readme: ---
  license: mit
  tags:
  - generated_from_keras_callback
  model-index:
  - name: XLMRobertaTrainedOnSWEz
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information Keras had access to. You should
  probably proofread and complete it, then remove this comment. -->
  
  # XLMRobertaTrainedOnSWEz
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Train Loss: 0.5177
  - Train End Logits Accuracy: 0.8335
  - Train Start Logits Accuracy: 0.8275
  - Validation Loss: 1.0855
  - Validation End Logits Accuracy: 0.7143
  - Validation Start Logits Accuracy: 0.7089
  - Epoch: 3
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 29248, '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 End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
  |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
  | 1.3112     | 0.6049                    | 0.5996                      | 0.9920          | 0.6931                         | 0.6899                           | 0     |
  | 0.8736     | 0.7267                    | 0.7230                      | 0.9677          | 0.7119                         | 0.7100                           | 1     |
  | 0.6621     | 0.7879                    | 0.7839                      | 1.0244          | 0.7074                         | 0.7058                           | 2     |
  | 0.5177     | 0.8335                    | 0.8275                      | 1.0855          | 0.7143                         | 0.7089                           | 3     |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - TensorFlow 2.6.4
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  