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multimodal-traj-class-no-numtransform
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: 1.1964
- Acc: 0.7237
- Relacc: 0.8446
- Num Fours: 617
- Mcc: 0.6029
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Acc | Relacc | Num Fours | Mcc |
|---|---|---|---|---|---|---|---|
| 0.9461 | 1.0 | 1212 | 0.8645 | 0.6752 | 0.7586 | 680 | 0.5115 |
| 0.8387 | 2.0 | 2424 | 0.7880 | 0.6979 | 0.7889 | 630 | 0.5526 |
| 0.7565 | 3.0 | 3636 | 0.7489 | 0.7183 | 0.8015 | 636 | 0.5851 |
| 0.6997 | 4.0 | 4848 | 0.7542 | 0.7061 | 0.7908 | 574 | 0.5569 |
| 0.6516 | 5.0 | 6060 | 0.6806 | 0.7388 | 0.8192 | 660 | 0.6176 |
| 0.6049 | 6.0 | 7272 | 0.6898 | 0.7406 | 0.8395 | 638 | 0.6269 |
| 0.5526 | 7.0 | 8484 | 0.6848 | 0.7408 | 0.8413 | 648 | 0.6288 |
| 0.5343 | 8.0 | 9696 | 0.6904 | 0.7359 | 0.8413 | 645 | 0.6207 |
| 0.4855 | 9.0 | 10908 | 0.7219 | 0.7400 | 0.8456 | 587 | 0.6253 |
| 0.4618 | 10.0 | 12120 | 0.7310 | 0.7464 | 0.8448 | 624 | 0.6314 |
| 0.4326 | 11.0 | 13332 | 0.7298 | 0.7575 | 0.8508 | 658 | 0.6536 |
| 0.4098 | 12.0 | 14544 | 0.8706 | 0.7266 | 0.8395 | 611 | 0.6026 |
| 0.3707 | 13.0 | 15756 | 0.8682 | 0.7431 | 0.8415 | 629 | 0.6260 |
| 0.3377 | 14.0 | 16968 | 0.9299 | 0.7371 | 0.8467 | 590 | 0.6220 |
| 0.315 | 15.0 | 18180 | 0.9393 | 0.7365 | 0.8463 | 635 | 0.6190 |
| 0.2984 | 16.0 | 19392 | 1.0106 | 0.7348 | 0.8426 | 593 | 0.6134 |
| 0.2804 | 17.0 | 20604 | 1.0719 | 0.7307 | 0.8465 | 623 | 0.6118 |
| 0.2644 | 18.0 | 21816 | 1.1245 | 0.7280 | 0.8446 | 642 | 0.6117 |
| 0.2469 | 19.0 | 23028 | 1.1745 | 0.7258 | 0.8430 | 619 | 0.6044 |
| 0.2273 | 20.0 | 24240 | 1.1964 | 0.7237 | 0.8446 | 617 | 0.6029 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
