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Regression_albert_NOaug_MSEloss
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: 0.4715
- Mse: 0.4715
- Mae: 0.6001
- R2: 0.1320
- Accuracy: 0.4737
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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 33 | 0.2966 | 0.2966 | 0.4630 | 0.1139 | 0.7568 |
| No log | 2.0 | 66 | 0.2679 | 0.2679 | 0.4039 | 0.1995 | 0.7568 |
| No log | 3.0 | 99 | 0.4088 | 0.4088 | 0.5125 | -0.2213 | 0.5405 |
| No log | 4.0 | 132 | 0.4331 | 0.4331 | 0.5399 | -0.2939 | 0.4865 |
| No log | 5.0 | 165 | 0.3699 | 0.3699 | 0.4317 | -0.1053 | 0.6757 |
| No log | 6.0 | 198 | 0.3456 | 0.3456 | 0.4117 | -0.0325 | 0.6216 |
| No log | 7.0 | 231 | 0.3371 | 0.3371 | 0.4155 | -0.0072 | 0.6757 |
| No log | 8.0 | 264 | 0.3261 | 0.3261 | 0.3811 | 0.0256 | 0.7297 |
| No log | 9.0 | 297 | 0.2312 | 0.2312 | 0.2705 | 0.3092 | 0.8108 |
| No log | 10.0 | 330 | 0.3194 | 0.3194 | 0.3681 | 0.0457 | 0.6757 |
| No log | 11.0 | 363 | 0.3638 | 0.3638 | 0.4124 | -0.0870 | 0.6757 |
| No log | 12.0 | 396 | 0.3101 | 0.3101 | 0.3630 | 0.0734 | 0.7027 |
| No log | 13.0 | 429 | 0.2762 | 0.2762 | 0.3221 | 0.1748 | 0.7568 |
| No log | 14.0 | 462 | 0.2970 | 0.2970 | 0.3376 | 0.1126 | 0.7297 |
| No log | 15.0 | 495 | 0.3185 | 0.3185 | 0.3532 | 0.0483 | 0.7297 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
