metadata
language: de
tags:
- exbert
- German
German Medical BERT
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.
Overview
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
Details
- 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.
Performance (Micro precision, recall, and f1 score for multilabel code classification)
| 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 |
Author
Manjil Shrestha: shresthamanjil21 [at] gmail.com
Related Paper: Report
Get in touch: LinkedIn