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: 4.6207
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 |
|---|---|---|---|
| 5.3769 | 0.22 | 50 | 4.5459 |
| 4.3879 | 0.44 | 100 | 4.0099 |
| 3.9828 | 0.66 | 150 | 3.8817 |
| 3.7387 | 0.88 | 200 | 3.5334 |
| 3.4702 | 1.11 | 250 | 3.4670 |
| 3.0784 | 1.33 | 300 | 3.5327 |
| 2.8014 | 1.55 | 350 | 2.8722 |
| 2.673 | 1.77 | 400 | 2.8246 |
| 2.4546 | 1.99 | 450 | 2.7050 |
| 2.094 | 2.21 | 500 | 2.7639 |
| 1.9542 | 2.43 | 550 | 2.3472 |
| 1.8966 | 2.65 | 600 | 2.4508 |
| 1.7745 | 2.88 | 650 | 2.2813 |
| 1.4371 | 3.1 | 700 | 2.5234 |
| 1.3257 | 3.32 | 750 | 2.4358 |
| 1.3269 | 3.54 | 800 | 2.3044 |
| 1.4035 | 3.76 | 850 | 2.3546 |
| 1.6189 | 3.98 | 900 | 2.0838 |
| 0.9209 | 4.2 | 950 | 2.3836 |
| 0.8405 | 4.42 | 1000 | 3.1673 |
| 0.9808 | 4.65 | 1050 | 2.8038 |
| 0.8978 | 4.87 | 1100 | 2.7652 |
| 0.8733 | 5.09 | 1150 | 3.0965 |
| 0.7449 | 5.31 | 1200 | 2.9948 |
| 0.8173 | 5.53 | 1250 | 2.8631 |
| 0.8322 | 5.75 | 1300 | 2.6144 |
| 0.7147 | 5.97 | 1350 | 3.2041 |
| 0.6495 | 6.19 | 1400 | 3.3711 |
| 0.5458 | 6.42 | 1450 | 3.5480 |
| 0.5624 | 6.64 | 1500 | 3.3366 |
| 0.5736 | 6.86 | 1550 | 2.9356 |
| 0.2921 | 7.08 | 1600 | 3.4028 |
| 0.3883 | 7.3 | 1650 | 3.4411 |
| 0.3614 | 7.52 | 1700 | 3.2267 |
| 0.4376 | 7.74 | 1750 | 3.2137 |
| 0.4849 | 7.96 | 1800 | 3.6388 |
| 0.5035 | 8.19 | 1850 | 4.0089 |
| 0.36 | 8.41 | 1900 | 3.7903 |
| 0.2238 | 8.63 | 1950 | 3.7131 |
| 0.276 | 8.85 | 2000 | 3.8541 |
| 0.2661 | 9.07 | 2050 | 3.5220 |
| 0.291 | 9.29 | 2100 | 3.9075 |
| 0.3767 | 9.51 | 2150 | 3.4267 |
| 0.1326 | 9.73 | 2200 | 3.9069 |
| 0.2365 | 9.96 | 2250 | 3.5183 |
| 0.2676 | 10.18 | 2300 | 3.4975 |
| 0.131 | 10.4 | 2350 | 3.8138 |
| 0.1818 | 10.62 | 2400 | 3.6213 |
| 0.2988 | 10.84 | 2450 | 3.7380 |
| 0.0511 | 11.06 | 2500 | 4.1506 |
| 0.1909 | 11.28 | 2550 | 3.7369 |
| 0.0854 | 11.5 | 2600 | 3.8751 |
| 0.1291 | 11.73 | 2650 | 3.7143 |
| 0.1896 | 11.95 | 2700 | 4.0645 |
| 0.0746 | 12.17 | 2750 | 4.0363 |
| 0.1254 | 12.39 | 2800 | 4.0050 |
| 0.1588 | 12.61 | 2850 | 4.1739 |
| 0.1622 | 12.83 | 2900 | 4.2698 |
| 0.047 | 13.05 | 2950 | 4.5366 |
| 0.1023 | 13.27 | 3000 | 4.3094 |
| 0.1195 | 13.5 | 3050 | 4.3112 |
| 0.0753 | 13.72 | 3100 | 4.3137 |
| 0.0545 | 13.94 | 3150 | 4.4992 |
| 0.0771 | 14.16 | 3200 | 4.4759 |
| 0.0648 | 14.38 | 3250 | 4.5531 |
| 0.0935 | 14.6 | 3300 | 4.3725 |
| 0.1032 | 14.82 | 3350 | 4.3321 |
| 0.0199 | 15.04 | 3400 | 4.3527 |
| 0.0498 | 15.27 | 3450 | 4.2477 |
| 0.0688 | 15.49 | 3500 | 4.2340 |
| 0.0271 | 15.71 | 3550 | 4.2386 |
| 0.0176 | 15.93 | 3600 | 4.4715 |
| 0.0319 | 16.15 | 3650 | 4.5608 |
| 0.0061 | 16.37 | 3700 | 4.5767 |
| 0.0043 | 16.59 | 3750 | 4.6581 |
| 0.071 | 16.81 | 3800 | 4.5622 |
| 0.0689 | 17.04 | 3850 | 4.5067 |
| 0.0328 | 17.26 | 3900 | 4.4449 |
| 0.0784 | 17.48 | 3950 | 4.3684 |
| 0.0387 | 17.7 | 4000 | 4.4261 |
| 0.0107 | 17.92 | 4050 | 4.5190 |
| 0.0056 | 18.14 | 4100 | 4.4963 |
| 0.0261 | 18.36 | 4150 | 4.5995 |
| 0.0061 | 18.58 | 4200 | 4.6121 |
| 0.0354 | 18.81 | 4250 | 4.5794 |
| 0.0111 | 19.03 | 4300 | 4.5977 |
| 0.022 | 19.25 | 4350 | 4.6151 |
| 0.0628 | 19.47 | 4400 | 4.6061 |
| 0.0186 | 19.69 | 4450 | 4.6180 |
| 0.0077 | 19.91 | 4500 | 4.6207 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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