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camembert-ner

Jean-Baptiste

Abbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location 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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Abbreviation|Description -|- O |Outside of a named entity MISC |Miscellaneous entity PER |Person’s name ORG |Organization LOC |Location In order to simplify, the prefix B- or I- from original conll2003 was removed. I used the train and test dataset from original conll2003 for training and the "validation" dataset for validation. This resulted in a dataset of size: Model performances computed on conll2003 validation dataset (computed on the tokens predictions)

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