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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-02_txt_vis_concat_enc_7_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: 1.3516
- Accuracy: 0.7825
- Exit 0 Accuracy: 0.0825
- Exit 1 Accuracy: 0.7525
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 | 2.6991 | 0.1275 | 0.0475 | 0.0625 |
No log | 1.98 | 33 | 2.5453 | 0.2225 | 0.0375 | 0.0775 |
No log | 3.0 | 50 | 2.3689 | 0.2975 | 0.045 | 0.165 |
No log | 3.96 | 66 | 2.1411 | 0.41 | 0.0525 | 0.4175 |
No log | 4.98 | 83 | 1.8781 | 0.5325 | 0.055 | 0.5175 |
No log | 6.0 | 100 | 1.6031 | 0.64 | 0.065 | 0.5725 |
No log | 6.96 | 116 | 1.4385 | 0.655 | 0.065 | 0.595 |
No log | 7.98 | 133 | 1.2796 | 0.6875 | 0.065 | 0.635 |
No log | 9.0 | 150 | 1.1812 | 0.7225 | 0.065 | 0.6575 |
No log | 9.96 | 166 | 1.0855 | 0.735 | 0.0675 | 0.68 |
No log | 10.98 | 183 | 1.0009 | 0.755 | 0.0675 | 0.6925 |
No log | 12.0 | 200 | 1.0138 | 0.73 | 0.0675 | 0.685 |
No log | 12.96 | 216 | 0.9252 | 0.7525 | 0.0675 | 0.68 |
No log | 13.98 | 233 | 0.9161 | 0.7525 | 0.07 | 0.69 |
No log | 15.0 | 250 | 1.0396 | 0.73 | 0.065 | 0.6925 |
No log | 15.96 | 266 | 1.0077 | 0.7425 | 0.07 | 0.6925 |
No log | 16.98 | 283 | 0.9578 | 0.7625 | 0.0675 | 0.71 |
No log | 18.0 | 300 | 0.9925 | 0.7625 | 0.07 | 0.72 |
No log | 18.96 | 316 | 1.0645 | 0.75 | 0.075 | 0.725 |
No log | 19.98 | 333 | 1.0794 | 0.7525 | 0.07 | 0.7325 |
No log | 21.0 | 350 | 1.0704 | 0.76 | 0.07 | 0.7325 |
No log | 21.96 | 366 | 1.0809 | 0.7675 | 0.075 | 0.7425 |
No log | 22.98 | 383 | 1.1286 | 0.7675 | 0.0725 | 0.745 |
No log | 24.0 | 400 | 1.0816 | 0.78 | 0.08 | 0.7475 |
No log | 24.96 | 416 | 1.1370 | 0.765 | 0.0825 | 0.7525 |
No log | 25.98 | 433 | 1.2095 | 0.7625 | 0.0825 | 0.7475 |
No log | 27.0 | 450 | 1.2139 | 0.77 | 0.08 | 0.755 |
No log | 27.96 | 466 | 1.2498 | 0.77 | 0.08 | 0.7475 |
No log | 28.98 | 483 | 1.2696 | 0.7575 | 0.085 | 0.7425 |
1.3298 | 30.0 | 500 | 1.2227 | 0.7825 | 0.09 | 0.7575 |
1.3298 | 30.96 | 516 | 1.2820 | 0.77 | 0.0825 | 0.7525 |
1.3298 | 31.98 | 533 | 1.2411 | 0.7825 | 0.0825 | 0.7525 |
1.3298 | 33.0 | 550 | 1.2531 | 0.775 | 0.085 | 0.7425 |
1.3298 | 33.96 | 566 | 1.2552 | 0.77 | 0.085 | 0.7375 |
1.3298 | 34.98 | 583 | 1.2638 | 0.78 | 0.0825 | 0.7525 |
1.3298 | 36.0 | 600 | 1.2798 | 0.775 | 0.0775 | 0.7525 |
1.3298 | 36.96 | 616 | 1.2777 | 0.7875 | 0.0775 | 0.76 |
1.3298 | 37.98 | 633 | 1.2900 | 0.78 | 0.0775 | 0.7525 |
1.3298 | 39.0 | 650 | 1.3050 | 0.78 | 0.08 | 0.7575 |
1.3298 | 39.96 | 666 | 1.3243 | 0.7825 | 0.08 | 0.75 |
1.3298 | 40.98 | 683 | 1.3234 | 0.7775 | 0.0825 | 0.7525 |
1.3298 | 42.0 | 700 | 1.3262 | 0.78 | 0.0775 | 0.7525 |
1.3298 | 42.96 | 716 | 1.3246 | 0.7875 | 0.0825 | 0.755 |
1.3298 | 43.98 | 733 | 1.3223 | 0.7825 | 0.08 | 0.7525 |
1.3298 | 45.0 | 750 | 1.3297 | 0.7825 | 0.0775 | 0.7525 |
1.3298 | 45.96 | 766 | 1.3311 | 0.7875 | 0.0825 | 0.7475 |
1.3298 | 46.98 | 783 | 1.3421 | 0.7775 | 0.0825 | 0.7525 |
1.3298 | 48.0 | 800 | 1.3358 | 0.7825 | 0.08 | 0.7525 |
1.3298 | 48.96 | 816 | 1.3365 | 0.78 | 0.0825 | 0.7525 |
1.3298 | 49.98 | 833 | 1.3442 | 0.785 | 0.08 | 0.755 |
1.3298 | 51.0 | 850 | 1.3480 | 0.785 | 0.0825 | 0.755 |
1.3298 | 51.96 | 866 | 1.3450 | 0.785 | 0.0825 | 0.755 |
1.3298 | 52.98 | 883 | 1.3472 | 0.7825 | 0.0825 | 0.75 |
1.3298 | 54.0 | 900 | 1.3486 | 0.78 | 0.0825 | 0.75 |
1.3298 | 54.96 | 916 | 1.3438 | 0.7825 | 0.0825 | 0.75 |
1.3298 | 55.98 | 933 | 1.3507 | 0.7825 | 0.0825 | 0.75 |
1.3298 | 57.0 | 950 | 1.3515 | 0.7825 | 0.0825 | 0.7525 |
1.3298 | 57.6 | 960 | 1.3516 | 0.7825 | 0.0825 | 0.7525 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3