layoutlmv2-base-uncased_finetuned_docvqa
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.8430
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.3379 | 0.22 | 50 | 4.6257 |
| 4.4305 | 0.44 | 100 | 4.2230 |
| 4.0588 | 0.66 | 150 | 3.9539 |
| 3.7822 | 0.88 | 200 | 3.7040 |
| 3.4957 | 1.11 | 250 | 3.4754 |
| 3.2417 | 1.33 | 300 | 3.1954 |
| 2.8607 | 1.55 | 350 | 2.8809 |
| 2.6602 | 1.77 | 400 | 2.9741 |
| 2.621 | 1.99 | 450 | 2.8658 |
| 2.1733 | 2.21 | 500 | 2.7248 |
| 2.106 | 2.43 | 550 | 2.4072 |
| 1.8389 | 2.65 | 600 | 2.4147 |
| 1.7862 | 2.88 | 650 | 2.2116 |
| 1.4224 | 3.1 | 700 | 2.4379 |
| 1.4773 | 3.32 | 750 | 2.4346 |
| 1.2225 | 3.54 | 800 | 2.5779 |
| 1.5368 | 3.76 | 850 | 2.4343 |
| 1.479 | 3.98 | 900 | 2.1432 |
| 0.7982 | 4.2 | 950 | 2.5897 |
| 0.8336 | 4.42 | 1000 | 2.8477 |
| 1.0647 | 4.65 | 1050 | 2.7111 |
| 0.8795 | 4.87 | 1100 | 2.5601 |
| 0.9265 | 5.09 | 1150 | 2.9547 |
| 0.7111 | 5.31 | 1200 | 3.1621 |
| 0.7244 | 5.53 | 1250 | 2.7862 |
| 0.9501 | 5.75 | 1300 | 2.4007 |
| 0.7424 | 5.97 | 1350 | 2.9918 |
| 0.4422 | 6.19 | 1400 | 3.5247 |
| 0.5952 | 6.42 | 1450 | 2.8743 |
| 0.7173 | 6.64 | 1500 | 2.7440 |
| 0.6311 | 6.86 | 1550 | 2.9658 |
| 0.393 | 7.08 | 1600 | 3.0994 |
| 0.3655 | 7.3 | 1650 | 3.3074 |
| 0.3432 | 7.52 | 1700 | 3.1921 |
| 0.5986 | 7.74 | 1750 | 3.3517 |
| 0.5456 | 7.96 | 1800 | 3.1552 |
| 0.565 | 8.19 | 1850 | 2.9922 |
| 0.3902 | 8.41 | 1900 | 3.6814 |
| 0.3408 | 8.63 | 1950 | 3.2820 |
| 0.241 | 8.85 | 2000 | 3.5644 |
| 0.3172 | 9.07 | 2050 | 3.4752 |
| 0.294 | 9.29 | 2100 | 3.7023 |
| 0.2993 | 9.51 | 2150 | 3.5031 |
| 0.0928 | 9.73 | 2200 | 4.0305 |
| 0.4598 | 9.96 | 2250 | 3.4260 |
| 0.2795 | 10.18 | 2300 | 3.2730 |
| 0.0887 | 10.4 | 2350 | 3.7174 |
| 0.3682 | 10.62 | 2400 | 3.4060 |
| 0.1924 | 10.84 | 2450 | 4.1368 |
| 0.1825 | 11.06 | 2500 | 4.1640 |
| 0.1987 | 11.28 | 2550 | 3.9908 |
| 0.0875 | 11.5 | 2600 | 4.1872 |
| 0.1719 | 11.73 | 2650 | 3.9948 |
| 0.2844 | 11.95 | 2700 | 4.1731 |
| 0.1085 | 12.17 | 2750 | 3.9568 |
| 0.1496 | 12.39 | 2800 | 3.9272 |
| 0.0701 | 12.61 | 2850 | 4.2957 |
| 0.1617 | 12.83 | 2900 | 4.2806 |
| 0.0934 | 13.05 | 2950 | 4.3200 |
| 0.0405 | 13.27 | 3000 | 4.1869 |
| 0.0898 | 13.5 | 3050 | 4.1207 |
| 0.189 | 13.72 | 3100 | 4.4437 |
| 0.0798 | 13.94 | 3150 | 4.6480 |
| 0.1199 | 14.16 | 3200 | 4.4105 |
| 0.0922 | 14.38 | 3250 | 4.4321 |
| 0.1556 | 14.6 | 3300 | 4.3353 |
| 0.1933 | 14.82 | 3350 | 4.0635 |
| 0.0164 | 15.04 | 3400 | 4.1792 |
| 0.064 | 15.27 | 3450 | 4.2202 |
| 0.0914 | 15.49 | 3500 | 4.2382 |
| 0.0287 | 15.71 | 3550 | 4.4255 |
| 0.1054 | 15.93 | 3600 | 4.5788 |
| 0.0306 | 16.15 | 3650 | 4.7566 |
| 0.0297 | 16.37 | 3700 | 4.6610 |
| 0.0529 | 16.59 | 3750 | 4.6494 |
| 0.0729 | 16.81 | 3800 | 4.6314 |
| 0.0388 | 17.04 | 3850 | 4.6675 |
| 0.0207 | 17.26 | 3900 | 4.7816 |
| 0.0889 | 17.48 | 3950 | 4.6941 |
| 0.0058 | 17.7 | 4000 | 4.6818 |
| 0.0068 | 17.92 | 4050 | 4.7755 |
| 0.0222 | 18.14 | 4100 | 4.7658 |
| 0.1152 | 18.36 | 4150 | 4.8247 |
| 0.0181 | 18.58 | 4200 | 4.8290 |
| 0.0349 | 18.81 | 4250 | 4.7989 |
| 0.0165 | 19.03 | 4300 | 4.8208 |
| 0.029 | 19.25 | 4350 | 4.8401 |
| 0.0073 | 19.47 | 4400 | 4.8544 |
| 0.0277 | 19.69 | 4450 | 4.8356 |
| 0.0164 | 19.91 | 4500 | 4.8430 |
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2
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