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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-30_txt_vis_concat_enc_2_ramp
This model is a fine-tuned version of microsoft/layoutlmv3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2478
- Accuracy: 0.795
- Exit 0 Accuracy: 0.085
- Exit 1 Accuracy: 0.57
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: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 24
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy |
|---|---|---|---|---|---|---|
| No log | 0.96 | 16 | 2.6891 | 0.1275 | 0.0525 | 0.0625 |
| No log | 1.98 | 33 | 2.5473 | 0.2425 | 0.04 | 0.0625 |
| No log | 3.0 | 50 | 2.3563 | 0.32 | 0.0475 | 0.0625 |
| No log | 3.96 | 66 | 2.1010 | 0.405 | 0.05 | 0.0625 |
| No log | 4.98 | 83 | 1.8280 | 0.515 | 0.05 | 0.0625 |
| No log | 6.0 | 100 | 1.6080 | 0.5925 | 0.05 | 0.0625 |
| No log | 6.96 | 116 | 1.3814 | 0.66 | 0.0525 | 0.0625 |
| No log | 7.98 | 133 | 1.2221 | 0.715 | 0.0525 | 0.0625 |
| No log | 9.0 | 150 | 1.1049 | 0.7375 | 0.06 | 0.0625 |
| No log | 9.96 | 166 | 1.0433 | 0.71 | 0.0575 | 0.0625 |
| No log | 10.98 | 183 | 0.9452 | 0.7625 | 0.0575 | 0.0625 |
| No log | 12.0 | 200 | 0.9152 | 0.7575 | 0.0575 | 0.0625 |
| No log | 12.96 | 216 | 0.9473 | 0.7575 | 0.065 | 0.0625 |
| No log | 13.98 | 233 | 0.9487 | 0.7525 | 0.065 | 0.0625 |
| No log | 15.0 | 250 | 0.9706 | 0.7625 | 0.065 | 0.0625 |
| No log | 15.96 | 266 | 0.9101 | 0.7925 | 0.0775 | 0.0625 |
| No log | 16.98 | 283 | 0.9571 | 0.7725 | 0.065 | 0.0625 |
| No log | 18.0 | 300 | 1.0558 | 0.76 | 0.0675 | 0.0625 |
| No log | 18.96 | 316 | 0.9547 | 0.77 | 0.0675 | 0.1075 |
| No log | 19.98 | 333 | 1.0204 | 0.7575 | 0.07 | 0.18 |
| No log | 21.0 | 350 | 1.1142 | 0.75 | 0.0675 | 0.25 |
| No log | 21.96 | 366 | 1.1336 | 0.75 | 0.0675 | 0.3425 |
| No log | 22.98 | 383 | 1.0917 | 0.76 | 0.07 | 0.3825 |
| No log | 24.0 | 400 | 1.1059 | 0.765 | 0.075 | 0.4325 |
| No log | 24.96 | 416 | 1.1171 | 0.775 | 0.075 | 0.4025 |
| No log | 25.98 | 433 | 1.0902 | 0.78 | 0.08 | 0.44 |
| No log | 27.0 | 450 | 1.1270 | 0.785 | 0.08 | 0.465 |
| No log | 27.96 | 466 | 1.1483 | 0.7925 | 0.0775 | 0.465 |
| No log | 28.98 | 483 | 1.1501 | 0.7875 | 0.0825 | 0.4875 |
| 1.6846 | 30.0 | 500 | 1.2854 | 0.7575 | 0.075 | 0.4925 |
| 1.6846 | 30.96 | 516 | 1.1910 | 0.7775 | 0.0725 | 0.5 |
| 1.6846 | 31.98 | 533 | 1.2389 | 0.77 | 0.0725 | 0.51 |
| 1.6846 | 33.0 | 550 | 1.2157 | 0.7775 | 0.0775 | 0.52 |
| 1.6846 | 33.96 | 566 | 1.2510 | 0.7675 | 0.075 | 0.52 |
| 1.6846 | 34.98 | 583 | 1.2536 | 0.7775 | 0.075 | 0.5225 |
| 1.6846 | 36.0 | 600 | 1.2163 | 0.7825 | 0.0825 | 0.5125 |
| 1.6846 | 36.96 | 616 | 1.1992 | 0.78 | 0.08 | 0.515 |
| 1.6846 | 37.98 | 633 | 1.2291 | 0.7775 | 0.0775 | 0.535 |
| 1.6846 | 39.0 | 650 | 1.1773 | 0.7925 | 0.08 | 0.5425 |
| 1.6846 | 39.96 | 666 | 1.1908 | 0.79 | 0.08 | 0.5375 |
| 1.6846 | 40.98 | 683 | 1.2103 | 0.7875 | 0.0825 | 0.5375 |
| 1.6846 | 42.0 | 700 | 1.2058 | 0.795 | 0.085 | 0.545 |
| 1.6846 | 42.96 | 716 | 1.2105 | 0.7925 | 0.085 | 0.55 |
| 1.6846 | 43.98 | 733 | 1.2077 | 0.8025 | 0.0825 | 0.5425 |
| 1.6846 | 45.0 | 750 | 1.2358 | 0.79 | 0.0825 | 0.5375 |
| 1.6846 | 45.96 | 766 | 1.2305 | 0.7975 | 0.08 | 0.55 |
| 1.6846 | 46.98 | 783 | 1.2409 | 0.79 | 0.08 | 0.5525 |
| 1.6846 | 48.0 | 800 | 1.2810 | 0.785 | 0.08 | 0.55 |
| 1.6846 | 48.96 | 816 | 1.2593 | 0.7925 | 0.0825 | 0.5575 |
| 1.6846 | 49.98 | 833 | 1.2397 | 0.7975 | 0.0825 | 0.5575 |
| 1.6846 | 51.0 | 850 | 1.2456 | 0.795 | 0.0825 | 0.56 |
| 1.6846 | 51.96 | 866 | 1.2508 | 0.7975 | 0.0825 | 0.565 |
| 1.6846 | 52.98 | 883 | 1.2577 | 0.7975 | 0.085 | 0.5675 |
| 1.6846 | 54.0 | 900 | 1.2537 | 0.7975 | 0.085 | 0.56 |
| 1.6846 | 54.96 | 916 | 1.2552 | 0.7975 | 0.085 | 0.57 |
| 1.6846 | 55.98 | 933 | 1.2520 | 0.795 | 0.085 | 0.5675 |
| 1.6846 | 57.0 | 950 | 1.2478 | 0.795 | 0.085 | 0.57 |
| 1.6846 | 57.6 | 960 | 1.2478 | 0.795 | 0.085 | 0.57 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
