This paper presents a fine-tuned German Medical BERT model for the medical domain, achieving improved performance on the NTS-ICD-10 text classification task. The model was trained using PyTorch and Hugging Face library on Colab GPU, with standard parameter settings and up to 25 epochs for classification. Evaluation results show significant improvement in micro precision, recall, and F1 score compared to the base German BERT model.
This paper presents a fine-tuned German Medical BERT model for the medical domain, achieving improved performance on the NTS-ICD-10 text classification task. The model was trained using PyTorch and Hugging Face library on Colab GPU, with standard parameter settings and up to 25 epochs for classification. Evaluation results show significant improvement in micro precision, recall, and F1 score compared to the base German BERT model.
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This is a fine-tuned model on the Medical domain for the German language and based on German BERT. This model has only been trained to improve on-target tasks (Masked Language Model). It can later be used to perform a downstream task of your needs, while I performed it for the NTS-ICD-10 text classification task.
Language model: bert-base-german-cased
Language: German
Fine-tuning: Medical articles (diseases, symptoms, therapies, etc..)
Eval data: NTS-ICD-10 dataset (Classification)
Infrastructure: Google Colab
Models | P | R | F1 |
---|---|---|---|
German BERT | 86.04 | 75.82 | 80.60 |
German MedBERT-256 (fine-tuned) | 87.41 | 77.97 | 82.42 |
German MedBERT-512 (fine-tuned) | 87.75 | 78.26 | 82.73 |
Manjil Shrestha: shresthamanjil21 [at] gmail.com
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