MTL-distilbert-base-uncased
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0874
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: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.5593 | 1.0 | 99 | 2.3163 |
| 2.4346 | 2.0 | 198 | 2.2918 |
| 2.3377 | 3.0 | 297 | 2.2345 |
| 2.2953 | 4.0 | 396 | 2.1463 |
| 2.2296 | 5.0 | 495 | 2.1761 |
| 2.2235 | 6.0 | 594 | 2.0721 |
| 2.1878 | 7.0 | 693 | 2.1460 |
| 2.1569 | 8.0 | 792 | 2.0856 |
| 2.1455 | 9.0 | 891 | 2.1039 |
| 2.1391 | 10.0 | 990 | 2.1112 |
| 2.1056 | 11.0 | 1089 | 2.0694 |
| 2.1076 | 12.0 | 1188 | 2.0501 |
| 2.0919 | 13.0 | 1287 | 2.0484 |
| 2.0669 | 14.0 | 1386 | 2.0342 |
| 2.0595 | 15.0 | 1485 | 2.0802 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0