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BioBERT Fine-tuning for Named Entitiy Recognition

BioBERTBERTFinetuneFinetuningNER
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Apr 14, 2025 2:14 PM
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This workflow shows an example on how to fine-tune a BERT model (in this case BioBERT) and use the trained model afterwards. BioBERT is a domain-specific language representation model pre-trained on large-scale biomedical corpora and thus performs better than base BERT when using biomedical data. In this example a BioBERT model is finetuned on the NCBI Disease dataset for token classification. A common token classification task is Named Entity Recognition (NER). The dataset contains a collection of 793 PubMed abstracts which are fully annotated including the disease names as tokens and the corresponding tags. The finetuned model is then used to recognize diseases in a given text which will then be displayed using spacy visualization for entity recognition. BioBERT: Title: BioBERT: a pre-trained biomedical language representation model for biomedical text mining Authors: Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo NCBI Disease dataset: Title: NCBI disease corpus: a resource for disease name recognition and concept normalization Authors: Dougan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong

External resources

  • Token Classification
  • Spacy Visualizers
  • NCBI Disease Corpus: A Resource for Disease Name Recognition and
  • Huggingface NCBI Disease Dataset
  • Huggingface BioBERT
  • BioBERT: a pre-trained biomedical language representation model for biomedical text mining
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