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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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