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distilbert-base-uncased-finetuned-IAM
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9814
- Accuracy: 0.5103
- F1: 0.4950
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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.5871 | 1.0 | 15 | 1.4971 | 0.3379 | 0.1821 |
| 1.4995 | 2.0 | 30 | 1.4588 | 0.3379 | 0.1707 |
| 1.464 | 3.0 | 45 | 1.4251 | 0.3655 | 0.2870 |
| 1.4105 | 4.0 | 60 | 1.4027 | 0.3793 | 0.2899 |
| 1.4269 | 5.0 | 75 | 1.3798 | 0.3793 | 0.2899 |
| 1.3835 | 6.0 | 90 | 1.3425 | 0.3724 | 0.3087 |
| 1.3885 | 7.0 | 105 | 1.3041 | 0.4069 | 0.3515 |
| 1.3286 | 8.0 | 120 | 1.3004 | 0.4621 | 0.4450 |
| 1.3572 | 9.0 | 135 | 1.2621 | 0.4345 | 0.3903 |
| 1.3176 | 10.0 | 150 | 1.2033 | 0.4552 | 0.4250 |
| 1.2509 | 11.0 | 165 | 1.1942 | 0.5034 | 0.4755 |
| 1.2781 | 12.0 | 180 | 1.1689 | 0.4828 | 0.4651 |
| 1.2156 | 13.0 | 195 | 1.1438 | 0.5034 | 0.4837 |
| 1.1518 | 14.0 | 210 | 1.1187 | 0.5034 | 0.4844 |
| 1.161 | 15.0 | 225 | 1.1013 | 0.5034 | 0.4858 |
| 1.1377 | 16.0 | 240 | 1.0882 | 0.5034 | 0.4796 |
| 1.1634 | 17.0 | 255 | 1.0692 | 0.5034 | 0.4860 |
| 1.0666 | 18.0 | 270 | 1.0591 | 0.5034 | 0.4772 |
| 1.1358 | 19.0 | 285 | 1.0455 | 0.5034 | 0.4736 |
| 1.1118 | 20.0 | 300 | 1.0313 | 0.5034 | 0.4872 |
| 1.0367 | 21.0 | 315 | 1.0228 | 0.5034 | 0.4853 |
| 1.0781 | 22.0 | 330 | 1.0106 | 0.5034 | 0.4857 |
| 1.0346 | 23.0 | 345 | 1.0034 | 0.5034 | 0.4935 |
| 1.1015 | 24.0 | 360 | 1.0032 | 0.5034 | 0.4806 |
| 1.0147 | 25.0 | 375 | 0.9911 | 0.5103 | 0.4903 |
| 1.0144 | 26.0 | 390 | 0.9856 | 0.5103 | 0.4972 |
| 1.022 | 27.0 | 405 | 0.9835 | 0.5103 | 0.4982 |
| 1.0218 | 28.0 | 420 | 0.9821 | 0.5103 | 0.4955 |
| 1.0173 | 29.0 | 435 | 0.9811 | 0.5103 | 0.4950 |
| 1.0241 | 30.0 | 450 | 0.9814 | 0.5103 | 0.4950 |
Framework versions
- Transformers 4.24.0
- Pytorch 2.0.0
- Datasets 2.10.1
- Tokenizers 0.11.0
