2024-01-31_one_stage_subgraphs_weighted_txt_vis_conc_all_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: 1.3452
- Accuracy: 0.7625
- Exit 0 Accuracy: 0.29
- Exit 1 Accuracy: 0.4625
- Exit 2 Accuracy: 0.5225
- Exit 3 Accuracy: 0.585
- Exit 4 Accuracy: 0.625
- Exit 5 Accuracy: 0.695
- Exit 6 Accuracy: 0.71
- Exit 7 Accuracy: 0.73
- Exit 8 Accuracy: 0.73
- Exit 9 Accuracy: 0.7575
- Exit 10 Accuracy: 0.76
- Exit 11 Accuracy: 0.7575
- Exit 12 Accuracy: 0.7625
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 | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy | Exit 5 Accuracy | Exit 6 Accuracy | Exit 7 Accuracy | Exit 8 Accuracy | Exit 9 Accuracy | Exit 10 Accuracy | Exit 11 Accuracy | Exit 12 Accuracy |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
No log | 0.96 | 16 | 2.6725 | 0.165 | 0.0975 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0925 |
No log | 1.98 | 33 | 2.4536 | 0.2625 | 0.125 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0675 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | 0.1425 |
No log | 3.0 | 50 | 2.1927 | 0.3825 | 0.14 | 0.1025 | 0.0625 | 0.0625 | 0.0625 | 0.125 | 0.0975 | 0.0625 | 0.0625 | 0.0625 | 0.065 | 0.0625 | 0.2325 |
No log | 3.96 | 66 | 1.9488 | 0.45 | 0.1575 | 0.1025 | 0.0625 | 0.0625 | 0.0625 | 0.1275 | 0.13 | 0.0625 | 0.0625 | 0.0625 | 0.0575 | 0.0625 | 0.335 |
No log | 4.98 | 83 | 1.6922 | 0.54 | 0.16 | 0.0975 | 0.0625 | 0.0625 | 0.0775 | 0.24 | 0.1975 | 0.12 | 0.0625 | 0.0625 | 0.065 | 0.0625 | 0.4175 |
No log | 6.0 | 100 | 1.4713 | 0.615 | 0.16 | 0.115 | 0.0625 | 0.0625 | 0.1375 | 0.3575 | 0.315 | 0.2125 | 0.1075 | 0.0625 | 0.075 | 0.0625 | 0.4875 |
No log | 6.96 | 116 | 1.2943 | 0.6625 | 0.165 | 0.1175 | 0.0625 | 0.0675 | 0.1525 | 0.3425 | 0.3675 | 0.38 | 0.105 | 0.0625 | 0.13 | 0.095 | 0.565 |
No log | 7.98 | 133 | 1.1118 | 0.7225 | 0.175 | 0.1275 | 0.0625 | 0.0675 | 0.1425 | 0.36 | 0.3925 | 0.4575 | 0.1075 | 0.0625 | 0.16 | 0.1925 | 0.6375 |
No log | 9.0 | 150 | 1.0188 | 0.74 | 0.1725 | 0.1225 | 0.0625 | 0.1025 | 0.185 | 0.4075 | 0.4475 | 0.535 | 0.0925 | 0.0625 | 0.1025 | 0.2925 | 0.6825 |
No log | 9.96 | 166 | 0.9689 | 0.7375 | 0.17 | 0.125 | 0.0625 | 0.1375 | 0.165 | 0.4675 | 0.475 | 0.5825 | 0.0975 | 0.115 | 0.2075 | 0.35 | 0.6975 |
No log | 10.98 | 183 | 0.8788 | 0.77 | 0.1775 | 0.16 | 0.0625 | 0.1125 | 0.2025 | 0.4975 | 0.49 | 0.5975 | 0.1225 | 0.1275 | 0.2525 | 0.4025 | 0.75 |
