotation errors. It takes existing NER training datasets that may have mistakes and processes them to identify and down-weight those errors. The output is a more robust NER model that performs better on real-world text analysis tasks, ultimately delivering more accurate entity extraction.
176 stars. No commits in the last 6 months.
Use this if you are training Named Entity Recognition (NER) models and suspect that human errors in your annotated training data are hurting your model's performance.
Not ideal if your primary concern is with evaluating model performance on an already clean and verified test dataset.
Stars
176
Forks
21
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 25, 2024
Commits (30d)
0
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