readme: ---
  tags:
  - generated_from_keras_callback
  model-index:
  - name: specializedMedBERT
    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. -->
  
  # specializedMedBERT
  
  This model is a fine-tuned version of [smanjil/German-MedBERT](https://huggingface.co/smanjil/German-MedBERT) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Train Loss: 1.5315
  - Train Sparse Categorical Accuracy: 0.8651
  - Validation Loss: 9.0076
  - Validation Sparse Categorical Accuracy: 0.0
  - Epoch: 99
  
  ## 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': 'ExponentialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 10000, 'decay_rate': 0.9, 'staircase': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
  - training_precision: float32
  
  ### Training results
  
  | Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
  | 5.9919     | 0.0033                            | 6.0158          | 0.0                                    | 0     |
  | 5.9288     | 0.0033                            | 6.1132          | 0.0                                    | 1     |
  | 5.8990     | 0.0033                            | 6.2120          | 0.0                                    | 2     |
  | 5.8696     | 0.0099                            | 6.3116          | 0.0                                    | 3     |
  | 5.8431     | 0.0066                            | 6.4143          | 0.0                                    | 4     |
  | 5.8144     | 0.0066                            | 6.5193          | 0.0                                    | 5     |
  | 5.7883     | 0.0066                            | 6.6265          | 0.0                                    | 6     |
  | 5.7623     | 0.0132                            | 6.7340          | 0.0                                    | 7     |
  | 5.7480     | 0.0164                            | 6.8418          | 0.0                                    | 8     |
  | 5.7154     | 0.0197                            | 6.9496          | 0.0                                    | 9     |
  | 5.6937     | 0.0164                            | 7.0557          | 0.0                                    | 10    |
  | 5.6707     | 0.0230                            | 7.1567          | 0.0                                    | 11    |
  | 5.6377     | 0.0263                            | 7.2541          | 0.0                                    | 12    |
  | 5.6186     | 0.0362                            | 7.3436          | 0.0                                    | 13    |
  | 5.5964     | 0.0428                            | 7.4283          | 0.0                                    | 14    |
  | 5.5677     | 0.0428                            | 7.5049          | 0.0                                    | 15    |
  | 5.5230     | 0.0428                            | 7.5759          | 0.0                                    | 16    |
  | 5.5032     | 0.0526                            | 7.6312          | 0.0                                    | 17    |
  | 5.4563     | 0.0526                            | 7.6620          | 0.0                                    | 18    |
  | 5.4089     | 0.0625                            | 7.6913          | 0.0                                    | 19    |
  | 5.3804     | 0.0691                            | 7.7320          | 0.0                                    | 20    |
  | 5.3208     | 0.0757                            | 7.7632          | 0.0                                    | 21    |
  | 5.2766     | 0.0855                            | 7.7825          | 0.0                                    | 22    |
  | 5.2238     | 0.0855                            | 7.7927          | 0.0                                    | 23    |
  | 5.1774     | 0.0987                            | 7.7880          | 0.0                                    | 24    |
  | 5.1243     | 0.0954                            | 7.8373          | 0.0                                    | 25    |
  | 5.0660     | 0.1053                            | 7.7977          | 0.0                                    | 26    |
  | 4.9892     | 0.1612                            | 7.8100          | 0.0                                    | 27    |
  | 4.9268     | 0.1678                            | 7.8606          | 0.0                                    | 28    |
  | 4.8715     | 0.1776                            | 7.8000          | 0.0                