A Named Entity Recognition model fine-tuned from CamemBERT on the Wikiner-FR dataset. Our model achieves high performance on various entities, including Persons, Organizations, Locations, and Miscellaneous entities.
A Named Entity Recognition model fine-tuned from CamemBERT on the Wikiner-FR dataset. Our model achieves high performance on various entities, including Persons, Organizations, Locations, and Miscellaneous entities.
[camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset. Model was trained on wikiner-fr dataset (~170 634 sentences). Model was validated on emails/chat data and overperformed other models on this type of data specifically. In particular the model seems to work better on entity that don't start with an upper case.
Training data was classified as follow:
Abbreviation | Description |
---|---|
O | Outside of a named entity |
MISC | Miscellaneous entity |
PER | Person’s name |
ORG | Organization |
LOC | Location |
Overall
precision | recall | f1 |
---|---|---|
0.8859 | 0.8971 | 0.8914 |
By entity
entity | precision | recall | f1 |
---|---|---|---|
PER | 0.9372 | 0.9598 | 0.9483 |
ORG | 0.8099 | 0.8265 | 0.8181 |
LOC | 0.8905 | 0.9005 | 0.8955 |
MISC | 0.8175 | 0.8117 | 0.8146 |
For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails: https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa