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results
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.9905
- Accuracy: 0.5067
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 132 | 0.6630 | 0.6267 |
| No log | 2.0 | 264 | 0.6697 | 0.6267 |
| No log | 3.0 | 396 | 0.6605 | 0.6267 |
| 0.6686 | 4.0 | 528 | 0.6797 | 0.6267 |
| 0.6686 | 5.0 | 660 | 0.6599 | 0.5733 |
| 0.6686 | 6.0 | 792 | 0.6702 | 0.58 |
| 0.6686 | 7.0 | 924 | 0.7593 | 0.5267 |
| 0.6278 | 8.0 | 1056 | 0.7622 | 0.6 |
| 0.6278 | 9.0 | 1188 | 0.8147 | 0.6067 |
| 0.6278 | 10.0 | 1320 | 1.2285 | 0.5733 |
| 0.6278 | 11.0 | 1452 | 1.2681 | 0.58 |
| 0.5453 | 12.0 | 1584 | 1.4571 | 0.5667 |
| 0.5453 | 13.0 | 1716 | 1.5210 | 0.5467 |
| 0.5453 | 14.0 | 1848 | 1.6548 | 0.5733 |
| 0.5453 | 15.0 | 1980 | 1.6931 | 0.5667 |
| 0.4703 | 16.0 | 2112 | 1.8606 | 0.5867 |
| 0.4703 | 17.0 | 2244 | 1.9779 | 0.56 |
| 0.4703 | 18.0 | 2376 | 2.3998 | 0.4933 |
| 0.3567 | 19.0 | 2508 | 2.2930 | 0.5 |
| 0.3567 | 20.0 | 2640 | 2.6606 | 0.4933 |
| 0.3567 | 21.0 | 2772 | 2.4945 | 0.4933 |
| 0.3567 | 22.0 | 2904 | 2.6740 | 0.5133 |
| 0.2371 | 23.0 | 3036 | 2.7472 | 0.5 |
| 0.2371 | 24.0 | 3168 | 2.7916 | 0.5 |
| 0.2371 | 25.0 | 3300 | 2.8399 | 0.5 |
| 0.2371 | 26.0 | 3432 | 2.8665 | 0.52 |
| 0.1688 | 27.0 | 3564 | 2.9246 | 0.5133 |
| 0.1688 | 28.0 | 3696 | 2.9675 | 0.5 |
| 0.1688 | 29.0 | 3828 | 2.9967 | 0.5067 |
| 0.1688 | 30.0 | 3960 | 2.9905 | 0.5067 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
