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layoutlmv2-base-uncased_finetuned_docvqa

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+ ---
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+ license: cc-by-nc-sa-4.0
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+ base_model: microsoft/layoutlmv2-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: layoutlmv2-base-uncased_finetuned_docvqa
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # layoutlmv2-base-uncased_finetuned_docvqa
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+
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+ This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 5.2363
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 20
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-------:|:----:|:---------------:|
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+ | 4.0408 | 0.2212 | 50 | 4.0001 |
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+ | 4.1144 | 0.4425 | 100 | 3.7920 |
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+ | 3.8854 | 0.6637 | 150 | 3.6503 |
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+ | 3.6048 | 0.8850 | 200 | 3.3228 |
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+ | 3.1846 | 1.1062 | 250 | 3.6110 |
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+ | 2.917 | 1.3274 | 300 | 2.9913 |
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+ | 2.8848 | 1.5487 | 350 | 2.7110 |
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+ | 2.5842 | 1.7699 | 400 | 2.4111 |
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+ | 2.1162 | 1.9912 | 450 | 2.4839 |
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+ | 1.8347 | 2.2124 | 500 | 2.7160 |
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+ | 1.786 | 2.4336 | 550 | 2.5238 |
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+ | 1.8828 | 2.6549 | 600 | 2.4274 |
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+ | 1.8181 | 2.8761 | 650 | 2.5544 |
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+ | 1.5656 | 3.0973 | 700 | 2.4362 |
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+ | 1.4265 | 3.3186 | 750 | 2.9550 |
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+ | 1.4967 | 3.5398 | 800 | 3.2754 |
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+ | 1.2732 | 3.7611 | 850 | 3.0296 |
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+ | 1.3162 | 3.9823 | 900 | 2.6941 |
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+ | 1.0837 | 4.2035 | 950 | 2.9119 |
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+ | 1.1094 | 4.4248 | 1000 | 3.0181 |
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+ | 1.1846 | 4.6460 | 1050 | 2.6419 |
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+ | 1.5768 | 4.8673 | 1100 | 4.0184 |
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+ | 1.4084 | 5.0885 | 1150 | 3.1371 |
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+ | 0.9783 | 5.3097 | 1200 | 2.9210 |
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+ | 0.984 | 5.5310 | 1250 | 3.0042 |
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+ | 0.7546 | 5.7522 | 1300 | 3.1277 |
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+ | 0.799 | 5.9735 | 1350 | 3.0501 |
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+ | 0.6629 | 6.1947 | 1400 | 3.2626 |
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+ | 0.8973 | 6.4159 | 1450 | 3.2922 |
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+ | 0.6816 | 6.6372 | 1500 | 3.0462 |
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+ | 0.539 | 6.8584 | 1550 | 3.1018 |
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+ | 0.6871 | 7.0796 | 1600 | 3.1925 |
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+ | 0.4569 | 7.3009 | 1650 | 3.2120 |
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+ | 0.6451 | 7.5221 | 1700 | 2.9812 |
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+ | 0.5579 | 7.7434 | 1750 | 3.3052 |
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+ | 0.4851 | 7.9646 | 1800 | 4.1491 |
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+ | 0.5851 | 8.1858 | 1850 | 3.5338 |
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+ | 0.4344 | 8.4071 | 1900 | 3.4542 |
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+ | 0.5021 | 8.6283 | 1950 | 3.2402 |
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+ | 0.4699 | 8.8496 | 2000 | 3.3066 |
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+ | 0.4668 | 9.0708 | 2050 | 3.6041 |
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+ | 0.2258 | 9.2920 | 2100 | 3.6862 |
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+ | 0.4708 | 9.5133 | 2150 | 3.7622 |
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+ | 0.3933 | 9.7345 | 2200 | 3.7370 |
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+ | 0.3858 | 9.9558 | 2250 | 3.3631 |
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+ | 0.3359 | 10.1770 | 2300 | 3.6203 |
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+ | 0.2365 | 10.3982 | 2350 | 3.7388 |
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+ | 0.3147 | 10.6195 | 2400 | 3.8653 |
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+ | 0.3401 | 10.8407 | 2450 | 4.0243 |
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+ | 0.1644 | 11.0619 | 2500 | 4.1857 |
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+ | 0.142 | 11.2832 | 2550 | 4.3611 |
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+ | 0.266 | 11.5044 | 2600 | 4.2761 |
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+ | 0.1592 | 11.7257 | 2650 | 4.3012 |
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+ | 0.1126 | 11.9469 | 2700 | 4.3518 |
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+ | 0.1409 | 12.1681 | 2750 | 4.4466 |
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+ | 0.0731 | 12.3894 | 2800 | 4.3459 |
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+ | 0.1243 | 12.6106 | 2850 | 4.3446 |
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+ | 0.2672 | 12.8319 | 2900 | 4.3548 |
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+ | 0.228 | 13.0531 | 2950 | 4.1020 |
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+ | 0.0622 | 13.2743 | 3000 | 4.4363 |
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+ | 0.1287 | 13.4956 | 3050 | 4.5345 |
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+ | 0.1974 | 13.7168 | 3100 | 4.6727 |
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+ | 0.2213 | 13.9381 | 3150 | 4.3807 |
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+ | 0.1551 | 14.1593 | 3200 | 4.4805 |
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+ | 0.1295 | 14.3805 | 3250 | 4.7027 |
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+ | 0.0664 | 14.6018 | 3300 | 4.7583 |
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+ | 0.1159 | 14.8230 | 3350 | 4.3252 |
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+ | 0.02 | 15.0442 | 3400 | 4.6594 |
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+ | 0.0438 | 15.2655 | 3450 | 4.8679 |
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+ | 0.0495 | 15.4867 | 3500 | 5.1235 |
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+ | 0.1143 | 15.7080 | 3550 | 5.1614 |
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+ | 0.1405 | 15.9292 | 3600 | 5.1302 |
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+ | 0.0351 | 16.1504 | 3650 | 5.0780 |
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+ | 0.1258 | 16.3717 | 3700 | 5.1000 |
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+ | 0.0387 | 16.5929 | 3750 | 5.0849 |
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+ | 0.0809 | 16.8142 | 3800 | 4.9809 |
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+ | 0.0955 | 17.0354 | 3850 | 5.0030 |
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+ | 0.0347 | 17.2566 | 3900 | 5.0040 |
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+ | 0.0716 | 17.4779 | 3950 | 4.9608 |
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+ | 0.0417 | 17.6991 | 4000 | 5.0922 |
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+ | 0.1394 | 17.9204 | 4050 | 5.1081 |
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+ | 0.0612 | 18.1416 | 4100 | 5.1859 |
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+ | 0.0057 | 18.3628 | 4150 | 5.2126 |
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+ | 0.0965 | 18.5841 | 4200 | 5.1589 |
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+ | 0.0131 | 18.8053 | 4250 | 5.1224 |
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+ | 0.0922 | 19.0265 | 4300 | 5.1521 |
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+ | 0.0353 | 19.2478 | 4350 | 5.1961 |
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+ | 0.0351 | 19.4690 | 4400 | 5.2249 |
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+ | 0.0161 | 19.6903 | 4450 | 5.2304 |
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+ | 0.0095 | 19.9115 | 4500 | 5.2363 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.0
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+ - Pytorch 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1