camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).
Introduction
[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates. Model was trained on enriched version of wikiner-fr dataset (~170 634 sentences).
On my test data (mix of chat and email), this model got an f1 score of ~83% (in comparison dateparser was ~70%). Dateparser library can still be be used on the output of this model in order to convert text to python datetime object (https://dateparser.readthedocs.io/en/latest/).
How to use camembert-ner-with-dates with HuggingFace
Load camembert-ner-with-dates and its sub-word tokenizer :
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner-with-dates")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner-with-dates")
##### Process text sample (from wikipedia)
from transformers import pipeline
nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.")
[{'entity_group': 'ORG',
'score': 0.9776379466056824,
'word': 'Apple',
'start': 0,
'end': 5},
{'entity_group': 'DATE',
'score': 0.9793774570737567,
'word': 'le 1er avril 1976 dans le',
'start': 15,
'end': 41},
{'entity_group': 'PER',
'score': 0.9958226680755615,
'word': 'Steve Jobs',
'start': 74,
'end': 85},
{'entity_group': 'LOC',
'score': 0.995087186495463,
'word': 'Los Altos',
'start': 87,
'end': 97},
{'entity_group': 'LOC',
'score': 0.9953305125236511,
'word': 'Californie',
'start': 100,
'end': 111},
{'entity_group': 'PER',
'score': 0.9961076378822327,
'word': 'Steve Jobs',
'start': 115,
'end': 126},
{'entity_group': 'PER',
'score': 0.9960325956344604,
'word': 'Steve Wozniak',
'start': 127,
'end': 141},
{'entity_group': 'PER',
'score': 0.9957776467005411,
'word': 'Ronald Wayne',
'start': 144,
'end': 157},
{'entity_group': 'DATE',
'score': 0.994030773639679,
'word': 'le 3 janvier 1977 à',
'start': 198,
'end': 218},
{'entity_group': 'ORG',
'score': 0.9720810294151306,
'word': "d'Apple Computer",
'start': 240,
'end': 257},
{'entity_group': 'DATE',
'score': 0.9924157659212748,
'word': '30 ans et',
'start': 272,
'end': 282},
{'entity_group': 'DATE',
'score': 0.9934852868318558,
'word': 'le 9 janvier 2015.',
'start': 363,
'end': 382}]
Model performances (metric: seqeval)
Global
'precision': 0.928
'recall': 0.928
'f1': 0.928
By entity
Label LOC: (precision:0.929, recall:0.932, f1:0.931, support:9510)
Label PER: (precision:0.952, recall:0.965, f1:0.959, support:9399)
Label MISC: (precision:0.878, recall:0.844, f1:0.860, support:5364)
Label ORG: (precision:0.848, recall:0.883, f1:0.865, support:2299)
Label DATE: Not relevant because of method used to add date tag on wikiner dataset (estimated f1 ~90%)