datasets:
- samirmsallem/argument_mining_de
language:
- de
metrics:
- accuracy
base_model:
- deepset/gbert-large
pipeline_tag: text-classification
library_name: transformers
model-index:
- name: checkpoints
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: samirmsallem/argument_mining_de
type: samirmsallem/argument_mining_de
metrics:
- name: Accuracy
type: accuracy
value: 0.9383561643835616
Text classification model for argument mining and detection
gbert-large-argument_mining is a text classification model in the scientific domain in German, finetuned from the model gbert-large. 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:
| Epoch | Accuracy | Loss |
|---|---|---|
| 1.0 | 0.9178 | 0.2491 |
| 2.0 | 0.9315 | 0.2479 |
| 3.0 | 0.9315 | 0.2853 |
| 4.0 | 0.9384 | 0.2503 |
| 5.0 | 0.9110 | 0.3678 |
| 6.0 | 0.9315 | 0.3436 |
| 7.0 | 0.9247 | 0.3807 |
| 8.0 | 0.9178 | 0.3862 |
| 9.0 | 0.9178 | 0.3953 |
| 10.0 | 0.9178 | 0.3964 |
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
| Text Classification Tag | Text Classification Label |
|---|---|
| 0 | CLAIM |
| 1 | COUNTERCLAIM |
| 2 | LINK |
| 3 | CONC |
| 4 | FUT |
| 5 | OTH |