<!-- 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. -->
importance_model
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.4867
- Train Sparse Categorical Accuracy: 0.8389
- Validation Loss: 0.6060
- Validation Sparse Categorical Accuracy: 0.8016
- 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 |
|---|---|---|---|---|
| 0.7037 | 0.7614 | 0.6077 | 0.7964 | 0 |
| 0.5683 | 0.8120 | 0.5615 | 0.8106 | 1 |
| 0.4867 | 0.8389 | 0.6060 | 0.8016 | 2 |
Framework versions
- Transformers 4.16.0
- TensorFlow 2.7.0
- Datasets 1.18.1
- Tokenizers 0.11.0