No log | 12.0 | 200 | 0.9443 | 0.7125 | 0.1725 | 0.165 | 0.0625 | 0.14 | 0.195 | 0.515 | 0.505 | 0.59 | 0.1525 | 0.17 | 0.2725 | 0.4 | 0.705 |
No log | 12.96 | 216 | 0.8964 | 0.7375 | 0.185 | 0.2025 | 0.0675 | 0.2225 | 0.21 | 0.505 | 0.51 | 0.6025 | 0.215 | 0.1325 | 0.405 | 0.4475 | 0.7375 |
No log | 13.98 | 233 | 0.9871 | 0.73 | 0.19 | 0.23 | 0.065 | 0.21 | 0.23 | 0.4625 | 0.505 | 0.6175 | 0.31 | 0.14 | 0.46 | 0.57 | 0.72 |
No log | 15.0 | 250 | 1.0090 | 0.73 | 0.2175 | 0.265 | 0.115 | 0.225 | 0.2075 | 0.47 | 0.525 | 0.64 | 0.4625 | 0.3525 | 0.485 | 0.63 | 0.7375 |
No log | 15.96 | 266 | 0.9539 | 0.76 | 0.2175 | 0.2525 | 0.13 | 0.185 | 0.35 | 0.3975 | 0.555 | 0.6375 | 0.46 | 0.435 | 0.46 | 0.6875 | 0.7575 |
No log | 16.98 | 283 | 0.9204 | 0.7625 | 0.2225 | 0.295 | 0.1375 | 0.27 | 0.3925 | 0.5075 | 0.595 | 0.65 | 0.5625 | 0.475 | 0.5275 | 0.6825 | 0.765 |
No log | 18.0 | 300 | 0.9639 | 0.77 | 0.2375 | 0.2675 | 0.17 | 0.2625 | 0.455 | 0.515 | 0.6175 | 0.685 | 0.5775 | 0.6525 | 0.63 | 0.7125 | 0.765 |
No log | 18.96 | 316 | 0.9644 | 0.7725 | 0.25 | 0.3175 | 0.175 | 0.3025 | 0.4975 | 0.5375 | 0.65 | 0.695 | 0.65 | 0.66 | 0.6225 | 0.745 | 0.7675 |
No log | 19.98 | 333 | 0.9984 | 0.7675 | 0.25 | 0.3275 | 0.1975 | 0.295 | 0.5375 | 0.58 | 0.6775 | 0.7 | 0.6825 | 0.65 | 0.6875 | 0.7425 | 0.7625 |
No log | 21.0 | 350 | 0.9756 | 0.775 | 0.24 | 0.3025 | 0.22 | 0.2825 | 0.52 | 0.59 | 0.6725 | 0.7125 | 0.6875 | 0.6775 | 0.6975 | 0.7525 | 0.7825 |
No log | 21.96 | 366 | 1.0060 | 0.7675 | 0.235 | 0.2525 | 0.255 | 0.2975 | 0.545 | 0.61 | 0.675 | 0.7125 | 0.6925 | 0.7 | 0.695 | 0.75 | 0.7675 |
No log | 22.98 | 383 | 1.0393 | 0.7675 | 0.245 | 0.265 | 0.2175 | 0.3125 | 0.53 | 0.62 | 0.69 | 0.7125 | 0.7275 | 0.7125 | 0.7 | 0.76 | 0.7675 |
No log | 24.0 | 400 | 1.0382 | 0.77 | 0.2475 | 0.29 | 0.2475 | 0.33 | 0.5525 | 0.66 | 0.7 | 0.72 | 0.755 | 0.735 | 0.7125 | 0.77 | 0.7625 |
No log | 24.96 | 416 | 1.0630 | 0.76 | 0.255 | 0.2525 | 0.23 | 0.3675 | 0.5325 | 0.6225 | 0.685 | 0.7125 | 0.75 | 0.7275 | 0.7275 | 0.7675 | 0.7625 |
No log | 25.98 | 433 | 1.0887 | 0.7625 | 0.26 | 0.2825 | 0.2425 | 0.3775 | 0.5325 | 0.6575 | 0.7025 | 0.705 | 0.7525 | 0.76 | 0.755 | 0.775 | 0.765 |
No log | 27.0 | 450 | 1.1224 | 0.7675 | 0.255 | 0.3125 | 0.2425 | 0.3875 | 0.5275 | 0.665 | 0.7 | 0.7125 | 0.7475 | 0.7675 | 0.75 | 0.7625 | 0.7675 |
