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
Fine-tuning: Medical articles (diseases, symptoms, therapies, etc..)
Eval data: NTS-ICD-10 dataset (Classification)
Infrastructure: Google Colab
- We fine-tuned using Pytorch with Huggingface library on Colab GPU.
- With standard parameter settings for fine-tuning as mentioned in the original BERT paper.
- Although had to train for up to 25 epochs for classification.
|German MedBERT-256 (fine-tuned)||87.41||77.97||82.42|
|German MedBERT-512 (fine-tuned)||87.75||78.26||82.73|
shresthamanjil21 [at] gmail.com
Related Paper: Report
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