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roberta
This model is a fine-tuned version of rinna/japanese-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2997
- Accuracy: 0.9359
- Recall: 0.8810
- Precision: 0.8836
- F1: 0.8823
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:
- learning_rate: 1.2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
|---|---|---|---|---|---|---|---|
| 0.2001 | 1.0 | 925 | 0.1768 | 0.9395 | 0.8651 | 0.9083 | 0.8862 |
| 0.1598 | 2.0 | 1850 | 0.2184 | 0.9365 | 0.8770 | 0.8884 | 0.8827 |
| 0.1093 | 3.0 | 2775 | 0.2577 | 0.9376 | 0.8700 | 0.8976 | 0.8836 |
| 0.0975 | 4.0 | 3700 | 0.2864 | 0.9351 | 0.9028 | 0.8650 | 0.8835 |
| 0.0661 | 5.0 | 4625 | 0.2997 | 0.9359 | 0.8810 | 0.8836 | 0.8823 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
