readme: ---
  license: apache-2.0
  tags:
  - generated_from_keras_callback
  model-index:
  - name: tmp3y468_8j
    results: []
  widget:
  - text: "Ich liebe dich!"
    example_title: "Non-vulgar"
  - text: "Leck mich am arsch"
    example_title: "Vulgar"
  ---
  
  # KIZervus
  
  This model is a fine-tuned version of [distilbert-base-german-cased](https://huggingface.co/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
  
  ### Supporter
  
  ![BMBF Logo](./BMBF_Logo.png)
  