metadata

language: fr
license: mit
datasets:
  - Jean-Baptiste/wikiner_fr
widget:
  - text: >-
      Boulanger, habitant à Boulanger et travaillant dans le magasin Boulanger
      situé dans la ville de Boulanger.

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 2 with the same consumption power thanks to DistilCamemBERT.

Dataset

The dataset used is wikiner_fr which represents ~170k sentences labelized in 5 categories : * PER: personality ; * LOC: location ; * ORG: organization ; * MISC: Miscellaneous entities ; * O: background (Other). Evaluation results

class	precision (%)	recall (%)	f1 (%)	support (#sub-word)
global	98.35	98.36	98.35	492'243
PER	96.22	97.41	96.81	27'842
LOC	93.93	93.50	93.72	31'431
ORG	85.13	87.08	86.10	7'662
MISC	88.55	81.84	85.06	13'553
O	99.40	99.55	99.47	411'755

How to use DistilCamemBERT-NER

from transformers import pipeline

ner = pipeline('ner', model="cmarkea/distilcamembert-base-ner", tokenizer="cmarkea/distilcamembert-base-ner", aggregation_strategy="simple")
result = ner("Le Crédit Mutuel Arkéa est une banque Française, elle comprend le CMB qui est une banque située en Bretagne et le CMSO qui est une banque qui se situe principalement en Aquitaine. C'est sous la présidence de Louis Lichou, dans les années 1980 que différentes filiales sont créées au sein du CMB et forment les principales filiales du groupe qui existent encore aujourd'hui (Federal Finance, Suravenir, Financo, etc.).")

result
[{'entity_group': 'ORG',
  'score': 0.99327177,
  'word': 'Crédit Mutuel Arkéa',
  'start': 3,
  'end': 22},
 {'entity_group': 'LOC',
  'score': 0.5869117,
  'word': 'Française',
  'start': 38,
  'end': 47},
 {'entity_group': 'ORG',
  'score': 0.9728106,
  'word': 'CMB',
  'start': 66,
  'end': 69},
 {'entity_group': 'LOC',
  'score': 0.9974824,
  'word': 'Bretagne',
  'start': 99,
  'end': 107},
 {'entity_group': 'ORG',
  'score': 0.956406,
  'word': 'CMSO',
  'start': 114,
  'end': 118},
 {'entity_group': 'LOC',
  'score': 0.99741644,
  'word': 'Aquitaine',
  'start': 169,
  'end': 178},
 {'entity_group': 'PER',
  'score': 0.9988959,
  'word': 'Louis Lichou',
  'start': 208,
  'end': 220},
 {'entity_group': 'ORG',
  'score': 0.93090177,
  'word': 'CMB',
  'start': 291,
  'end': 294},
 {'entity_group': 'ORG',
  'score': 0.9965743,
  'word': 'Federal Finance',
  'start': 374,
  'end': 389},
 {'entity_group': 'ORG',
  'score': 0.99655724,
  'word': 'Suravenir',
  'start': 391,
  'end': 400},
 {'entity_group': 'ORG',
  'score': 0.99653435,
  'word': 'Financo',
  'start': 402,
  'end': 409}]