camembert ner: model fine tuned from camemBERT for NER task. Introduction [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 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 How to use camembert ner with HuggingFace Load camembert ner and its sub word tokenizer : Model performances (metric: seqeval) 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
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