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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: tmp3y468_8j |
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results: [] |
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widget: |
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- text: "Ich liebe dich!" |
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example_title: "Non-vulgar" |
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- text: "Leck mich am arsch" |
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example_title: "Vulgar" |
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--- |
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# KIZervus |
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This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/distilbert-base-german-cased). |
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It is trained to classify german text into the classes "vulgar" speech and "non-vulgar" speech. |
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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 |
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Both data and training procedure are documented in the GitHub repo. Your are welcome to contribute. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.4640 |
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- Train Accuracy: 0.7744 |
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- Validation Loss: 0.4852 |
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- Validation Accuracy: 0.7937 |
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- Epoch: 1 |
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## Training procedure |
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For details, see the repo and documentation here: https://github.com/NKDataConv/KIZervus |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |
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|:----------:|:--------------:|:---------------:|:-------------------:|:-----:| |
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| 0.4830 | 0.7617 | 0.5061 | 0.7406 | 0 | |
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| 0.4640 | 0.7744 | 0.4852 | 0.7937 | 1 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- TensorFlow 2.8.2 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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### Supporter |
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