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}]