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Regression_bert_NOaug_CustomLoss
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0264
- Train Mae: 0.1981
- Train Mse: 0.0536
- Train R2-score: 0.9557
- Validation Loss: 0.1484
- Validation Mae: 0.3703
- Validation Mse: 0.2656
- Validation R2-score: 0.8862
- Epoch: 14
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-04, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
Train Loss | Train Mae | Train Mse | Train R2-score | Validation Loss | Validation Mae | Validation Mse | Validation R2-score | Epoch |
---|---|---|---|---|---|---|---|---|
0.1477 | 0.5158 | 0.3587 | 0.8489 | 0.1118 | 0.5348 | 0.3366 | 0.8997 | 0 |
0.1280 | 0.4634 | 0.2930 | 0.8414 | 0.1375 | 0.4847 | 0.3121 | 0.8873 | 1 |
0.1232 | 0.4331 | 0.2728 | -0.3855 | 0.1453 | 0.5454 | 0.4140 | 0.8773 | 2 |
0.0862 | 0.3752 | 0.2042 | 0.8843 | 0.1683 | 0.4117 | 0.2940 | 0.8728 | 3 |
0.0827 | 0.3573 | 0.1824 | 0.9046 | 0.1383 | 0.3792 | 0.2434 | 0.8940 | 4 |
0.0701 | 0.4034 | 0.2084 | 0.8164 | 0.1313 | 0.4766 | 0.3297 | 0.8879 | 5 |
0.0473 | 0.2988 | 0.1245 | 0.8744 | 0.1544 | 0.4001 | 0.2930 | 0.8780 | 6 |
0.0370 | 0.2501 | 0.0887 | 0.8672 | 0.1464 | 0.4236 | 0.3019 | 0.8809 | 7 |
0.0346 | 0.3122 | 0.1224 | 0.9196 | 0.1296 | 0.4837 | 0.3147 | 0.8885 | 8 |
0.0303 | 0.2493 | 0.0864 | 0.9624 | 0.1399 | 0.4292 | 0.2975 | 0.8876 | 9 |
0.0312 | 0.2527 | 0.0862 | 0.9426 | 0.1436 | 0.3984 | 0.2722 | 0.8876 | 10 |
0.0301 | 0.2160 | 0.0657 | 0.6312 | 0.1479 | 0.3819 | 0.2836 | 0.8849 | 11 |
0.0275 | 0.2286 | 0.0712 | 0.9543 | 0.1473 | 0.3770 | 0.2634 | 0.8851 | 12 |
0.0272 | 0.2209 | 0.0656 | 0.9691 | 0.1372 | 0.4141 | 0.2886 | 0.8899 | 13 |
0.0264 | 0.1981 | 0.0536 | 0.9557 | 0.1484 | 0.3703 | 0.2656 | 0.8862 | 14 |
Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3