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

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