No log | 27.96 | 466 | 1.1230 | 0.7625 | 0.275 | 0.3675 | 0.2775 | 0.3825 | 0.5525 | 0.67 | 0.6875 | 0.7075 | 0.7425 | 0.7475 | 0.745 | 0.7675 | 0.7575 |
No log | 28.98 | 483 | 1.1384 | 0.7525 | 0.2625 | 0.375 | 0.3075 | 0.38 | 0.5375 | 0.67 | 0.7 | 0.7325 | 0.745 | 0.7525 | 0.7525 | 0.75 | 0.75 |
0.3128 | 30.0 | 500 | 1.1192 | 0.76 | 0.285 | 0.42 | 0.415 | 0.4425 | 0.585 | 0.6825 | 0.725 | 0.7375 | 0.755 | 0.7725 | 0.7625 | 0.765 | 0.76 |
0.3128 | 30.96 | 516 | 1.1687 | 0.7625 | 0.27 | 0.3775 | 0.335 | 0.3875 | 0.5675 | 0.665 | 0.685 | 0.725 | 0.7375 | 0.7475 | 0.7525 | 0.7575 | 0.76 |
0.3128 | 31.98 | 533 | 1.2018 | 0.755 | 0.2625 | 0.37 | 0.3325 | 0.385 | 0.5575 | 0.6625 | 0.69 | 0.73 | 0.7475 | 0.76 | 0.7575 | 0.76 | 0.75 |
0.3128 | 33.0 | 550 | 1.1723 | 0.7725 | 0.265 | 0.355 | 0.3425 | 0.4 | 0.575 | 0.65 | 0.685 | 0.715 | 0.745 | 0.76 | 0.77 | 0.77 | 0.775 |
0.3128 | 33.96 | 566 | 1.2252 | 0.7475 | 0.28 | 0.4175 | 0.4325 | 0.4675 | 0.5775 | 0.67 | 0.7025 | 0.715 | 0.7475 | 0.7525 | 0.76 | 0.755 | 0.745 |
0.3128 | 34.98 | 583 | 1.1831 | 0.765 | 0.29 | 0.4375 | 0.435 | 0.4825 | 0.5925 | 0.68 | 0.7175 | 0.735 | 0.75 | 0.7575 | 0.765 | 0.765 | 0.77 |
0.3128 | 36.0 | 600 | 1.2292 | 0.755 | 0.28 | 0.4375 | 0.4275 | 0.4875 | 0.5875 | 0.6725 | 0.7 | 0.7275 | 0.745 | 0.7475 | 0.745 | 0.75 | 0.7525 |
0.3128 | 36.96 | 616 | 1.2460 | 0.755 | 0.2825 | 0.425 | 0.435 | 0.5125 | 0.59 | 0.6775 | 0.7075 | 0.73 | 0.745 | 0.7525 | 0.755 | 0.755 | 0.755 |
0.3128 | 37.98 | 633 | 1.2560 | 0.7525 | 0.2675 | 0.4525 | 0.46 | 0.5175 | 0.5875 | 0.6675 | 0.705 | 0.7225 | 0.74 | 0.745 | 0.745 | 0.7475 | 0.7525 |
0.3128 | 39.0 | 650 | 1.2463 | 0.77 | 0.2825 | 0.45 | 0.475 | 0.5225 | 0.59 | 0.67 | 0.6975 | 0.73 | 0.7425 | 0.76 | 0.7625 | 0.76 | 0.765 |
0.3128 | 39.96 | 666 | 1.2493 | 0.765 | 0.2775 | 0.455 | 0.49 | 0.5325 | 0.6 | 0.6825 | 0.7225 | 0.7425 | 0.75 | 0.765 | 0.76 | 0.765 | 0.7625 |
0.3128 | 40.98 | 683 | 1.2727 | 0.7625 | 0.275 | 0.47 | 0.49 | 0.535 | 0.61 | 0.68 | 0.7 | 0.7275 | 0.74 | 0.7525 | 0.76 | 0.76 | 0.7575 |
0.3128 | 42.0 | 700 | 1.2951 | 0.7525 | 0.2725 | 0.445 | 0.495 | 0.5525 | 0.5975 | 0.67 | 0.6975 | 0.735 | 0.7575 | 0.75 | 0.7575 | 0.76 | 0.75 |
0.3128 | 42.96 | 716 | 1.2865 | 0.75 | 0.275 | 0.455 | 0.5075 | 0.5525 | 0.6025 | 0.695 | 0.71 | 0.73 | 0.745 | 0.7475 | 0.7575 | 0.755 | 0.75 |
