zzz47zzz/CFNER

[EMNLP2022] Released code for paper "Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition"

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This project helps data scientists and AI engineers improve named entity recognition (NER) models over time. It takes raw text data as input and outputs a continually learning NER model that can identify new entity types without forgetting previously learned ones. This is for professionals building and maintaining NLP systems in fields like healthcare, finance, or general text analysis.

No commits in the last 6 months.

Use this if you need to update your named entity recognition models with new entity types while ensuring they retain their accuracy on existing entity types, which is common in evolving data environments.

Not ideal if you are looking for a pre-trained, ready-to-use NER model and do not need to perform incremental learning on custom or continuously updated datasets.

named-entity-recognition natural-language-processing continual-learning text-analytics information-extraction
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

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Stars

23

Forks

5

Language

Python

License

MIT

Last pushed

Feb 09, 2023

Commits (30d)

0

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