gbert-base-claim_premise is a text classification model in the non-scientific domain in German, finetuned from the model
gbert-base.
It was trained using a
annotated dataset containing claim and premise sentences from essay feedback. The dataset was created by T. Wambsganss, C. Niklaus, M. Söllner, S. Handschuh and J. M. Leimeister and is available
here.
Training was conducted on a 10 epoch fine-tuning approach, however this repository contains the results of the second epoch, since it has the best accuracy:
In relation to the dataset, the model demonstrates that it can effectively learn to distinguish between the two classes claim and premise. However, the rapid onset of overfitting after epoch 2 suggests that the dataset is imbalanced and noisy. Further work should enable the model to be trained on more robust data to ensure better evaluation results.
