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EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-02_txt_vis_concat_enc_10_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: 1.3065
- Accuracy: 0.78
- Exit 0 Accuracy: 0.08
- Exit 1 Accuracy: 0.78
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 | 2.6831 | 0.1225 | 0.0425 | 0.0625 |
No log | 1.98 | 33 | 2.5269 | 0.2475 | 0.0475 | 0.0625 |
No log | 3.0 | 50 | 2.3314 | 0.3 | 0.065 | 0.0625 |
No log | 3.96 | 66 | 2.1407 | 0.3875 | 0.065 | 0.0625 |
No log | 4.98 | 83 | 1.9319 | 0.495 | 0.065 | 0.0625 |
No log | 6.0 | 100 | 1.6262 | 0.6075 | 0.0675 | 0.0625 |
No log | 6.96 | 116 | 1.4189 | 0.6525 | 0.0675 | 0.0625 |
No log | 7.98 | 133 | 1.2238 | 0.705 | 0.0675 | 0.0625 |
No log | 9.0 | 150 | 1.1216 | 0.725 | 0.065 | 0.0625 |
No log | 9.96 | 166 | 1.0243 | 0.745 | 0.0675 | 0.0625 |
No log | 10.98 | 183 | 0.9489 | 0.7725 | 0.0675 | 0.0625 |
No log | 12.0 | 200 | 0.9294 | 0.755 | 0.065 | 0.0625 |
No log | 12.96 | 216 | 0.9293 | 0.765 | 0.0675 | 0.0625 |
No log | 13.98 | 233 | 0.9327 | 0.76 | 0.07 | 0.0625 |
No log | 15.0 | 250 | 0.9204 | 0.785 | 0.065 | 0.55 |
No log | 15.96 | 266 | 0.9853 | 0.765 | 0.0725 | 0.7075 |
No log | 16.98 | 283 | 0.9700 | 0.77 | 0.0675 | 0.745 |
No log | 18.0 | 300 | 1.0333 | 0.755 | 0.0675 | 0.7475 |
No log | 18.96 | 316 | 1.0310 | 0.765 | 0.0675 | 0.7675 |
No log | 19.98 | 333 | 0.9923 | 0.785 | 0.07 | 0.7925 |
No log | 21.0 | 350 | 1.0907 | 0.7825 | 0.07 | 0.78 |
No log | 21.96 | 366 | 1.0952 | 0.7775 | 0.07 | 0.78 |
No log | 22.98 | 383 | 1.1303 | 0.7675 | 0.07 | 0.775 |
No log | 24.0 | 400 | 1.0843 | 0.78 | 0.0725 | 0.78 |
No log | 24.96 | 416 | 1.1523 | 0.7775 | 0.075 | 0.7775 |
No log | 25.98 | 433 | 1.1420 | 0.77 | 0.07 | 0.765 |
No log | 27.0 | 450 | 1.1594 | 0.7775 | 0.0675 | 0.7775 |
No log | 27.96 | 466 | 1.1929 | 0.775 | 0.07 | 0.7775 |
No log | 28.98 | 483 | 1.1958 | 0.78 | 0.0725 | 0.785 |
1.4332 | 30.0 | 500 | 1.1998 | 0.7775 | 0.0775 | 0.785 |
1.4332 | 30.96 | 516 | 1.2055 | 0.7825 | 0.0725 | 0.7825 |
1.4332 | 31.98 | 533 | 1.2077 | 0.7825 | 0.0775 | 0.78 |
1.4332 | 33.0 | 550 | 1.2200 | 0.78 | 0.0775 | 0.7825 |
1.4332 | 33.96 | 566 | 1.2262 | 0.78 | 0.0775 | 0.7825 |
1.4332 | 34.98 | 583 | 1.2393 | 0.775 | 0.0725 | 0.78 |
1.4332 | 36.0 | 600 | 1.2447 | 0.7775 | 0.075 | 0.78 |
1.4332 | 36.96 | 616 | 1.2493 | 0.7775 | 0.0725 | 0.7775 |
1.4332 | 37.98 | 633 | 1.2579 | 0.775 | 0.0775 | 0.7775 |
1.4332 | 39.0 | 650 | 1.2564 | 0.7775 | 0.0775 | 0.78 |
1.4332 | 39.96 | 666 | 1.2599 | 0.7775 | 0.0775 | 0.78 |
1.4332 | 40.98 | 683 | 1.2615 | 0.7775 | 0.08 | 0.785 |
1.4332 | 42.0 | 700 | 1.2718 | 0.78 | 0.075 | 0.78 |
1.4332 | 42.96 | 716 | 1.2750 | 0.78 | 0.0775 | 0.78 |
1.4332 | 43.98 | 733 | 1.2776 | 0.78 | 0.075 | 0.78 |
1.4332 | 45.0 | 750 | 1.2833 | 0.7825 | 0.075 | 0.78 |
1.4332 | 45.96 | 766 | 1.2873 | 0.7825 | 0.0775 | 0.78 |
1.4332 | 46.98 | 783 | 1.2951 | 0.78 | 0.0775 | 0.7775 |
1.4332 | 48.0 | 800 | 1.2968 | 0.78 | 0.075 | 0.7775 |
1.4332 | 48.96 | 816 | 1.2979 | 0.78 | 0.0775 | 0.78 |
1.4332 | 49.98 | 833 | 1.2994 | 0.78 | 0.075 | 0.78 |
1.4332 | 51.0 | 850 | 1.3009 | 0.78 | 0.0775 | 0.7775 |
1.4332 | 51.96 | 866 | 1.3009 | 0.7775 | 0.0775 | 0.78 |
1.4332 | 52.98 | 883 | 1.3019 | 0.78 | 0.0775 | 0.7825 |
1.4332 | 54.0 | 900 | 1.3036 | 0.78 | 0.08 | 0.78 |
1.4332 | 54.96 | 916 | 1.3048 | 0.78 | 0.08 | 0.78 |
1.4332 | 55.98 | 933 | 1.3058 | 0.78 | 0.08 | 0.78 |
1.4332 | 57.0 | 950 | 1.3063 | 0.78 | 0.08 | 0.78 |
1.4332 | 57.6 | 960 | 1.3065 | 0.78 | 0.08 | 0.78 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3