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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-26_txt_vis_concat_enc_5_6_7_8_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: 1.7273
- Accuracy: 0.725
- Exit 0 Accuracy: 0.07
- Exit 1 Accuracy: 0.7175
- Exit 2 Accuracy: 0.725
- Exit 3 Accuracy: 0.72
- Exit 4 Accuracy: 0.72
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 |
---|---|---|---|---|---|---|---|---|---|
No log | 0.96 | 16 | 2.6945 | 0.1475 | 0.0675 | 0.0625 | 0.0625 | 0.0625 | 0.0625 |
No log | 1.98 | 33 | 2.5294 | 0.235 | 0.0725 | 0.1775 | 0.0625 | 0.0575 | 0.0625 |
No log | 3.0 | 50 | 2.3532 | 0.2975 | 0.0575 | 0.1925 | 0.155 | 0.1475 | 0.0625 |
No log | 3.96 | 66 | 2.1334 | 0.3675 | 0.0675 | 0.21 | 0.2225 | 0.145 | 0.0625 |
No log | 4.98 | 83 | 1.8627 | 0.5075 | 0.0625 | 0.245 | 0.245 | 0.2075 | 0.0625 |
No log | 6.0 | 100 | 1.6181 | 0.575 | 0.0625 | 0.2875 | 0.2975 | 0.2875 | 0.0625 |
No log | 6.96 | 116 | 1.4648 | 0.5825 | 0.0675 | 0.335 | 0.2925 | 0.3225 | 0.08 |
No log | 7.98 | 133 | 1.3335 | 0.6525 | 0.0775 | 0.43 | 0.355 | 0.37 | 0.175 |
No log | 9.0 | 150 | 1.2244 | 0.6625 | 0.0725 | 0.395 | 0.4025 | 0.44 | 0.24 |
No log | 9.96 | 166 | 1.0634 | 0.7325 | 0.07 | 0.4425 | 0.415 | 0.5025 | 0.295 |
No log | 10.98 | 183 | 1.0834 | 0.6975 | 0.07 | 0.4375 | 0.435 | 0.5075 | 0.3375 |
No log | 12.0 | 200 | 0.9979 | 0.7325 | 0.0675 | 0.495 | 0.5025 | 0.54 | 0.445 |
No log | 12.96 | 216 | 1.0157 | 0.7125 | 0.0625 | 0.475 | 0.54 | 0.575 | 0.4475 |
No log | 13.98 | 233 | 1.0106 | 0.715 | 0.06 | 0.51 | 0.555 | 0.595 | 0.535 |
No log | 15.0 | 250 | 1.0085 | 0.73 | 0.0575 | 0.555 | 0.6275 | 0.61 | 0.575 |
No log | 15.96 | 266 | 1.0560 | 0.72 | 0.06 | 0.5775 | 0.6175 | 0.5975 | 0.6275 |
No log | 16.98 | 283 | 1.0970 | 0.7275 | 0.055 | 0.61 | 0.6475 | 0.6525 | 0.675 |
No log | 18.0 | 300 | 1.1175 | 0.7425 | 0.055 | 0.6025 | 0.6575 | 0.65 | 0.6725 |
No log | 18.96 | 316 | 1.1457 | 0.7225 | 0.055 | 0.635 | 0.6625 | 0.6575 | 0.6775 |
No log | 19.98 | 333 | 1.1841 | 0.7325 | 0.0625 | 0.6475 | 0.66 | 0.66 | 0.71 |
No log | 21.0 | 350 | 1.2550 | 0.72 | 0.055 | 0.6675 | 0.6725 | 0.665 | 0.6925 |
No log | 21.96 | 366 | 1.1865 | 0.7375 | 0.0525 | 0.6575 | 0.6675 | 0.665 | 0.71 |
No log | 22.98 | 383 | 1.2642 | 0.7375 | 0.0525 | 0.6625 | 0.7 | 0.6775 | 0.71 |
No log | 24.0 | 400 | 1.2972 | 0.7375 | 0.0525 | 0.675 | 0.6875 | 0.6775 | 0.71 |
No log | 24.96 | 416 | 1.3043 | 0.7375 | 0.055 | 0.68 | 0.7075 | 0.695 | 0.7125 |
No log | 25.98 | 433 | 1.3557 | 0.73 | 0.0575 | 0.675 | 0.7075 | 0.705 | 0.7175 |
