vit-base_rvl_cdip-N1K_AURC_4

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: 0.2768
  • Accuracy: 0.8738
  • Brier Loss: 0.2167
  • Nll: 0.9821
  • F1 Micro: 0.8738
  • F1 Macro: 0.8749
  • Ece: 0.0970
  • Aurc: 0.0292

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: 4
  • eval_batch_size: 4
  • 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
0.1764 1.0 4000 0.3808 0.8217 0.2750 1.2675 0.8217 0.8194 0.1016 0.0461
0.1131 2.0 8000 0.3321 0.8413 0.2583 1.3120 0.8413 0.8421 0.0949 0.0418
0.113 3.0 12000 0.3781 0.8207 0.2910 1.4889 0.8207 0.8213 0.1162 0.0496
0.0814 4.0 16000 0.4793 0.8157 0.3036 1.4208 0.8157 0.8151 0.1302 0.0552
0.0542 5.0 20000 0.2914 0.8658 0.2279 1.1541 0.8658 0.8657 0.0955 0.0320
0.0238 6.0 24000 0.3059 0.8568 0.2401 1.1686 0.8568 0.8581 0.1012 0.0354
0.0197 7.0 28000 0.3077 0.8545 0.2390 1.1659 0.8545 0.8553 0.1059 0.0354
0.0116 8.0 32000 0.3169 0.8705 0.2172 1.0323 0.8705 0.8704 0.0918 0.0314
0.0054 9.0 36000 0.2850 0.8738 0.2199 1.0171 0.8738 0.8747 0.0960 0.0302
0.0128 10.0 40000 0.2768 0.8738 0.2167 0.9821 0.8738 0.8749 0.0970 0.0292

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.2.0.dev20231002
  • Datasets 2.7.1
  • Tokenizers 0.13.3
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