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: 4.8270
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 |
|---|---|---|---|
| 5.2536 | 0.22 | 50 | 4.4585 |
| 4.2935 | 0.44 | 100 | 4.1809 |
| 4.0821 | 0.66 | 150 | 3.7822 |
| 3.8393 | 0.88 | 200 | 3.4892 |
| 3.4613 | 1.11 | 250 | 3.4728 |
| 2.9792 | 1.33 | 300 | 3.0083 |
| 2.9501 | 1.55 | 350 | 2.9516 |
| 2.8889 | 1.77 | 400 | 2.7119 |
| 2.4728 | 1.99 | 450 | 2.6027 |
| 1.9523 | 2.21 | 500 | 2.5411 |
| 1.9312 | 2.43 | 550 | 2.5406 |
| 1.8471 | 2.65 | 600 | 2.4484 |
| 1.7456 | 2.88 | 650 | 1.9633 |
| 1.4404 | 3.1 | 700 | 2.5037 |
| 1.3567 | 3.32 | 750 | 2.4763 |
| 1.3247 | 3.54 | 800 | 2.3191 |
| 1.2739 | 3.76 | 850 | 2.4416 |
| 1.2548 | 3.98 | 900 | 2.3970 |
| 0.8073 | 4.2 | 950 | 2.3073 |
| 0.8992 | 4.42 | 1000 | 2.6353 |
| 0.8439 | 4.65 | 1050 | 2.7353 |
| 0.9051 | 4.87 | 1100 | 2.4587 |
| 0.7201 | 5.09 | 1150 | 3.1759 |
| 0.6394 | 5.31 | 1200 | 3.2409 |
| 0.6142 | 5.53 | 1250 | 3.0543 |
| 0.6385 | 5.75 | 1300 | 2.5017 |
| 0.5985 | 5.97 | 1350 | 3.1824 |
| 0.2824 | 6.19 | 1400 | 3.3628 |
| 0.4695 | 6.42 | 1450 | 2.9493 |
| 0.6322 | 6.64 | 1500 | 2.8337 |
| 0.5114 | 6.86 | 1550 | 3.2387 |
| 0.4555 | 7.08 | 1600 | 3.3994 |
| 0.3308 | 7.3 | 1650 | 3.3562 |
| 0.2209 | 7.52 | 1700 | 3.5050 |
| 0.4407 | 7.74 | 1750 | 3.8571 |
| 0.442 | 7.96 | 1800 | 3.5946 |
| 0.2722 | 8.19 | 1850 | 3.6526 |
| 0.423 | 8.41 | 1900 | 3.1283 |
| 0.316 | 8.63 | 1950 | 3.4300 |
| 0.3435 | 8.85 | 2000 | 3.6418 |
| 0.2815 | 9.07 | 2050 | 3.4021 |
| 0.2051 | 9.29 | 2100 | 3.7000 |
| 0.2442 | 9.51 | 2150 | 3.4389 |
| 0.1833 | 9.73 | 2200 | 3.7243 |
| 0.2408 | 9.96 | 2250 | 3.6520 |
| 0.1971 | 10.18 | 2300 | 3.5589 |
| 0.196 | 10.4 | 2350 | 3.7747 |
| 0.2511 | 10.62 | 2400 | 3.5574 |
| 0.1473 | 10.84 | 2450 | 3.7469 |
| 0.1141 | 11.06 | 2500 | 3.7303 |
| 0.1058 | 11.28 | 2550 | 4.0495 |
| 0.1845 | 11.5 | 2600 | 3.8454 |
| 0.1548 | 11.73 | 2650 | 4.2611 |
| 0.1009 | 11.95 | 2700 | 4.1706 |
| 0.1412 | 12.17 | 2750 | 4.2072 |
| 0.0809 | 12.39 | 2800 | 4.2862 |
| 0.1126 | 12.61 | 2850 | 4.0895 |
| 0.0736 | 12.83 | 2900 | 4.2500 |
| 0.0525 | 13.05 | 2950 | 4.3110 |
| 0.0621 | 13.27 | 3000 | 4.1005 |
| 0.1199 | 13.5 | 3050 | 4.1956 |
| 0.0837 | 13.72 | 3100 | 4.6910 |
| 0.1912 | 13.94 | 3150 | 4.2988 |
| 0.0891 | 14.16 | 3200 | 4.3609 |
| 0.0478 | 14.38 | 3250 | 4.3829 |
| 0.0727 | 14.6 | 3300 | 4.2299 |
| 0.1063 | 14.82 | 3350 | 3.9861 |
| 0.0392 | 15.04 | 3400 | 4.1840 |
| 0.1334 | 15.27 | 3450 | 4.3275 |
| 0.0178 | 15.49 | 3500 | 4.3959 |
| 0.0917 | 15.71 | 3550 | 4.4633 |
| 0.0477 | 15.93 | 3600 | 4.5239 |
| 0.0058 | 16.15 | 3650 | 4.5556 |
| 0.0719 | 16.37 | 3700 | 4.5381 |
| 0.0412 | 16.59 | 3750 | 4.5468 |
| 0.0051 | 16.81 | 3800 | 4.6192 |
| 0.0437 | 17.04 | 3850 | 4.6033 |
| 0.0062 | 17.26 | 3900 | 4.7104 |
| 0.0374 | 17.48 | 3950 | 4.5678 |
| 0.0113 | 17.7 | 4000 | 4.6266 |
| 0.0616 | 17.92 | 4050 | 4.6180 |
| 0.0082 | 18.14 | 4100 | 4.7478 |
| 0.0034 | 18.36 | 4150 | 4.7665 |
| 0.0395 | 18.58 | 4200 | 4.7568 |
| 0.0108 | 18.81 | 4250 | 4.7385 |
| 0.015 | 19.03 | 4300 | 4.8266 |
| 0.1061 | 19.25 | 4350 | 4.8090 |
| 0.0181 | 19.47 | 4400 | 4.8226 |
| 0.0055 | 19.69 | 4450 | 4.8228 |
| 0.0134 | 19.91 | 4500 | 4.8270 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.10.1
- Tokenizers 0.14.1
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