metadata
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layout_qa_hparam_tuning
results: []
layout_qa_hparam_tuning
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.3973
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-06
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.0364 | 0.28 | 50 | 5.7109 |
| 5.6991 | 0.55 | 100 | 5.3444 |
| 5.3564 | 0.83 | 150 | 5.0481 |
| 5.1086 | 1.1 | 200 | 4.8591 |
| 4.8464 | 1.38 | 250 | 4.6824 |
| 4.7178 | 1.66 | 300 | 4.5995 |
| 4.6003 | 1.93 | 350 | 4.4761 |
| 4.4415 | 2.21 | 400 | 4.3781 |
| 4.3911 | 2.49 | 450 | 4.3017 |
| 4.2507 | 2.76 | 500 | 4.2496 |
| 4.1975 | 3.04 | 550 | 4.2142 |
| 4.0971 | 3.31 | 600 | 4.1524 |
| 4.0671 | 3.59 | 650 | 4.1038 |
| 4.0225 | 3.87 | 700 | 4.0486 |
| 3.9641 | 4.14 | 750 | 4.0478 |
| 3.9662 | 4.42 | 800 | 4.0082 |
| 3.8185 | 4.7 | 850 | 4.0001 |
| 3.8798 | 4.97 | 900 | 3.9235 |
| 3.7622 | 5.25 | 950 | 3.9549 |
| 3.7109 | 5.52 | 1000 | 3.8668 |
| 3.7218 | 5.8 | 1050 | 3.8849 |
| 3.6718 | 6.08 | 1100 | 3.9426 |
| 3.6925 | 6.35 | 1150 | 3.8288 |
| 3.5893 | 6.63 | 1200 | 3.8240 |
| 3.5545 | 6.91 | 1250 | 3.8149 |
| 3.4922 | 7.18 | 1300 | 3.8104 |
| 3.5117 | 7.46 | 1350 | 3.8128 |
| 3.3699 | 7.73 | 1400 | 3.7590 |
| 3.4538 | 8.01 | 1450 | 3.7577 |
| 3.3669 | 8.29 | 1500 | 3.7370 |
| 3.3516 | 8.56 | 1550 | 3.7278 |
| 3.3264 | 8.84 | 1600 | 3.6671 |
| 3.3102 | 9.12 | 1650 | 3.6953 |
| 3.241 | 9.39 | 1700 | 3.6474 |
| 3.278 | 9.67 | 1750 | 3.8793 |
| 3.2593 | 9.94 | 1800 | 3.6447 |
| 3.1663 | 10.22 | 1850 | 3.8442 |
| 3.0952 | 10.5 | 1900 | 3.6431 |
| 3.1355 | 10.77 | 1950 | 3.6261 |
| 3.0874 | 11.05 | 2000 | 3.5631 |
| 3.0178 | 11.33 | 2050 | 3.5662 |
| 2.9257 | 11.6 | 2100 | 3.4744 |
| 2.9164 | 11.88 | 2150 | 3.4374 |
| 2.8061 | 12.15 | 2200 | 3.4550 |
| 2.8664 | 12.43 | 2250 | 3.4217 |
| 2.7886 | 12.71 | 2300 | 3.4294 |
| 2.8398 | 12.98 | 2350 | 3.3906 |
| 2.7823 | 13.26 | 2400 | 3.4311 |
| 2.7024 | 13.54 | 2450 | 3.4267 |
| 2.7443 | 13.81 | 2500 | 3.3412 |
| 2.6747 | 14.09 | 2550 | 3.3656 |
| 2.723 | 14.36 | 2600 | 3.5019 |
| 2.6278 | 14.64 | 2650 | 3.4287 |
| 2.5001 | 14.92 | 2700 | 3.5152 |
| 2.5718 | 15.19 | 2750 | 3.3615 |
| 2.5734 | 15.47 | 2800 | 3.3193 |
