gbert-base-argument_mining

Text classification model for argument mining and detection

gbert-base-argument_mining is a text classification model in the scientific domain in German, finetuned from the model gbert-base. It was trained using a synthetically created, annotated dataset containing different sentence types occuring in conclusions of scientific theses and papers.

Training

Training was conducted on a 10 epoch fine-tuning approach, however this repository contains the results of the fourth 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 4 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.

Text Classification Tags