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PFPC_model
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4653
- Accuracy: 0.9042
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: 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
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 285 | 1.2663 | 0.7251 |
| 1.6418 | 2.0 | 570 | 0.7247 | 0.8420 |
| 1.6418 | 3.0 | 855 | 0.5652 | 0.8657 |
| 0.5038 | 4.0 | 1140 | 0.4997 | 0.8769 |
| 0.5038 | 5.0 | 1425 | 0.4709 | 0.8905 |
| 0.2428 | 6.0 | 1710 | 0.4769 | 0.8968 |
| 0.2428 | 7.0 | 1995 | 0.4764 | 0.8980 |
| 0.1256 | 8.0 | 2280 | 0.4716 | 0.9067 |
| 0.0758 | 9.0 | 2565 | 0.4710 | 0.9055 |
| 0.0758 | 10.0 | 2850 | 0.4653 | 0.9042 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
