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: 5.0085
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 |
|---|---|---|---|
| 5.3352 | 0.22 | 50 | 4.5120 |
| 4.3566 | 0.44 | 100 | 4.0171 |
| 3.9989 | 0.66 | 150 | 3.9234 |
| 3.8014 | 0.88 | 200 | 3.5051 |
| 3.5509 | 1.11 | 250 | 3.5408 |
| 3.1372 | 1.33 | 300 | 3.2247 |
| 2.9307 | 1.55 | 350 | 3.1225 |
| 2.928 | 1.77 | 400 | 2.9461 |
| 2.7004 | 1.99 | 450 | 2.5206 |
| 2.1271 | 2.21 | 500 | 2.6079 |
| 2.1387 | 2.43 | 550 | 2.8524 |
| 1.9593 | 2.65 | 600 | 2.8749 |
| 2.0105 | 2.88 | 650 | 2.6666 |
| 1.84 | 3.1 | 700 | 3.0599 |
| 1.9359 | 3.32 | 750 | 3.0472 |
| 1.547 | 3.54 | 800 | 2.2308 |
| 1.4161 | 3.76 | 850 | 2.2889 |
| 2.1804 | 3.98 | 900 | 2.1462 |
| 1.0261 | 4.2 | 950 | 2.9056 |
| 1.392 | 4.42 | 1000 | 3.0021 |
| 1.3816 | 4.65 | 1050 | 2.6913 |
| 1.0117 | 4.87 | 1100 | 2.8484 |
| 1.0094 | 5.09 | 1150 | 2.6936 |
| 0.7316 | 5.31 | 1200 | 2.9901 |
| 0.9172 | 5.53 | 1250 | 2.6366 |
| 0.8608 | 5.75 | 1300 | 2.8584 |
| 0.7116 | 5.97 | 1350 | 3.1944 |
| 0.321 | 6.19 | 1400 | 3.4703 |
| 0.6663 | 6.42 | 1450 | 3.0456 |
| 0.6319 | 6.64 | 1500 | 3.3318 |
| 0.7001 | 6.86 | 1550 | 3.1439 |
| 0.5952 | 7.08 | 1600 | 3.3220 |
| 0.39 | 7.3 | 1650 | 3.8266 |
| 0.434 | 7.52 | 1700 | 3.8287 |
| 0.7599 | 7.74 | 1750 | 3.4079 |
| 0.52 | 7.96 | 1800 | 3.3982 |
| 0.5257 | 8.19 | 1850 | 3.5208 |
| 0.4304 | 8.41 | 1900 | 3.8404 |
| 0.4213 | 8.63 | 1950 | 3.9974 |
| 0.3033 | 8.85 | 2000 | 3.9492 |
| 0.2947 | 9.07 | 2050 | 3.9279 |
| 0.2285 | 9.29 | 2100 | 3.5652 |
| 0.3472 | 9.51 | 2150 | 3.5741 |
| 0.2644 | 9.73 | 2200 | 3.8685 |
| 0.3667 | 9.96 | 2250 | 3.5242 |
| 0.1528 | 10.18 | 2300 | 3.5848 |
| 0.1489 | 10.4 | 2350 | 3.8603 |
| 0.1984 | 10.62 | 2400 | 3.6773 |
| 0.3131 | 10.84 | 2450 | 3.7021 |
| 0.1866 | 11.06 | 2500 | 3.8918 |
| 0.1908 | 11.28 | 2550 | 3.9479 |
| 0.1955 | 11.5 | 2600 | 3.9596 |
| 0.1382 | 11.73 | 2650 | 4.1168 |
| 0.2528 | 11.95 | 2700 | 4.1007 |
| 0.0538 | 12.17 | 2750 | 4.2003 |
| 0.1354 | 12.39 | 2800 | 4.3118 |
| 0.1218 | 12.61 | 2850 | 4.1494 |
| 0.1956 | 12.83 | 2900 | 4.1475 |
| 0.0691 | 13.05 | 2950 | 4.4141 |
| 0.0526 | 13.27 | 3000 | 4.7115 |
| 0.0984 | 13.5 | 3050 | 4.6013 |
| 0.1828 | 13.72 | 3100 | 4.2457 |
| 0.0906 | 13.94 | 3150 | 4.4969 |
| 0.025 | 14.16 | 3200 | 4.6981 |
| 0.0149 | 14.38 | 3250 | 4.8642 |
| 0.123 | 14.6 | 3300 | 4.5326 |
| 0.0876 | 14.82 | 3350 | 4.5953 |
| 0.0771 | 15.04 | 3400 | 4.4175 |
| 0.066 | 15.27 | 3450 | 4.6324 |
| 0.0542 | 15.49 | 3500 | 4.5058 |
| 0.0293 | 15.71 | 3550 | 4.7244 |
| 0.0428 | 15.93 | 3600 | 4.9415 |
| 0.009 | 16.15 | 3650 | 4.9592 |
| 0.0715 | 16.37 | 3700 | 4.9211 |
| 0.0044 | 16.59 | 3750 | 4.9854 |
| 0.0767 | 16.81 | 3800 | 4.7985 |
| 0.0356 | 17.04 | 3850 | 4.7618 |
| 0.0562 | 17.26 | 3900 | 4.9239 |
| 0.0085 | 17.48 | 3950 | 4.9837 |
| 0.0114 | 17.7 | 4000 | 5.0808 |
| 0.0057 | 17.92 | 4050 | 5.0377 |
| 0.0306 | 18.14 | 4100 | 5.0137 |
| 0.0426 | 18.36 | 4150 | 4.9367 |
| 0.0429 | 18.58 | 4200 | 5.0050 |
| 0.0081 | 18.81 | 4250 | 4.9806 |
| 0.0168 | 19.03 | 4300 | 4.9902 |
| 0.0074 | 19.25 | 4350 | 4.9939 |
| 0.0075 | 19.47 | 4400 | 4.9986 |
| 0.0307 | 19.69 | 4450 | 5.0095 |
| 0.02 | 19.91 | 4500 | 5.0085 |
Framework versions
- Transformers 4.29.1
- Pytorch 1.12.1
- Datasets 2.11.0
- Tokenizers 0.11.0
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