<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
vit-base_rvl_cdip-N1K_aAURC_64
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: 0.4857
- Accuracy: 0.8892
- Brier Loss: 0.1843
- Nll: 0.9506
- F1 Micro: 0.8892
- F1 Macro: 0.8895
- Ece: 0.0837
- Aurc: 0.0193
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: 64
- eval_batch_size: 64
- 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 | 250 | 0.3824 | 0.888 | 0.1700 | 1.1756 | 0.888 | 0.8884 | 0.0548 | 0.0185 |
| 0.1403 | 2.0 | 500 | 0.3988 | 0.8925 | 0.1681 | 1.1230 | 0.8925 | 0.8936 | 0.0549 | 0.0199 |
| 0.1403 | 3.0 | 750 | 0.4099 | 0.8865 | 0.1756 | 1.0948 | 0.8865 | 0.8868 | 0.0672 | 0.0187 |
| 0.0442 | 4.0 | 1000 | 0.4297 | 0.8925 | 0.1747 | 1.0568 | 0.8925 | 0.8931 | 0.0685 | 0.0191 |
| 0.0442 | 5.0 | 1250 | 0.4467 | 0.8925 | 0.1775 | 1.0202 | 0.8925 | 0.8928 | 0.0734 | 0.0194 |
| 0.0119 | 6.0 | 1500 | 0.4612 | 0.8908 | 0.1808 | 0.9834 | 0.8907 | 0.8914 | 0.0772 | 0.0191 |
| 0.0119 | 7.0 | 1750 | 0.4762 | 0.8882 | 0.1845 | 0.9761 | 0.8882 | 0.8885 | 0.0827 | 0.0197 |
| 0.0062 | 8.0 | 2000 | 0.4763 | 0.892 | 0.1824 | 0.9652 | 0.892 | 0.8923 | 0.0789 | 0.0192 |
| 0.0062 | 9.0 | 2250 | 0.4854 | 0.8892 | 0.1844 | 0.9509 | 0.8892 | 0.8895 | 0.0834 | 0.0193 |
| 0.0051 | 10.0 | 2500 | 0.4857 | 0.8892 | 0.1843 | 0.9506 | 0.8892 | 0.8895 | 0.0837 | 0.0193 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
