Michael-Beukman/NERTransfer

Investigating transfer learning in low-resourced languages, specifically in a named entity recognition (NER) task (IJCNLP-AACL 2023). http://arxiv.org/abs/2309.05311

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This project helps natural language processing researchers understand how named entity recognition (NER) models perform across different low-resourced African languages. It takes pre-trained language models and NER datasets in various African languages as input, then fine-tunes and evaluates them. The output includes performance metrics, detailed analyses, and visualizations about how well models transfer knowledge between languages. Researchers focusing on multilingual NLP, especially for African languages, would find this valuable.

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Use this if you are an NLP researcher investigating the effectiveness of transfer learning and zero-shot learning for named entity recognition in low-resourced languages.

Not ideal if you are looking for a ready-to-use NER tool for business applications or a simple API to integrate into an existing product.

natural-language-processing african-languages named-entity-recognition transfer-learning low-resource-nlp
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 13 / 25

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7

Forks

2

Language

Python

License

Apache-2.0

Last pushed

Oct 09, 2023

Commits (30d)

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