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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_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: 1.1314
- Accuracy: 0.7925
- Exit 0 Accuracy: 0.075
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 |
|---|---|---|---|---|---|
| No log | 0.96 | 16 | 2.6835 | 0.1175 | 0.0775 |
| No log | 1.98 | 33 | 2.4924 | 0.2375 | 0.0675 |
| No log | 3.0 | 50 | 2.2916 | 0.34 | 0.07 |
| No log | 3.96 | 66 | 2.0277 | 0.475 | 0.07 |
| No log | 4.98 | 83 | 1.7026 | 0.5775 | 0.07 |
| No log | 6.0 | 100 | 1.4354 | 0.6625 | 0.075 |
| No log | 6.96 | 116 | 1.2357 | 0.6975 | 0.07 |
| No log | 7.98 | 133 | 1.0754 | 0.745 | 0.0725 |
| No log | 9.0 | 150 | 0.9760 | 0.77 | 0.07 |
| No log | 9.96 | 166 | 0.8967 | 0.765 | 0.07 |
| No log | 10.98 | 183 | 0.8561 | 0.7875 | 0.07 |
| No log | 12.0 | 200 | 0.8837 | 0.7575 | 0.07 |
| No log | 12.96 | 216 | 0.8885 | 0.7725 | 0.07 |
| No log | 13.98 | 233 | 0.7819 | 0.7725 | 0.0675 |
| No log | 15.0 | 250 | 0.9067 | 0.7725 | 0.07 |
| No log | 15.96 | 266 | 0.9086 | 0.775 | 0.075 |
| No log | 16.98 | 283 | 0.8444 | 0.795 | 0.07 |
| No log | 18.0 | 300 | 0.9359 | 0.7775 | 0.0725 |
| No log | 18.96 | 316 | 0.9696 | 0.7825 | 0.07 |
| No log | 19.98 | 333 | 0.9254 | 0.7825 | 0.075 |
| No log | 21.0 | 350 | 0.9879 | 0.7775 | 0.0675 |
| No log | 21.96 | 366 | 0.9894 | 0.79 | 0.075 |
| No log | 22.98 | 383 | 1.0087 | 0.785 | 0.07 |
| No log | 24.0 | 400 | 1.0101 | 0.785 | 0.07 |
| No log | 24.96 | 416 | 1.0188 | 0.7875 | 0.075 |
| No log | 25.98 | 433 | 1.0266 | 0.79 | 0.075 |
| No log | 27.0 | 450 | 1.0357 | 0.79 | 0.07 |
| No log | 27.96 | 466 | 1.0505 | 0.7875 | 0.0675 |
| No log | 28.98 | 483 | 1.0524 | 0.7825 | 0.07 |
| 1.6927 | 30.0 | 500 | 1.0656 | 0.785 | 0.075 |
| 1.6927 | 30.96 | 516 | 1.0642 | 0.785 | 0.0725 |
| 1.6927 | 31.98 | 533 | 1.0740 | 0.785 | 0.0725 |
| 1.6927 | 33.0 | 550 | 1.0830 | 0.785 | 0.075 |
| 1.6927 | 33.96 | 566 | 1.0860 | 0.785 | 0.0675 |
| 1.6927 | 34.98 | 583 | 1.0939 | 0.7875 | 0.0675 |
| 1.6927 | 36.0 | 600 | 1.0969 | 0.79 | 0.0675 |
| 1.6927 | 36.96 | 616 | 1.0966 | 0.7875 | 0.07 |
| 1.6927 | 37.98 | 633 | 1.1024 | 0.79 | 0.07 |
| 1.6927 | 39.0 | 650 | 1.1045 | 0.7875 | 0.075 |
| 1.6927 | 39.96 | 666 | 1.1050 | 0.7875 | 0.0725 |
| 1.6927 | 40.98 | 683 | 1.1085 | 0.7875 | 0.075 |
| 1.6927 | 42.0 | 700 | 1.1171 | 0.7875 | 0.075 |
| 1.6927 | 42.96 | 716 | 1.1193 | 0.7875 | 0.075 |
| 1.6927 | 43.98 | 733 | 1.1188 | 0.79 | 0.075 |
| 1.6927 | 45.0 | 750 | 1.1220 | 0.79 | 0.0725 |
| 1.6927 | 45.96 | 766 | 1.1270 | 0.79 | 0.0725 |
| 1.6927 | 46.98 | 783 | 1.1268 | 0.7875 | 0.075 |
| 1.6927 | 48.0 | 800 | 1.1264 | 0.79 | 0.075 |
| 1.6927 | 48.96 | 816 | 1.1267 | 0.795 | 0.075 |
| 1.6927 | 49.98 | 833 | 1.1273 | 0.79 | 0.0725 |
| 1.6927 | 51.0 | 850 | 1.1268 | 0.79 | 0.07 |
| 1.6927 | 51.96 | 866 | 1.1283 | 0.795 | 0.07 |
| 1.6927 | 52.98 | 883 | 1.1293 | 0.795 | 0.075 |
| 1.6927 | 54.0 | 900 | 1.1306 | 0.795 | 0.075 |
| 1.6927 | 54.96 | 916 | 1.1306 | 0.795 | 0.075 |
| 1.6927 | 55.98 | 933 | 1.1310 | 0.795 | 0.075 |
| 1.6927 | 57.0 | 950 | 1.1314 | 0.7925 | 0.075 |
| 1.6927 | 57.6 | 960 | 1.1314 | 0.7925 | 0.075 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
