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 two with the same consumption power thanks to DistilCamemBERT .

Dataset

The dataset used is wikiner_fr , which represents ~170k sentences labeled in 5 categories :

• PER: personality ;
• LOC: location ;
• ORG: organization ;
• MISC: miscellaneous entities (movies title, books, etc.) ;
• O: background (Outside entity).

Evaluation results

Benchmark

This model performance is compared to 2 reference models (see below) with the metric f1 score. For the mean inference time measure, an AMD Ryzen 5 4500U @ 2.3GHz with 6 cores was used:

How to use DistilCamemBERT-NER

from transformers import pipeline

ner = pipeline(
    task='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': 0.9974479,
  'word': 'Crédit Mutuel Arkéa',
  'start': 3,
  'end': 22},
 {'entity_group': 'LOC',
  'score': 0.9000358,
  'word': 'Française',
  'start': 38,
  'end': 47},
 {'entity_group': 'ORG',
  'score': 0.9788757,
  'word': 'CMB',
  'start': 66,
  'end': 69},
 {'entity_group': 'LOC',
  'score': 0.99919766,
  'word': 'Bretagne',
  'start': 99,
  'end': 107},
 {'entity_group': 'ORG',
  'score': 0.9594884,
  'word': 'CMSO',
  'start': 114,
  'end': 118},
 {'entity_group': 'LOC',
  'score': 0.99935514,
  'word': 'Aquitaine',
  'start': 169,
  'end': 178},
 {'entity_group': 'PER',
  'score': 0.99911094,
  'word': 'Louis Lichou',
  'start': 208,
  'end': 220},
 {'entity_group': 'ORG',
  'score': 0.96226394,
  'word': 'CMB',
  'start': 291,
  'end': 294},
 {'entity_group': 'ORG',
  'score': 0.9983959,
  'word': 'Federal Finance',
  'start': 374,
  'end': 389},
 {'entity_group': 'ORG',
  'score': 0.9984454,
  'word': 'Suravenir',
  'start': 391,
  'end': 400},
 {'entity_group': 'ORG',
  'score': 0.9985084,
  'word': 'Financo',
  'start': 402,
  'end': 409}]

Optimum + ONNX

from optimum.onnxruntime import ORTModelForTokenClassification
from transformers import AutoTokenizer, pipeline

HUB_MODEL = "cmarkea/distilcamembert-base-nli"
tokenizer = AutoTokenizer.from_pretrained(HUB_MODEL)
model = ORTModelForTokenClassification.from_pretrained(HUB_MODEL)
onnx_qa = pipeline("token-classification", model=model, tokenizer=tokenizer)

# Quantized onnx model
quantized_model = ORTModelForTokenClassification.from_pretrained(
    HUB_MODEL, file_name="model_quantized.onnx"
)

Citation

@inproceedings{delestre:hal-03674695,
  TITLE = {{DistilCamemBERT : une distillation du mod{\`e}le fran{\c c}ais CamemBERT}},
  AUTHOR = {Delestre, Cyrile and Amar, Abibatou},
  URL = {https://hal.archives-ouvertes.fr/hal-03674695},
  BOOKTITLE = {{CAp (Conf{\'e}rence sur l'Apprentissage automatique)}},
  ADDRESS = {Vannes, France},
  YEAR = {2022},
  MONTH = Jul,
  KEYWORDS = {NLP ; Transformers ; CamemBERT ; Distillation},
  PDF = {https://hal.archives-ouvertes.fr/hal-03674695/file/cap2022.pdf},
  HAL_ID = {hal-03674695},
  HAL_VERSION = {v1},
}