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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-01_txt_vis_concat_enc_5_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: 1.7881
- Accuracy: 0.7275
- Exit 0 Accuracy: 0.09
- Exit 1 Accuracy: 0.705
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 | 2.7022 | 0.1225 | 0.0525 | 0.1 |
No log | 1.98 | 33 | 2.5401 | 0.2375 | 0.0425 | 0.1775 |
No log | 3.0 | 50 | 2.3562 | 0.32 | 0.06 | 0.3125 |
No log | 3.96 | 66 | 2.0733 | 0.41 | 0.0675 | 0.3375 |
No log | 4.98 | 83 | 1.8281 | 0.53 | 0.0675 | 0.4325 |
No log | 6.0 | 100 | 1.5814 | 0.605 | 0.07 | 0.495 |
No log | 6.96 | 116 | 1.4011 | 0.655 | 0.065 | 0.525 |
No log | 7.98 | 133 | 1.2755 | 0.6925 | 0.0725 | 0.57 |
No log | 9.0 | 150 | 1.1738 | 0.71 | 0.065 | 0.5975 |
No log | 9.96 | 166 | 1.1008 | 0.7025 | 0.0675 | 0.5775 |
No log | 10.98 | 183 | 1.1162 | 0.7025 | 0.0725 | 0.6275 |
No log | 12.0 | 200 | 1.1360 | 0.6725 | 0.0725 | 0.6175 |
No log | 12.96 | 216 | 1.0388 | 0.7325 | 0.0675 | 0.6475 |
No log | 13.98 | 233 | 1.1008 | 0.705 | 0.0675 | 0.665 |
No log | 15.0 | 250 | 1.1237 | 0.7125 | 0.0725 | 0.6575 |
No log | 15.96 | 266 | 1.1345 | 0.7125 | 0.065 | 0.67 |
No log | 16.98 | 283 | 1.1696 | 0.7125 | 0.075 | 0.6575 |
No log | 18.0 | 300 | 1.2075 | 0.705 | 0.0775 | 0.655 |
No log | 18.96 | 316 | 1.3137 | 0.705 | 0.0775 | 0.665 |
No log | 19.98 | 333 | 1.3152 | 0.7 | 0.075 | 0.685 |
No log | 21.0 | 350 | 1.3460 | 0.7 | 0.08 | 0.6725 |
No log | 21.96 | 366 | 1.3561 | 0.7175 | 0.0825 | 0.6825 |
No log | 22.98 | 383 | 1.4231 | 0.7075 | 0.085 | 0.685 |
No log | 24.0 | 400 | 1.4084 | 0.72 | 0.0925 | 0.695 |
No log | 24.96 | 416 | 1.4287 | 0.72 | 0.0875 | 0.705 |
No log | 25.98 | 433 | 1.4479 | 0.7175 | 0.085 | 0.6925 |
No log | 27.0 | 450 | 1.5538 | 0.715 | 0.085 | 0.6975 |
No log | 27.96 | 466 | 1.5187 | 0.72 | 0.085 | 0.69 |
No log | 28.98 | 483 | 1.5472 | 0.71 | 0.0875 | 0.6775 |
1.395 | 30.0 | 500 | 1.6103 | 0.705 | 0.0875 | 0.6875 |
1.395 | 30.96 | 516 | 1.6125 | 0.715 | 0.085 | 0.715 |
1.395 | 31.98 | 533 | 1.5962 | 0.7225 | 0.085 | 0.7025 |
1.395 | 33.0 | 550 | 1.6054 | 0.7225 | 0.0875 | 0.695 |
1.395 | 33.96 | 566 | 1.5790 | 0.72 | 0.0875 | 0.6975 |
1.395 | 34.98 | 583 | 1.5978 | 0.72 | 0.0875 | 0.71 |
1.395 | 36.0 | 600 | 1.6560 | 0.7125 | 0.09 | 0.7025 |
1.395 | 36.96 | 616 | 1.6633 | 0.7175 | 0.09 | 0.6975 |
1.395 | 37.98 | 633 | 1.6619 | 0.72 | 0.0875 | 0.6925 |
1.395 | 39.0 | 650 | 1.6841 | 0.72 | 0.09 | 0.6975 |
1.395 | 39.96 | 666 | 1.7132 | 0.7175 | 0.09 | 0.71 |
1.395 | 40.98 | 683 | 1.7284 | 0.7175 | 0.09 | 0.7025 |
1.395 | 42.0 | 700 | 1.7035 | 0.7275 | 0.0875 | 0.7025 |
1.395 | 42.96 | 716 | 1.7357 | 0.7225 | 0.09 | 0.71 |
1.395 | 43.98 | 733 | 1.7345 | 0.725 | 0.09 | 0.705 |
1.395 | 45.0 | 750 | 1.7187 | 0.7275 | 0.09 | 0.705 |
1.395 | 45.96 | 766 | 1.7534 | 0.7225 | 0.0925 | 0.7025 |
1.395 | 46.98 | 783 | 1.7550 | 0.7275 | 0.09 | 0.695 |
1.395 | 48.0 | 800 | 1.7578 | 0.73 | 0.09 | 0.7125 |
1.395 | 48.96 | 816 | 1.7672 | 0.73 | 0.09 | 0.7025 |
1.395 | 49.98 | 833 | 1.7894 | 0.725 | 0.09 | 0.69 |
1.395 | 51.0 | 850 | 1.7910 | 0.725 | 0.09 | 0.7075 |
1.395 | 51.96 | 866 | 1.7902 | 0.7225 | 0.09 | 0.705 |
1.395 | 52.98 | 883 | 1.7817 | 0.725 | 0.09 | 0.7025 |
1.395 | 54.0 | 900 | 1.7853 | 0.7275 | 0.09 | 0.695 |
1.395 | 54.96 | 916 | 1.7874 | 0.7275 | 0.09 | 0.7 |
1.395 | 55.98 | 933 | 1.7896 | 0.7275 | 0.09 | 0.705 |
1.395 | 57.0 | 950 | 1.7882 | 0.7275 | 0.09 | 0.705 |
1.395 | 57.6 | 960 | 1.7881 | 0.7275 | 0.09 | 0.705 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3