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albert-base-ours-run-2
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2462
- Accuracy: 0.695
- Precision: 0.6550
- Recall: 0.6529
- F1: 0.6539
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: 1e-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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.999 | 1.0 | 200 | 0.9155 | 0.615 | 0.5590 | 0.5590 | 0.5524 |
| 0.7736 | 2.0 | 400 | 0.8488 | 0.6 | 0.5639 | 0.5689 | 0.5256 |
| 0.5836 | 3.0 | 600 | 0.8760 | 0.67 | 0.6259 | 0.6158 | 0.6191 |
| 0.4153 | 4.0 | 800 | 1.0050 | 0.675 | 0.6356 | 0.6212 | 0.5974 |
| 0.3188 | 5.0 | 1000 | 1.2033 | 0.655 | 0.6254 | 0.5977 | 0.5991 |
| 0.2335 | 6.0 | 1200 | 1.3407 | 0.625 | 0.5955 | 0.6039 | 0.5937 |
| 0.1752 | 7.0 | 1400 | 1.4246 | 0.72 | 0.6846 | 0.6815 | 0.6820 |
| 0.1056 | 8.0 | 1600 | 1.9654 | 0.69 | 0.6589 | 0.6251 | 0.6311 |
| 0.0696 | 9.0 | 1800 | 1.9376 | 0.715 | 0.6908 | 0.6632 | 0.6627 |
| 0.0352 | 10.0 | 2000 | 1.9970 | 0.72 | 0.6880 | 0.6784 | 0.6817 |
| 0.0227 | 11.0 | 2200 | 2.1449 | 0.705 | 0.6901 | 0.6641 | 0.6679 |
| 0.0199 | 12.0 | 2400 | 2.2213 | 0.72 | 0.6891 | 0.6685 | 0.6749 |
| 0.0077 | 13.0 | 2600 | 2.1500 | 0.69 | 0.6729 | 0.6704 | 0.6647 |
| 0.0067 | 14.0 | 2800 | 2.1780 | 0.69 | 0.6632 | 0.6651 | 0.6621 |
| 0.0034 | 15.0 | 3000 | 2.1759 | 0.71 | 0.6800 | 0.6786 | 0.6788 |
| 0.0013 | 16.0 | 3200 | 2.2139 | 0.71 | 0.6760 | 0.6721 | 0.6735 |
| 0.0005 | 17.0 | 3400 | 2.2282 | 0.7 | 0.6606 | 0.6593 | 0.6599 |
| 0.0003 | 18.0 | 3600 | 2.2257 | 0.7 | 0.6606 | 0.6593 | 0.6599 |
| 0.0003 | 19.0 | 3800 | 2.2492 | 0.695 | 0.6550 | 0.6529 | 0.6539 |
| 0.0002 | 20.0 | 4000 | 2.2462 | 0.695 | 0.6550 | 0.6529 | 0.6539 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
