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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only
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: 1.3116
- Accuracy: 0.7775
- Exit 0 Accuracy: 0.065
- Exit 1 Accuracy: 0.09
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 | 2.6827 | 0.1275 | 0.0625 | 0.04 |
| No log | 1.98 | 33 | 2.5158 | 0.25 | 0.06 | 0.095 |
| No log | 3.0 | 50 | 2.3010 | 0.33 | 0.0625 | 0.095 |
| No log | 3.96 | 66 | 1.9997 | 0.435 | 0.055 | 0.0925 |
| No log | 4.98 | 83 | 1.7239 | 0.6 | 0.06 | 0.0925 |
| No log | 6.0 | 100 | 1.4812 | 0.6175 | 0.06 | 0.09 |
| No log | 6.96 | 116 | 1.2872 | 0.6875 | 0.0625 | 0.09 |
| No log | 7.98 | 133 | 1.1118 | 0.74 | 0.055 | 0.09 |
| No log | 9.0 | 150 | 1.0144 | 0.7425 | 0.0625 | 0.09 |
| No log | 9.96 | 166 | 0.9663 | 0.7475 | 0.0575 | 0.09 |
| No log | 10.98 | 183 | 0.9532 | 0.7475 | 0.0625 | 0.09 |
| No log | 12.0 | 200 | 0.9157 | 0.7525 | 0.06 | 0.09 |
| No log | 12.96 | 216 | 0.8894 | 0.77 | 0.06 | 0.09 |
| No log | 13.98 | 233 | 0.9460 | 0.75 | 0.0625 | 0.09 |
| No log | 15.0 | 250 | 1.0019 | 0.745 | 0.0625 | 0.09 |
| No log | 15.96 | 266 | 0.9059 | 0.77 | 0.0625 | 0.0875 |
| No log | 16.98 | 283 | 1.0664 | 0.7325 | 0.06 | 0.0875 |
| No log | 18.0 | 300 | 1.0637 | 0.74 | 0.065 | 0.0875 |
| No log | 18.96 | 316 | 1.0398 | 0.7725 | 0.09 | 0.085 |
| No log | 19.98 | 333 | 1.0745 | 0.775 | 0.06 | 0.0875 |
| No log | 21.0 | 350 | 1.0653 | 0.78 | 0.0625 | 0.0875 |
| No log | 21.96 | 366 | 1.0705 | 0.785 | 0.065 | 0.0875 |
| No log | 22.98 | 383 | 1.1014 | 0.78 | 0.0725 | 0.0875 |
| No log | 24.0 | 400 | 1.1335 | 0.78 | 0.0625 | 0.0875 |
| No log | 24.96 | 416 | 1.1510 | 0.775 | 0.0725 | 0.0875 |
| No log | 25.98 | 433 | 1.1528 | 0.7825 | 0.0675 | 0.0875 |
| No log | 27.0 | 450 | 1.1758 | 0.7825 | 0.0625 | 0.0875 |
| No log | 27.96 | 466 | 1.1836 | 0.785 | 0.07 | 0.0875 |
| No log | 28.98 | 483 | 1.1927 | 0.78 | 0.0675 | 0.0875 |
| 1.6955 | 30.0 | 500 | 1.2061 | 0.7825 | 0.0775 | 0.0875 |
| 1.6955 | 30.96 | 516 | 1.2128 | 0.7775 | 0.065 | 0.0875 |
| 1.6955 | 31.98 | 533 | 1.2172 | 0.7725 | 0.07 | 0.0875 |
| 1.6955 | 33.0 | 550 | 1.2237 | 0.775 | 0.075 | 0.0875 |
| 1.6955 | 33.96 | 566 | 1.2399 | 0.7775 | 0.0625 | 0.0875 |
| 1.6955 | 34.98 | 583 | 1.2590 | 0.78 | 0.065 | 0.0875 |
| 1.6955 | 36.0 | 600 | 1.2586 | 0.7825 | 0.065 | 0.0875 |
| 1.6955 | 36.96 | 616 | 1.2603 | 0.775 | 0.0675 | 0.0875 |
| 1.6955 | 37.98 | 633 | 1.2576 | 0.78 | 0.065 | 0.0875 |
| 1.6955 | 39.0 | 650 | 1.2698 | 0.7775 | 0.075 | 0.0875 |
| 1.6955 | 39.96 | 666 | 1.2775 | 0.7725 | 0.075 | 0.0875 |
| 1.6955 | 40.98 | 683 | 1.2769 | 0.7725 | 0.07 | 0.0875 |
| 1.6955 | 42.0 | 700 | 1.2769 | 0.7725 | 0.0625 | 0.0875 |
| 1.6955 | 42.96 | 716 | 1.2804 | 0.775 | 0.0675 | 0.0875 |
| 1.6955 | 43.98 | 733 | 1.2834 | 0.775 | 0.065 | 0.085 |
| 1.6955 | 45.0 | 750 | 1.2907 | 0.7775 | 0.0675 | 0.0875 |
| 1.6955 | 45.96 | 766 | 1.2968 | 0.7775 | 0.0675 | 0.0875 |
| 1.6955 | 46.98 | 783 | 1.2981 | 0.7775 | 0.065 | 0.0875 |
| 1.6955 | 48.0 | 800 | 1.3017 | 0.7775 | 0.065 | 0.0875 |
| 1.6955 | 48.96 | 816 | 1.3050 | 0.7775 | 0.0675 | 0.09 |
| 1.6955 | 49.98 | 833 | 1.3050 | 0.775 | 0.07 | 0.09 |
| 1.6955 | 51.0 | 850 | 1.3044 | 0.775 | 0.07 | 0.09 |
| 1.6955 | 51.96 | 866 | 1.3057 | 0.775 | 0.0675 | 0.09 |
| 1.6955 | 52.98 | 883 | 1.3072 | 0.7775 | 0.0675 | 0.09 |
| 1.6955 | 54.0 | 900 | 1.3101 | 0.7775 | 0.0675 | 0.09 |
| 1.6955 | 54.96 | 916 | 1.3119 | 0.7775 | 0.065 | 0.09 |
| 1.6955 | 55.98 | 933 | 1.3116 | 0.7775 | 0.065 | 0.09 |
| 1.6955 | 57.0 | 950 | 1.3115 | 0.7775 | 0.065 | 0.09 |
| 1.6955 | 57.6 | 960 | 1.3116 | 0.7775 | 0.065 | 0.09 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
