samy-hadj/bioBertFineTuned

BioBERT Fine-Tuned

Model Description

This model is a fine-tuned version of BioBERT, a pre-trained biomedical language model, adapted for medical text classification. It classifies medical abstracts into predefined categories based on their content.

Training Data

• Dataset: Contains 2286 medical abstracts across five categories:
  • Neoplasms
  • Digestive System Diseases
  • Nervous System Diseases
  • Cardiovascular Diseases
  • General Pathological Conditions
• Preprocessing: Includes normalization, lemmatization, tokenization, stopword removal, and medical term standardization.

Intended Use

• Medical Text Classification: This model can be used for categorizing medical abstracts and research papers into relevant medical departments.

Limitations

• Not suitable for general-purpose NLP tasks.
• Domain-specific: The model may not perform well outside the medical field or with non-English text.