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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-29_lte_test
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: 1.5042
- Accuracy: 0.75
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 |
|---|---|---|---|---|
| No log | 0.96 | 16 | 2.6827 | 0.1375 |
| No log | 1.98 | 33 | 2.5024 | 0.24 |
| No log | 3.0 | 50 | 2.3456 | 0.315 |
| No log | 3.96 | 66 | 2.1650 | 0.3625 |
| No log | 4.98 | 83 | 1.9151 | 0.475 |
| No log | 6.0 | 100 | 1.6499 | 0.6025 |
| No log | 6.96 | 116 | 1.4450 | 0.6425 |
| No log | 7.98 | 133 | 1.2701 | 0.6825 |
| No log | 9.0 | 150 | 1.1825 | 0.6725 |
| No log | 9.96 | 166 | 1.0343 | 0.7275 |
| No log | 10.98 | 183 | 1.0277 | 0.72 |
| No log | 12.0 | 200 | 0.9242 | 0.7625 |
| No log | 12.96 | 216 | 0.9228 | 0.745 |
| No log | 13.98 | 233 | 1.0066 | 0.7125 |
| No log | 15.0 | 250 | 0.9636 | 0.75 |
| No log | 15.96 | 266 | 0.9258 | 0.7475 |
| No log | 16.98 | 283 | 1.0153 | 0.745 |
| No log | 18.0 | 300 | 1.0909 | 0.7375 |
| No log | 18.96 | 316 | 1.1108 | 0.735 |
| No log | 19.98 | 333 | 1.0873 | 0.74 |
| No log | 21.0 | 350 | 1.0968 | 0.75 |
| No log | 21.96 | 366 | 1.1242 | 0.7625 |
| No log | 22.98 | 383 | 1.1795 | 0.7575 |
| No log | 24.0 | 400 | 1.1529 | 0.7625 |
| No log | 24.96 | 416 | 1.1900 | 0.76 |
| No log | 25.98 | 433 | 1.2364 | 0.75 |
| No log | 27.0 | 450 | 1.2554 | 0.7675 |
| No log | 27.96 | 466 | 1.2810 | 0.75 |
| No log | 28.98 | 483 | 1.3241 | 0.755 |
| 5.5136 | 30.0 | 500 | 1.3343 | 0.7625 |
| 5.5136 | 30.96 | 516 | 1.3430 | 0.7575 |
| 5.5136 | 31.98 | 533 | 1.3808 | 0.7525 |
| 5.5136 | 33.0 | 550 | 1.3886 | 0.7575 |
| 5.5136 | 33.96 | 566 | 1.3628 | 0.7625 |
| 5.5136 | 34.98 | 583 | 1.3966 | 0.745 |
| 5.5136 | 36.0 | 600 | 1.3708 | 0.7625 |
| 5.5136 | 36.96 | 616 | 1.4044 | 0.755 |
| 5.5136 | 37.98 | 633 | 1.4421 | 0.755 |
| 5.5136 | 39.0 | 650 | 1.4101 | 0.7575 |
| 5.5136 | 39.96 | 666 | 1.4206 | 0.755 |
| 5.5136 | 40.98 | 683 | 1.4098 | 0.7725 |
| 5.5136 | 42.0 | 700 | 1.4874 | 0.745 |
| 5.5136 | 42.96 | 716 | 1.5017 | 0.75 |
| 5.5136 | 43.98 | 733 | 1.4326 | 0.77 |
| 5.5136 | 45.0 | 750 | 1.4896 | 0.7575 |
| 5.5136 | 45.96 | 766 | 1.4124 | 0.7725 |
| 5.5136 | 46.98 | 783 | 1.4505 | 0.765 |
| 5.5136 | 48.0 | 800 | 1.4823 | 0.755 |
| 5.5136 | 48.96 | 816 | 1.4516 | 0.765 |
| 5.5136 | 49.98 | 833 | 1.4879 | 0.7575 |
| 5.5136 | 51.0 | 850 | 1.4876 | 0.755 |
| 5.5136 | 51.96 | 866 | 1.4850 | 0.755 |
| 5.5136 | 52.98 | 883 | 1.5151 | 0.7575 |
| 5.5136 | 54.0 | 900 | 1.5031 | 0.76 |
| 5.5136 | 54.96 | 916 | 1.4955 | 0.7575 |
| 5.5136 | 55.98 | 933 | 1.5084 | 0.75 |
| 5.5136 | 57.0 | 950 | 1.5053 | 0.75 |
| 5.5136 | 57.6 | 960 | 1.5042 | 0.75 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
