CamemBERT-bio : a Tasty French Language Model Better for your Health

CamemBERT-bio is a state-of-the-art french biomedical language model built using continual-pretraining from camembert-base. It was trained on a french public biomedical corpus of 413M words containing scientific documents, drug leaflets and clinical cases extrated from theses and articles. It shows 2.54 points of F1 score improvement on average on 5 different biomedical named entity recognition tasks compared to camembert-base.

Absract

Clinical data in hospitals are increasingly accessible for research through clinical data warehouses, however these documents are unstructured. It is therefore necessary to extract information from medical reports to conduct clinical studies. Transfer learning with BERT-like models such as CamemBERT has allowed major advances, especially for named entity recognition. However, these models are trained for plain language and are less efficient on biomedical data. This is why we propose a new french public biomedical dataset on which we have continued the pre-training of CamemBERT. Thus, we introduce a first version of CamemBERT-bio, a specialized public model for the french biomedical domain that shows 2.54 points of F1 score improvement on average on different biomedical named entity recognition tasks.

Developed by: Rian Touchent, Eric Villemonte de La Clergerie
Logo by: Alix Chagué
License: MIT

Training Details

Training Data

Corpus	Details	Size
ISTEX	diverse scientific literature indexed on ISTEX	276 M
CLEAR	drug leaflets	73 M
E3C	various documents from journals, drug leaflets, and clinical cases	64 M
Total		413 M

Training Procedure

We used continual-pretraining from camembert-base. We trained the model using the Masked Language Modeling (MLM) objective with Whole Word Masking for 50k steps during 39 hours with 2 Tesla V100.

Evaluation

Fine-tuning

For fine-tuning, we utilized Optuna to select the hyperparameters. The learning rate was set to 5e-5, with a warmup ratio of 0.224 and a batch size of 16. The fine-tuning process was carried out for 2000 steps. For prediction, a simple linear layer was added on top of the model. Notably, none of the CamemBERT layers were frozen during the fine-tuning process.

Scoring

To evaluate the performance of the model, we used the seqeval tool in strict mode with the IOB2 scheme. For each evaluation, the best fine-tuned model on the validation set was selected to calculate the final score on the test set. To ensure reliability, we averaged over 10 evaluations with different seeds.

Results

Style	Dataset	Score	CamemBERT	CamemBERT-bio
Clinical	CAS1	F1	70.50 ± 1.75	73.03 ± 1.29
		P	70.12 ± 1.93	71.71 ± 1.61
		R	70.89 ± 1.78	74.42 ± 1.49
	CAS2	F1	79.02 ± 0.92	81.66 ± 0.59
		P	77.3 ± 1.36	80.96 ± 0.91
		R	80.83 ± 0.96	82.37 ± 0.69
	E3C	F1	67.63 ± 1.45	69.85 ± 1.58
		P	78.19 ± 0.72	79.11 ± 0.42
		R	59.61 ± 2.25	62.56 ± 2.50
Drug leaflets	EMEA	F1	74.14 ± 1.95	76.71 ± 1.50
		P	74.62 ± 1.97	76.92 ± 1.96
		R	73.68 ± 2.22	76.52 ± 1.62
Scientific	MEDLINE	F1	65.73 ± 0.40	68.47 ± 0.54
		P	64.94 ± 0.82	67.77 ± 0.88
		R	66.56 ± 0.56	69.21 ± 1.32

Environmental Impact estimation

Hardware Type: 2 x Tesla V100
Hours used: 39 hours
Provider: INRIA clusters
Compute Region: Paris, France
Carbon Emitted: 0.84 kg CO2 eq.

Citation information


@inproceedings{touchent-de-la-clergerie-2024-camembert-bio,
    title = "{C}amem{BERT}-bio: Leveraging Continual Pre-training for Cost-Effective Models on {F}rench Biomedical Data",
    author = "Touchent, Rian  and
      de la Clergerie, {\'E}ric",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.241",
    pages = "2692--2701",
    abstract = "Clinical data in hospitals are increasingly accessible for research through clinical data warehouses. However these documents are unstructured and it is therefore necessary to extract information from medical reports to conduct clinical studies. Transfer learning with BERT-like models such as CamemBERT has allowed major advances for French, especially for named entity recognition. However, these models are trained for plain language and are less efficient on biomedical data. Addressing this gap, we introduce CamemBERT-bio, a dedicated French biomedical model derived from a new public French biomedical dataset. Through continual pre-training of the original CamemBERT, CamemBERT-bio achieves an improvement of 2.54 points of F1-score on average across various biomedical named entity recognition tasks, reinforcing the potential of continual pre-training as an equally proficient yet less computationally intensive alternative to training from scratch. Additionally, we highlight the importance of using a standard evaluation protocol that provides a clear view of the current state-of-the-art for French biomedical models.",
}


@inproceedings{touchent:hal-04130187,
  TITLE = {{CamemBERT-bio : Un mod{\`e}le de langue fran{\c c}ais savoureux et meilleur pour la sant{\'e}}},
  AUTHOR = {Touchent, Rian and Romary, Laurent and De La Clergerie, Eric},
  URL = {https://hal.science/hal-04130187},
  BOOKTITLE = {{18e  Conf{\'e}rence en Recherche d'Information et Applications \\ 16e Rencontres Jeunes Chercheurs en RI \\ 30e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles \\ 25e Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues}},
  ADDRESS = {Paris, France},
  EDITOR = {Servan, Christophe and Vilnat, Anne},
  PUBLISHER = {{ATALA}},
  PAGES = {323-334},
  YEAR = {2023},
  KEYWORDS = {comptes rendus m{\'e}dicaux ; TAL clinique ; CamemBERT ; extraction d'information ; biom{\'e}dical ; reconnaissance d'entit{\'e}s nomm{\'e}es},
  HAL_ID = {hal-04130187},
  HAL_VERSION = {v1},
}

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