readme: ---
  license: mit
  tags:
  - generated_from_keras_callback
  model-index:
  - name: xtremedistil-l6-h256-uncased-future-time-references-D2
    results: []
  datasets:
  - jonaskoenig/trump_administration_statement
  - jonaskoenig/future-time-refernces-static-filter-D2
  ---
  
  <!-- 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. -->
  
  # xtremedistil-l6-h256-uncased-future-time-references-D2
  
  This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on [jonaskoenig/future-time-refernces-static-filter-D2](https://huggingface.co/datasets/jonaskoenig/future-time-refernces-static-filter-D2) and [jonaskoenig/trump_administration_statement](https://huggingface.co/datasets/jonaskoenig/trump_administration_statement).
  It achieves the following results on the evaluation set:
  - Train Loss: 0.0055
  - Train Sparse Categorical Accuracy: 0.9984
  - Validation Loss: 0.0074
  - Validation Sparse Categorical Accuracy: 0.9984
  - Epoch: 4
  
  ## 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': 5e-05, '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 |
  |:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
  | 0.0388     | 0.9891                            | 0.0154          | 0.9957                                 | 0     |
  | 0.0133     | 0.9962                            | 0.0088          | 0.9975                                 | 1     |
  | 0.0087     | 0.9974                            | 0.0081          | 0.9978                                 | 2     |
  | 0.0068     | 0.9980                            | 0.0074          | 0.9982                                 | 3     |
  | 0.0055     | 0.9984                            | 0.0074          | 0.9984                                 | 4     |
  
  The test accuracy is: 99.81%
  
  ### Framework versions
  
  - Transformers 4.20.1
  - TensorFlow 2.9.1
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  