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Towards a ModernBERT Model Adapted to the Biomedical Domain in Italian.

Mattia Robbiani1, Lorenzo Scarciglia1, Veronika Levdik1

  • 1MeDiTech Institute, SUPSI, Lugano, Switzerland.

Studies in Health Technology and Informatics
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Summary

We developed bio-modernbert-ita, an Italian BERT model for biomedical text. This model shows improved performance in named entity recognition tasks, offering a foundation for future Italian biomedical NLP research.

Keywords:
BERTBiomedicalItalianModernBERTNamed Entity Recognition

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Biomedical Informatics

Background:

  • Limited availability of Italian biomedical data hinders the development of specialized NLP models.
  • Existing general-purpose language models may not capture the nuances of biomedical terminology.

Purpose of the Study:

  • To create a domain-adapted Italian BERT model (bio-modernbert-ita) for biomedical text processing.
  • To evaluate the model's effectiveness in named entity recognition (NER) tasks.

Main Methods:

  • Translation of 22 million PubMed abstracts into Italian to form a training corpus.
  • Continued pre-training of the Modern-BERT model on the translated corpus.
  • Fine-tuning the model for named entity recognition and evaluation on E3C and PharmaER.IT datasets.

Main Results:

  • bio-modernbert-ita achieved an F1-score of 56.7% on the E3C dataset.
  • The model reached an F1-score of 75.9% on the PharmaER.IT dataset.
  • Demonstrated performance improvements of +4.9% (E3C) and +6.5% (PharmaER.IT) over the baseline model.

Conclusions:

  • bio-modernbert-ita serves as a valuable starting point for Italian biomedical NLP.
  • The domain adaptation approach effectively enhances model performance on specialized tasks.
  • Future work includes further pre-training and expansion to clinical domain applications.