0.3128 | 43.98 | 733 | 1.2864 | 0.76 | 0.2775 | 0.465 | 0.5025 | 0.5575 | 0.6075 | 0.6925 | 0.6975 | 0.73 | 0.7375 | 0.7575 | 0.7625 | 0.755 | 0.7575 |
0.3128 | 45.0 | 750 | 1.3615 | 0.7575 | 0.285 | 0.465 | 0.5075 | 0.5525 | 0.6175 | 0.6875 | 0.7075 | 0.735 | 0.73 | 0.7375 | 0.75 | 0.745 | 0.7525 |
0.3128 | 45.96 | 766 | 1.3161 | 0.7525 | 0.2825 | 0.47 | 0.5125 | 0.5575 | 0.62 | 0.6825 | 0.6975 | 0.7275 | 0.735 | 0.7525 | 0.755 | 0.755 | 0.7525 |
0.3128 | 46.98 | 783 | 1.3508 | 0.755 | 0.29 | 0.4775 | 0.5125 | 0.5725 | 0.6175 | 0.6875 | 0.705 | 0.7175 | 0.73 | 0.7525 | 0.7575 | 0.755 | 0.7575 |
0.3128 | 48.0 | 800 | 1.3321 | 0.76 | 0.285 | 0.47 | 0.5175 | 0.565 | 0.62 | 0.6925 | 0.7125 | 0.73 | 0.7475 | 0.755 | 0.7575 | 0.7575 | 0.7575 |
0.3128 | 48.96 | 816 | 1.3362 | 0.7625 | 0.2825 | 0.465 | 0.515 | 0.5725 | 0.6275 | 0.69 | 0.705 | 0.74 | 0.745 | 0.7625 | 0.765 | 0.76 | 0.76 |
0.3128 | 49.98 | 833 | 1.3070 | 0.76 | 0.2825 | 0.4725 | 0.5175 | 0.5725 | 0.62 | 0.69 | 0.71 | 0.7325 | 0.7375 | 0.7525 | 0.76 | 0.76 | 0.7625 |
0.3128 | 51.0 | 850 | 1.3199 | 0.7575 | 0.2875 | 0.47 | 0.5125 | 0.5775 | 0.625 | 0.6875 | 0.705 | 0.7375 | 0.735 | 0.755 | 0.7675 | 0.76 | 0.7575 |
0.3128 | 51.96 | 866 | 1.3464 | 0.755 | 0.2875 | 0.4675 | 0.515 | 0.5775 | 0.6275 | 0.685 | 0.7075 | 0.73 | 0.7325 | 0.755 | 0.76 | 0.7525 | 0.755 |
0.3128 | 52.98 | 883 | 1.3286 | 0.7575 | 0.29 | 0.47 | 0.515 | 0.5775 | 0.6275 | 0.6925 | 0.7125 | 0.7325 | 0.7425 | 0.76 | 0.7625 | 0.76 | 0.76 |
0.3128 | 54.0 | 900 | 1.3277 | 0.7625 | 0.2925 | 0.4625 | 0.52 | 0.5825 | 0.6275 | 0.6975 | 0.715 | 0.735 | 0.74 | 0.7575 | 0.765 | 0.7575 | 0.76 |
0.3128 | 54.96 | 916 | 1.3274 | 0.7625 | 0.2925 | 0.4625 | 0.52 | 0.5875 | 0.6275 | 0.695 | 0.7125 | 0.7375 | 0.7375 | 0.7575 | 0.7625 | 0.7625 | 0.7625 |
0.3128 | 55.98 | 933 | 1.3393 | 0.7625 | 0.29 | 0.4625 | 0.5225 | 0.585 | 0.625 | 0.695 | 0.71 | 0.7375 | 0.7375 | 0.755 | 0.7625 | 0.755 | 0.76 |
0.3128 | 57.0 | 950 | 1.3453 | 0.7625 | 0.29 | 0.46 | 0.5225 | 0.585 | 0.625 | 0.695 | 0.71 | 0.73 | 0.73 | 0.7575 | 0.76 | 0.7575 | 0.7625 |
0.3128 | 57.6 | 960 | 1.3452 | 0.7625 | 0.29 | 0.4625 | 0.5225 | 0.585 | 0.625 | 0.695 | 0.71 | 0.73 | 0.73 | 0.7575 | 0.76 | 0.7575 | 0.7625 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for Omar95farag/2024-01-31_one_stage_subgraphs_weighted_txt_vis_conc_all_ramp
Base model
microsoft/layoutlmv3-base