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jonaskoenig/destillbert-uncased-future_statements
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0092
- Train Sparse Categorical Accuracy: 0.9975
- Validation Loss: 0.0184
- Validation Sparse Categorical Accuracy: 0.9954
- Epoch: 2
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 |
|---|---|---|---|---|
| 0.0596 | 0.9804 | 0.0181 | 0.9953 | 0 |
| 0.0154 | 0.9957 | 0.0147 | 0.9959 | 1 |
| 0.0092 | 0.9975 | 0.0184 | 0.9954 | 2 |
Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