| 2.5112 | 15.75 | 2850 | 3.4028 |
| 2.4499 | 16.02 | 2900 | 3.4374 |
| 2.4631 | 16.3 | 2950 | 3.3910 |
| 2.4246 | 16.57 | 3000 | 3.2926 |
| 2.4075 | 16.85 | 3050 | 3.1869 |
| 2.3691 | 17.13 | 3100 | 3.2002 |
| 2.3557 | 17.4 | 3150 | 3.1995 |
| 2.309 | 17.68 | 3200 | 3.3596 |
| 2.2738 | 17.96 | 3250 | 3.2819 |
| 2.2371 | 18.23 | 3300 | 3.2793 |
| 2.2578 | 18.51 | 3350 | 3.1955 |
| 2.1887 | 18.78 | 3400 | 3.1516 |
| 2.2166 | 19.06 | 3450 | 3.1920 |
| 2.1767 | 19.34 | 3500 | 3.0891 |
| 2.1307 | 19.61 | 3550 | 3.1467 |
| 2.1769 | 19.89 | 3600 | 3.1935 |
| 2.0798 | 20.17 | 3650 | 3.2426 |
| 2.1029 | 20.44 | 3700 | 3.1828 |
| 2.0654 | 20.72 | 3750 | 3.2298 |
| 1.997 | 20.99 | 3800 | 3.2313 |
| 1.9933 | 21.27 | 3850 | 3.1501 |
| 2.0084 | 21.55 | 3900 | 3.0830 |
| 1.9963 | 21.82 | 3950 | 3.2029 |
| 1.889 | 22.1 | 4000 | 3.2676 |
| 2.0014 | 22.38 | 4050 | 3.0189 |
| 1.9031 | 22.65 | 4100 | 3.0549 |
| 1.9464 | 22.93 | 4150 | 3.2659 |
| 1.8972 | 23.2 | 4200 | 3.2271 |
| 1.8549 | 23.48 | 4250 | 3.0585 |
| 1.8106 | 23.76 | 4300 | 3.2286 |
| 1.8222 | 24.03 | 4350 | 3.2233 |
| 1.8537 | 24.31 | 4400 | 2.9525 |
| 1.7717 | 24.59 | 4450 | 3.1129 |
| 1.8045 | 24.86 | 4500 | 3.1795 |
| 1.7783 | 25.14 | 4550 | 3.1206 |
| 1.7119 | 25.41 | 4600 | 3.1325 |
| 1.6936 | 25.69 | 4650 | 3.0850 |
| 1.776 | 25.97 | 4700 | 2.8785 |
| 1.7269 | 26.24 | 4750 | 2.9847 |
| 1.6276 | 26.52 | 4800 | 3.0743 |
| 1.6228 | 26.8 | 4850 | 3.1257 |
| 1.7509 | 27.07 | 4900 | 3.0451 |
| 1.6658 | 27.35 | 4950 | 3.1540 |
| 1.6688 | 27.62 | 5000 | 2.9553 |
| 1.5576 | 27.9 | 5050 | 3.0843 |
| 1.5457 | 28.18 | 5100 | 3.1677 |
| 1.638 | 28.45 | 5150 | 3.0357 |
| 1.5004 | 28.73 | 5200 | 3.0918 |
| 1.6639 | 29.01 | 5250 | 3.0215 |
| 1.5465 | 29.28 | 5300 | 3.1257 |
| 1.4719 | 29.56 | 5350 | 3.0513 |
| 1.5599 | 29.83 | 5400 | 3.0366 |
| 1.5755 | 30.11 | 5450 | 2.9535 |
| 1.496 | 30.39 | 5500 | 3.0343 |
| 1.5915 | 30.66 | 5550 | 3.1121 |
| 1.4198 | 30.94 | 5600 | 3.0673 |
| 1.5062 | 31.22 | 5650 | 2.9743 |
| 1.3817 | 31.49 | 5700 | 3.0471 |
| 1.4361 | 31.77 | 5750 | 2.9827 |
| 1.4624 | 32.04 | 5800 | 3.2212 |
| 1.4895 | 32.32 | 5850 | 3.0745 |
| 1.4598 | 32.6 | 5900 | 3.0424 |
| 1.4379 | 32.87 | 5950 | 3.0214 |
