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favs_sort_classification_v2
This model is a fine-tuned version of bert-base-cased on the sort_v2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1553
- F1: 0.9801
- Roc Auc: 0.9805
- Accuracy: 0.8966
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: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.5589 | 1.0 | 21 | 0.5325 | 0.4815 | 0.6585 | 0.0345 |
| 0.4621 | 2.0 | 42 | 0.4465 | 0.5225 | 0.6780 | 0.0 |
| 0.4144 | 3.0 | 63 | 0.4131 | 0.5950 | 0.7172 | 0.0345 |
| 0.3669 | 4.0 | 84 | 0.3793 | 0.6167 | 0.7279 | 0.0345 |
| 0.3524 | 5.0 | 105 | 0.3455 | 0.6880 | 0.7689 | 0.0690 |
| 0.2987 | 6.0 | 126 | 0.3086 | 0.8116 | 0.8533 | 0.4138 |
| 0.2734 | 7.0 | 147 | 0.2767 | 0.8392 | 0.8772 | 0.5172 |
| 0.2532 | 8.0 | 168 | 0.2483 | 0.8472 | 0.8837 | 0.5172 |
| 0.2166 | 9.0 | 189 | 0.2285 | 0.8707 | 0.9032 | 0.5862 |
| 0.19 | 10.0 | 210 | 0.2012 | 0.9459 | 0.9525 | 0.7586 |
| 0.1833 | 11.0 | 231 | 0.1856 | 0.9530 | 0.9590 | 0.7931 |
| 0.1751 | 12.0 | 252 | 0.1748 | 0.9595 | 0.9610 | 0.7931 |
| 0.173 | 13.0 | 273 | 0.1633 | 0.9467 | 0.9569 | 0.7931 |
| 0.16 | 14.0 | 294 | 0.1553 | 0.9801 | 0.9805 | 0.8966 |
| 0.1396 | 15.0 | 315 | 0.1503 | 0.9733 | 0.9740 | 0.8621 |
| 0.1467 | 16.0 | 336 | 0.1417 | 0.9737 | 0.9785 | 0.8621 |
| 0.1271 | 17.0 | 357 | 0.1380 | 0.9669 | 0.9720 | 0.8621 |
| 0.1228 | 18.0 | 378 | 0.1346 | 0.9669 | 0.9720 | 0.8621 |
| 0.1257 | 19.0 | 399 | 0.1308 | 0.9801 | 0.9805 | 0.8966 |
| 0.1156 | 20.0 | 420 | 0.1280 | 0.9801 | 0.9805 | 0.8966 |
| 0.1242 | 21.0 | 441 | 0.1250 | 0.9801 | 0.9805 | 0.8966 |
| 0.1146 | 22.0 | 462 | 0.1236 | 0.9801 | 0.9805 | 0.8966 |
| 0.1262 | 23.0 | 483 | 0.1228 | 0.9801 | 0.9805 | 0.8966 |
| 0.1268 | 24.0 | 504 | 0.1227 | 0.9801 | 0.9805 | 0.8966 |
| 0.1133 | 25.0 | 525 | 0.1224 | 0.9801 | 0.9805 | 0.8966 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