No log | 27.0 | 450 | 1.3476 | 0.7525 | 0.0575 | 0.6875 | 0.705 | 0.705 | 0.7325 |
No log | 27.96 | 466 | 1.4352 | 0.7275 | 0.0575 | 0.7 | 0.7275 | 0.7175 | 0.73 |
No log | 28.98 | 483 | 1.4874 | 0.7175 | 0.055 | 0.695 | 0.7 | 0.71 | 0.715 |
1.4115 | 30.0 | 500 | 1.4958 | 0.715 | 0.055 | 0.7025 | 0.6975 | 0.7175 | 0.725 |
1.4115 | 30.96 | 516 | 1.5134 | 0.7225 | 0.0575 | 0.72 | 0.735 | 0.73 | 0.7425 |
1.4115 | 31.98 | 533 | 1.5730 | 0.72 | 0.0575 | 0.7175 | 0.715 | 0.73 | 0.7325 |
1.4115 | 33.0 | 550 | 1.5390 | 0.7225 | 0.0575 | 0.7325 | 0.725 | 0.72 | 0.7275 |
1.4115 | 33.96 | 566 | 1.5308 | 0.73 | 0.0575 | 0.72 | 0.7125 | 0.7125 | 0.7225 |
1.4115 | 34.98 | 583 | 1.5720 | 0.7275 | 0.0575 | 0.73 | 0.7175 | 0.7175 | 0.73 |
1.4115 | 36.0 | 600 | 1.6400 | 0.7225 | 0.06 | 0.715 | 0.715 | 0.715 | 0.7275 |
1.4115 | 36.96 | 616 | 1.5729 | 0.745 | 0.06 | 0.72 | 0.73 | 0.73 | 0.7325 |
1.4115 | 37.98 | 633 | 1.5931 | 0.7375 | 0.065 | 0.73 | 0.7325 | 0.735 | 0.7325 |
1.4115 | 39.0 | 650 | 1.6346 | 0.725 | 0.065 | 0.7225 | 0.7175 | 0.73 | 0.7325 |
1.4115 | 39.96 | 666 | 1.6544 | 0.7225 | 0.065 | 0.72 | 0.7225 | 0.72 | 0.7225 |
1.4115 | 40.98 | 683 | 1.6793 | 0.725 | 0.065 | 0.735 | 0.7225 | 0.7325 | 0.7325 |
1.4115 | 42.0 | 700 | 1.6609 | 0.73 | 0.065 | 0.715 | 0.7225 | 0.73 | 0.7275 |
1.4115 | 42.96 | 716 | 1.6950 | 0.7175 | 0.0675 | 0.72 | 0.7275 | 0.7225 | 0.7225 |
1.4115 | 43.98 | 733 | 1.7207 | 0.7275 | 0.07 | 0.725 | 0.7275 | 0.7325 | 0.7275 |
1.4115 | 45.0 | 750 | 1.6978 | 0.7325 | 0.07 | 0.725 | 0.7225 | 0.7225 | 0.725 |
1.4115 | 45.96 | 766 | 1.6981 | 0.7325 | 0.07 | 0.725 | 0.735 | 0.7275 | 0.7275 |
1.4115 | 46.98 | 783 | 1.7356 | 0.73 | 0.07 | 0.7075 | 0.72 | 0.715 | 0.715 |
1.4115 | 48.0 | 800 | 1.7017 | 0.73 | 0.07 | 0.7175 | 0.7225 | 0.7275 | 0.725 |
1.4115 | 48.96 | 816 | 1.7040 | 0.73 | 0.07 | 0.72 | 0.725 | 0.7225 | 0.7275 |
1.4115 | 49.98 | 833 | 1.7298 | 0.7225 | 0.07 | 0.715 | 0.73 | 0.73 | 0.7225 |
1.4115 | 51.0 | 850 | 1.7115 | 0.725 | 0.07 | 0.72 | 0.725 | 0.725 | 0.7225 |
1.4115 | 51.96 | 866 | 1.7245 | 0.7325 | 0.07 | 0.71 | 0.7275 | 0.72 | 0.7225 |
1.4115 | 52.98 | 883 | 1.6998 | 0.74 | 0.07 | 0.72 | 0.725 | 0.725 | 0.7225 |
1.4115 | 54.0 | 900 | 1.7205 | 0.7325 | 0.07 | 0.7175 | 0.7275 | 0.725 | 0.7225 |
1.4115 | 54.96 | 916 | 1.7318 | 0.7275 | 0.07 | 0.72 | 0.73 | 0.7225 | 0.7225 |
1.4115 | 55.98 | 933 | 1.7341 | 0.7275 | 0.07 | 0.7175 | 0.7275 | 0.72 | 0.7225 |
1.4115 | 57.0 | 950 | 1.7280 | 0.7225 | 0.07 | 0.7175 | 0.725 | 0.7175 | 0.72 |
1.4115 | 57.6 | 960 | 1.7273 | 0.725 | 0.07 | 0.7175 | 0.725 | 0.72 | 0.72 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3