| 1.429 | 33.15 | 6000 | 3.9556 |
| 1.4837 | 33.43 | 6050 | 3.0527 |
| 1.4427 | 33.7 | 6100 | 3.0360 |
| 1.6037 | 33.98 | 6150 | 3.0011 |
| 1.3789 | 34.25 | 6200 | 2.9842 |
| 1.4559 | 34.53 | 6250 | 2.9825 |
| 1.3494 | 34.81 | 6300 | 3.0216 |
| 1.3313 | 35.08 | 6350 | 2.9506 |
| 1.3074 | 35.36 | 6400 | 2.9899 |
| 1.3534 | 35.64 | 6450 | 3.3824 |
| 1.4189 | 35.91 | 6500 | 2.9109 |
| 1.2795 | 36.19 | 6550 | 3.2013 |
| 1.377 | 36.46 | 6600 | 3.1894 |
| 1.3627 | 36.74 | 6650 | 3.0203 |
| 1.3731 | 37.02 | 6700 | 3.0597 |
| 1.2557 | 37.29 | 6750 | 3.1781 |
| 1.362 | 37.57 | 6800 | 3.3320 |
| 1.3448 | 37.85 | 6850 | 3.0893 |
| 1.3337 | 38.12 | 6900 | 3.3698 |
| 1.3455 | 38.4 | 6950 | 3.0614 |
| 1.3397 | 38.67 | 7000 | 3.2179 |
| 1.2439 | 38.95 | 7050 | 3.1908 |
| 1.25 | 39.23 | 7100 | 3.3292 |
| 1.3099 | 39.5 | 7150 | 3.1604 |
| 1.3465 | 39.78 | 7200 | 3.1365 |
| 1.2703 | 40.06 | 7250 | 3.2937 |
| 1.2662 | 40.33 | 7300 | 3.3199 |
| 1.233 | 40.61 | 7350 | 3.1995 |
| 1.2786 | 40.88 | 7400 | 3.1360 |
| 1.3409 | 41.16 | 7450 | 3.1513 |
| 1.2395 | 41.44 | 7500 | 3.2488 |
| 1.1858 | 41.71 | 7550 | 3.3637 |
| 1.3312 | 41.99 | 7600 | 3.2043 |
| 1.2245 | 42.27 | 7650 | 3.3381 |
| 1.2631 | 42.54 | 7700 | 3.3504 |
| 1.257 | 42.82 | 7750 | 3.1843 |
| 1.1715 | 43.09 | 7800 | 3.3320 |
| 1.2017 | 43.37 | 7850 | 3.1980 |
| 1.2711 | 43.65 | 7900 | 3.2528 |
| 1.2091 | 43.92 | 7950 | 3.1928 |
| 1.2574 | 44.2 | 8000 | 3.4765 |
| 1.1915 | 44.48 | 8050 | 3.2830 |
| 1.1754 | 44.75 | 8100 | 3.3196 |
| 1.263 | 45.03 | 8150 | 3.2323 |
| 1.1522 | 45.3 | 8200 | 3.2954 |
| 1.1563 | 45.58 | 8250 | 3.3078 |
| 1.2196 | 45.86 | 8300 | 3.4295 |
| 1.2375 | 46.13 | 8350 | 3.3431 |
| 1.2307 | 46.41 | 8400 | 3.3140 |
| 1.1926 | 46.69 | 8450 | 3.3558 |
| 1.1743 | 46.96 | 8500 | 3.2817 |
| 1.1721 | 47.24 | 8550 | 3.2732 |
| 1.192 | 47.51 | 8600 | 3.3022 |
| 1.1642 | 47.79 | 8650 | 3.3513 |
| 1.2049 | 48.07 | 8700 | 3.3494 |
| 1.1157 | 48.34 | 8750 | 3.3900 |
| 1.2006 | 48.62 | 8800 | 3.3109 |
| 1.1384 | 48.9 | 8850 | 3.3915 |
| 1.1437 | 49.17 | 8900 | 3.4193 |
| 1.2226 | 49.45 | 8950 | 3.3782 |
| 1.1074 | 49.72 | 9000 | 3.3965 |
| 1.1955 | 50.0 | 9050 | 3.3973 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0