Regression_albert_4
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: 0.1353
- Mse: 0.1353
- Mae: 0.3311
- R2: 0.0037
- Accuracy: 0.8421
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 33 | 0.0644 | 0.0644 | 0.1871 | 0.2305 | 0.9459 |
| No log | 2.0 | 66 | 0.1220 | 0.1220 | 0.2936 | -0.4587 | 0.8919 |
| No log | 3.0 | 99 | 0.0755 | 0.0755 | 0.2180 | 0.0979 | 0.9459 |
| No log | 4.0 | 132 | 0.0662 | 0.0662 | 0.1757 | 0.2086 | 0.9189 |
| No log | 5.0 | 165 | 0.0827 | 0.0827 | 0.1978 | 0.0121 | 0.8919 |
| No log | 6.0 | 198 | 0.0962 | 0.0962 | 0.2147 | -0.1498 | 0.9189 |
| No log | 7.0 | 231 | 0.0918 | 0.0918 | 0.1867 | -0.0973 | 0.8919 |
| No log | 8.0 | 264 | 0.0955 | 0.0955 | 0.2075 | -0.1419 | 0.8378 |
| No log | 9.0 | 297 | 0.0950 | 0.0950 | 0.2361 | -0.1358 | 0.8649 |
| No log | 10.0 | 330 | 0.0875 | 0.0875 | 0.1819 | -0.0455 | 0.8108 |
| No log | 11.0 | 363 | 0.0922 | 0.0922 | 0.2030 | -0.1020 | 0.8649 |
| No log | 12.0 | 396 | 0.0976 | 0.0976 | 0.2194 | -0.1666 | 0.8378 |
| No log | 13.0 | 429 | 0.0872 | 0.0872 | 0.2206 | -0.0416 | 0.8649 |
| No log | 14.0 | 462 | 0.0810 | 0.0810 | 0.1818 | 0.0315 | 0.8919 |
| No log | 15.0 | 495 | 0.0877 | 0.0877 | 0.1861 | -0.0485 | 0.9189 |
| 0.0535 | 16.0 | 528 | 0.0882 | 0.0882 | 0.1963 | -0.0541 | 0.8919 |
| 0.0535 | 17.0 | 561 | 0.0814 | 0.0814 | 0.1869 | 0.0268 | 0.9189 |
| 0.0535 | 18.0 | 594 | 0.0902 | 0.0902 | 0.1953 | -0.0775 | 0.8649 |
| 0.0535 | 19.0 | 627 | 0.0934 | 0.0934 | 0.1957 | -0.1169 | 0.8649 |
| 0.0535 | 20.0 | 660 | 0.0923 | 0.0923 | 0.1928 | -0.1027 | 0.8649 |
Framework versions
- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2