DistilCamemBERT NER =================== We present DistilCamemBERT NER, which is DistilCamemBERT fine tuned for the NER (Named Entity Recognition) task for the French language. The work is inspired by Jean Baptiste/camembert ner based on the CamemBERT model. The problem of the modelizations based on CamemBERT is at the scaling moment, for the production phase, for example. Indeed, inference cost can be a technological issue. To counteract this effect, we propose this modelization which divides the inference time by two with the same consumption power thanks to DistilCamemBERT. Dataset The dataset used is wikiner fr, which represents ~170k sentences labeled in 5 categories : PER: personality ; LOC: location ; ORG: organization ; MISC: miscellaneous entities (movies title, books, etc.) ; O: background (Outside entity). Evaluation results class precision (%) recall (%) f1 (%) support ( sub word) : : : : : : : : : : global 98.17 98.19 98.18 378,776 PER 96.78 96.87 96.82 23,754 LOC 94.05 93.59 93.82 27,196 ORG 86.05 85.92 85.98 6,526 MISC 88.78 84.69 86.69 11,891 O 99.26 99.47 99.37 309,409 Benchmark This model performance is compared to 2 reference models (see below) with the metric f1…
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