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vit-base_rvl_cdip-N1K_AURC_256
This model is a fine-tuned version of jordyvl/vit-base_rvl-cdip on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2459
- Accuracy: 0.8968
- Brier Loss: 0.1720
- Nll: 0.9246
- F1 Micro: 0.8968
- F1 Macro: 0.8967
- Ece: 0.0709
- Aurc: 0.0191
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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|---|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 63 | 0.1138 | 0.8922 | 0.1604 | 1.1695 | 0.8922 | 0.8926 | 0.0478 | 0.0170 |
| No log | 2.0 | 126 | 0.1565 | 0.8952 | 0.1607 | 1.1000 | 0.8952 | 0.8952 | 0.0532 | 0.0176 |
| No log | 3.0 | 189 | 0.1722 | 0.8972 | 0.1620 | 1.0250 | 0.8972 | 0.8973 | 0.0584 | 0.0175 |
| No log | 4.0 | 252 | 0.2006 | 0.897 | 0.1642 | 0.9921 | 0.897 | 0.8969 | 0.0615 | 0.0181 |
| No log | 5.0 | 315 | 0.2142 | 0.8988 | 0.1668 | 0.9670 | 0.8988 | 0.8986 | 0.0640 | 0.0183 |
| No log | 6.0 | 378 | 0.2207 | 0.8975 | 0.1688 | 0.9482 | 0.8975 | 0.8975 | 0.0674 | 0.0186 |
| No log | 7.0 | 441 | 0.2310 | 0.897 | 0.1700 | 0.9397 | 0.897 | 0.8969 | 0.0697 | 0.0188 |
| 0.008 | 8.0 | 504 | 0.2401 | 0.8968 | 0.1714 | 0.9268 | 0.8968 | 0.8966 | 0.0705 | 0.0190 |
| 0.008 | 9.0 | 567 | 0.2441 | 0.8975 | 0.1719 | 0.9262 | 0.8975 | 0.8974 | 0.0709 | 0.0191 |
| 0.008 | 10.0 | 630 | 0.2459 | 0.8968 | 0.1720 | 0.9246 | 0.8968 | 0.8967 | 0.0709 | 0.0191 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
