osiria/bert-base-cased-ner-en

Task: Named Entity Recognition
Model: BERT
Lang: EN

Model description

This is a BERT [1] cased model for the English language, fine-tuned for Named Entity Recognition (Person, Location, Organization and Miscellanea classes) on the WikiNER dataset [2], using Google's bert-base-cased as a pre-trained model.

Training and Performances

The model is trained to perform entity recognition over 4 classes: PER (persons), LOC (locations), ORG (organizations), MISC (miscellanea, mainly events, products and services). It has been fine-tuned for Named Entity Recognition, using the WikiNER English dataset. The model has been trained for 1 epoch with a constant learning rate of 1e-5.

References

[1] https://arxiv.org/abs/1810.04805

[2] https://www.sciencedirect.com/science/article/pii/S0004370212000276

Limitations

This model is mainly trained on Wikipedia, so it's particularly suitable for natively digital text from the world wide web, written in a correct and fluent form (like wikis, web pages, news, etc.). However, it may show limitations when it comes to chaotic text, containing errors and slang expressions (like social media posts) or when it comes to domain-specific text (like medical, financial or legal content).

License