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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-02_txt_vis_concat_enc_9_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: 1.2989
- Accuracy: 0.7625
- Exit 0 Accuracy: 0.0775
- Exit 1 Accuracy: 0.755
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 | 2.6928 | 0.1225 | 0.0525 | 0.0625 |
No log | 1.98 | 33 | 2.5722 | 0.23 | 0.035 | 0.0625 |
No log | 3.0 | 50 | 2.3786 | 0.2925 | 0.0425 | 0.0625 |
No log | 3.96 | 66 | 2.1686 | 0.39 | 0.05 | 0.0625 |
No log | 4.98 | 83 | 1.9395 | 0.505 | 0.045 | 0.0625 |
No log | 6.0 | 100 | 1.6538 | 0.5875 | 0.0475 | 0.0625 |
No log | 6.96 | 116 | 1.4783 | 0.6425 | 0.0475 | 0.0625 |
No log | 7.98 | 133 | 1.2744 | 0.7175 | 0.0525 | 0.0625 |
No log | 9.0 | 150 | 1.1395 | 0.7325 | 0.06 | 0.0625 |
No log | 9.96 | 166 | 1.0234 | 0.7525 | 0.065 | 0.0625 |
No log | 10.98 | 183 | 0.9838 | 0.75 | 0.0675 | 0.0625 |
No log | 12.0 | 200 | 0.9310 | 0.7475 | 0.065 | 0.0625 |
No log | 12.96 | 216 | 0.9234 | 0.755 | 0.065 | 0.0625 |
No log | 13.98 | 233 | 0.9256 | 0.7675 | 0.065 | 0.0625 |
No log | 15.0 | 250 | 0.9318 | 0.7725 | 0.0675 | 0.0625 |
No log | 15.96 | 266 | 0.9192 | 0.7475 | 0.0675 | 0.0875 |
No log | 16.98 | 283 | 0.9302 | 0.7625 | 0.065 | 0.4775 |
No log | 18.0 | 300 | 0.9552 | 0.76 | 0.065 | 0.685 |
No log | 18.96 | 316 | 1.0063 | 0.775 | 0.065 | 0.715 |
No log | 19.98 | 333 | 1.0117 | 0.7675 | 0.065 | 0.7425 |
No log | 21.0 | 350 | 0.9867 | 0.775 | 0.065 | 0.77 |
No log | 21.96 | 366 | 1.0445 | 0.7725 | 0.0675 | 0.775 |
No log | 22.98 | 383 | 1.0835 | 0.765 | 0.07 | 0.7725 |
No log | 24.0 | 400 | 1.0637 | 0.775 | 0.0725 | 0.7725 |
No log | 24.96 | 416 | 1.1717 | 0.765 | 0.0725 | 0.75 |
No log | 25.98 | 433 | 1.0935 | 0.7675 | 0.0675 | 0.77 |
No log | 27.0 | 450 | 1.2155 | 0.7625 | 0.0675 | 0.7675 |
No log | 27.96 | 466 | 1.1269 | 0.7675 | 0.0675 | 0.765 |
No log | 28.98 | 483 | 1.1821 | 0.775 | 0.0675 | 0.755 |
1.4929 | 30.0 | 500 | 1.1562 | 0.7775 | 0.0725 | 0.7675 |
1.4929 | 30.96 | 516 | 1.1784 | 0.7625 | 0.07 | 0.7525 |
1.4929 | 31.98 | 533 | 1.1937 | 0.76 | 0.0725 | 0.7625 |
1.4929 | 33.0 | 550 | 1.2074 | 0.76 | 0.0775 | 0.7575 |
1.4929 | 33.96 | 566 | 1.2167 | 0.76 | 0.0775 | 0.7525 |
1.4929 | 34.98 | 583 | 1.2324 | 0.7575 | 0.0725 | 0.755 |
1.4929 | 36.0 | 600 | 1.2309 | 0.7525 | 0.075 | 0.755 |
1.4929 | 36.96 | 616 | 1.2377 | 0.76 | 0.0725 | 0.755 |
1.4929 | 37.98 | 633 | 1.2426 | 0.765 | 0.075 | 0.7525 |
1.4929 | 39.0 | 650 | 1.2471 | 0.76 | 0.075 | 0.7525 |
1.4929 | 39.96 | 666 | 1.2536 | 0.7625 | 0.075 | 0.7525 |
1.4929 | 40.98 | 683 | 1.2556 | 0.7575 | 0.0775 | 0.755 |
1.4929 | 42.0 | 700 | 1.2607 | 0.7625 | 0.0725 | 0.755 |
1.4929 | 42.96 | 716 | 1.2662 | 0.765 | 0.075 | 0.755 |
1.4929 | 43.98 | 733 | 1.2702 | 0.765 | 0.075 | 0.755 |
1.4929 | 45.0 | 750 | 1.2757 | 0.7675 | 0.075 | 0.7575 |
1.4929 | 45.96 | 766 | 1.2773 | 0.7675 | 0.0775 | 0.755 |
1.4929 | 46.98 | 783 | 1.2805 | 0.7675 | 0.0775 | 0.755 |
1.4929 | 48.0 | 800 | 1.2809 | 0.765 | 0.075 | 0.7525 |
1.4929 | 48.96 | 816 | 1.2841 | 0.76 | 0.0775 | 0.7525 |
1.4929 | 49.98 | 833 | 1.2902 | 0.76 | 0.075 | 0.7525 |
1.4929 | 51.0 | 850 | 1.2930 | 0.765 | 0.0775 | 0.755 |
1.4929 | 51.96 | 866 | 1.2963 | 0.7625 | 0.0775 | 0.7575 |
1.4929 | 52.98 | 883 | 1.2968 | 0.76 | 0.0775 | 0.7575 |
1.4929 | 54.0 | 900 | 1.2977 | 0.7625 | 0.0775 | 0.7575 |
1.4929 | 54.96 | 916 | 1.2978 | 0.765 | 0.0775 | 0.755 |
1.4929 | 55.98 | 933 | 1.2986 | 0.765 | 0.0775 | 0.755 |
1.4929 | 57.0 | 950 | 1.2988 | 0.765 | 0.0775 | 0.755 |
1.4929 | 57.6 | 960 | 1.2989 | 0.7625 | 0.0775 | 0.755 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3