readme: ---
  language: fr
  datasets:
  - Jean-Baptiste/wikiner_fr
  widget:
  - text: "Je m'appelle jean-baptiste et j'habite à montréal depuis fevr 2012"
  ---
  
  # 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 :
  
  ```python
  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%)
  
  
   ```
  
  