KIZervus
This model is a fine-tuned version of distilbert-base-german-cased. It is trained to classify german text into the classes "vulgar" speech and "non-vulgar" speech. The data set is a collection of other labeled sources in german. For an overview, see the github repository here: https://github.com/NKDataConv/KIZervus Both data and training procedure are documented in the GitHub repo. Your are welcome to contribute.
It achieves the following results on the evaluation set:
- Train Loss: 0.4640
- Train Accuracy: 0.7744
- Validation Loss: 0.4852
- Validation Accuracy: 0.7937
- Epoch: 1
Training procedure
For details, see the repo and documentation here: https://github.com/NKDataConv/KIZervus
Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 822, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
---|---|---|---|---|
0.4830 | 0.7617 | 0.5061 | 0.7406 | 0 |
0.4640 | 0.7744 | 0.4852 | 0.7937 | 1 |
Framework versions
- Transformers 4.21.2
- TensorFlow 2.8.2
- Datasets 2.2.2
- Tokenizers 0.12.1