readme: ---
  language: fr
  datasets:
  - Jean-Baptiste/wikiner_fr
  widget:
  - text: "Je m'appelle jean-baptiste et je vis à montréal"
  - text: "george washington est allé à washington"
  license: mit
  ---
  
  # 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 :
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner")
  model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner")
  
  
  ##### 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': 0.9472818374633789,
    'word': 'Apple',
    'start': 0,
    'end': 5},
   {'entity_group': 'PER',
    'score': 0.9838564991950989,
    'word': 'Steve Jobs',
    'start': 74,
    'end': 85},
   {'entity_group': 'LOC',
    'score': 0.9831605950991312,
    'word': 'Los Altos',
    'start': 87,
    'end': 97},
   {'entity_group': 'LOC',
    'score': 0.9834540486335754,
    'word': 'Californie',
    'start': 100,
    'end': 111},
   {'entity_group': 'PER',
    'score': 0.9841555754343668,
    'word': 'Steve Jobs',
    'start': 115,
    'end': 126},
   {'entity_group': 'PER',
    'score': 0.9843501806259155,
    'word': 'Steve Wozniak',
    'start': 127,
    'end': 141},
   {'entity_group': 'PER',
    'score': 0.9841533899307251,
    'word': 'Ronald Wayne',
    'start': 144,
    'end': 157},
   {'entity_group': 'ORG',
    'score': 0.9468960364659628,
    'word': 'Apple Computer',
    'start': 243,
    'end': 257}]
  
  ```
  
  
  ## 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
  