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layoutlmv2-base-uncased_finetuned_docvqa

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  2. pytorch_model.bin +1 -1
  3. runs/Aug14_19-19-30_c7eddaa25bde/events.out.tfevents.1692041074.c7eddaa25bde.44492.0 +2 -2
README.md ADDED
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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: 4.5570
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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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+ | 5.2892 | 0.22 | 50 | 4.6213 |
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+ | 4.4958 | 0.44 | 100 | 4.1766 |
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+ | 4.1602 | 0.66 | 150 | 3.8565 |
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+ | 3.9538 | 0.88 | 200 | 3.5805 |
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+ | 3.5903 | 1.11 | 250 | 3.4594 |
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+ | 3.1555 | 1.33 | 300 | 3.1305 |
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+ | 3.0041 | 1.55 | 350 | 2.9394 |
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+ | 2.8133 | 1.77 | 400 | 2.7611 |
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+ | 2.5517 | 1.99 | 450 | 2.6359 |
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+ | 1.9883 | 2.21 | 500 | 2.6822 |
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+ | 2.1057 | 2.43 | 550 | 2.4430 |
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+ | 2.0872 | 2.65 | 600 | 2.4388 |
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+ | 1.7591 | 2.88 | 650 | 2.3726 |
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+ | 1.5244 | 3.1 | 700 | 2.3325 |
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+ | 1.5257 | 3.32 | 750 | 2.7668 |
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+ | 1.3295 | 3.54 | 800 | 2.2800 |
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+ | 1.3167 | 3.76 | 850 | 2.3108 |
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+ | 1.2051 | 3.98 | 900 | 2.6429 |
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+ | 1.0532 | 4.2 | 950 | 3.1483 |
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+ | 1.0167 | 4.42 | 1000 | 2.2237 |
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+ | 1.1105 | 4.65 | 1050 | 2.3531 |
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+ | 1.006 | 4.87 | 1100 | 2.8026 |
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+ | 0.8193 | 5.09 | 1150 | 3.0897 |
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+ | 0.7502 | 5.31 | 1200 | 3.5147 |
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+ | 0.9308 | 5.53 | 1250 | 2.5877 |
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+ | 0.6495 | 5.75 | 1300 | 2.8749 |
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+ | 0.7933 | 5.97 | 1350 | 2.7216 |
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+ | 0.7199 | 6.19 | 1400 | 3.1257 |
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+ | 0.5458 | 6.42 | 1450 | 3.5710 |
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+ | 0.7406 | 6.64 | 1500 | 2.5855 |
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+ | 0.5537 | 6.86 | 1550 | 2.9661 |
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+ | 0.493 | 7.08 | 1600 | 3.3691 |
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+ | 0.4938 | 7.3 | 1650 | 3.1937 |
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+ | 0.5407 | 7.52 | 1700 | 3.3585 |
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+ | 0.5243 | 7.74 | 1750 | 2.7650 |
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+ | 0.4177 | 7.96 | 1800 | 3.2618 |
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+ | 0.4094 | 8.19 | 1850 | 3.2418 |
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+ | 0.4061 | 8.41 | 1900 | 3.2710 |
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+ | 0.3248 | 8.63 | 1950 | 3.3732 |
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+ | 0.3787 | 8.85 | 2000 | 3.3467 |
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+ | 0.2709 | 9.07 | 2050 | 3.1261 |
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+ | 0.2655 | 9.29 | 2100 | 3.4552 |
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+ | 0.2552 | 9.51 | 2150 | 3.4344 |
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+ | 0.3321 | 9.73 | 2200 | 3.5558 |
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+ | 0.4165 | 9.96 | 2250 | 3.0775 |
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+ | 0.2052 | 10.18 | 2300 | 3.4985 |
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+ | 0.2642 | 10.4 | 2350 | 3.8930 |
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+ | 0.2461 | 10.62 | 2400 | 3.7029 |
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+ | 0.2895 | 10.84 | 2450 | 3.4143 |
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+ | 0.1041 | 11.06 | 2500 | 3.6652 |
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+ | 0.1316 | 11.28 | 2550 | 3.8867 |
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+ | 0.1165 | 11.5 | 2600 | 4.1669 |
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+ | 0.1925 | 11.73 | 2650 | 4.3376 |
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+ | 0.371 | 11.95 | 2700 | 3.9125 |
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+ | 0.1793 | 12.17 | 2750 | 4.0051 |
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+ | 0.1771 | 12.39 | 2800 | 3.8161 |
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+ | 0.2158 | 12.61 | 2850 | 3.8786 |
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+ | 0.1838 | 12.83 | 2900 | 3.7687 |
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+ | 0.1364 | 13.05 | 2950 | 4.1103 |
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+ | 0.0364 | 13.27 | 3000 | 4.1468 |
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+ | 0.2488 | 13.5 | 3050 | 4.1149 |
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+ | 0.1046 | 13.72 | 3100 | 4.1887 |
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+ | 0.1254 | 13.94 | 3150 | 4.0097 |
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+ | 0.033 | 14.16 | 3200 | 4.3023 |
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+ | 0.0373 | 14.38 | 3250 | 4.3530 |
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+ | 0.0911 | 14.6 | 3300 | 4.2621 |
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+ | 0.1014 | 14.82 | 3350 | 3.8815 |
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+ | 0.0238 | 15.04 | 3400 | 4.4097 |
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+ | 0.1141 | 15.27 | 3450 | 4.4720 |
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+ | 0.0278 | 15.49 | 3500 | 4.4407 |
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+ | 0.0673 | 15.71 | 3550 | 4.4176 |
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+ | 0.0691 | 15.93 | 3600 | 4.4863 |
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+ | 0.0379 | 16.15 | 3650 | 4.6924 |
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+ | 0.057 | 16.37 | 3700 | 4.5305 |
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+ | 0.1087 | 16.59 | 3750 | 4.5050 |
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+ | 0.0047 | 16.81 | 3800 | 4.5652 |
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+ | 0.051 | 17.04 | 3850 | 4.5482 |
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+ | 0.0054 | 17.26 | 3900 | 4.5337 |
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+ | 0.0542 | 17.48 | 3950 | 4.5370 |
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+ | 0.0132 | 17.7 | 4000 | 4.4744 |
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+ | 0.0698 | 17.92 | 4050 | 4.4535 |
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+ | 0.0214 | 18.14 | 4100 | 4.4660 |
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+ | 0.033 | 18.36 | 4150 | 4.4818 |
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+ | 0.029 | 18.58 | 4200 | 4.5033 |
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+ | 0.0283 | 18.81 | 4250 | 4.4825 |
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+ | 0.013 | 19.03 | 4300 | 4.4794 |
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+ | 0.0538 | 19.25 | 4350 | 4.5183 |
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+ | 0.0171 | 19.47 | 4400 | 4.5373 |
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+ | 0.0122 | 19.69 | 4450 | 4.5501 |
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+ | 0.0222 | 19.91 | 4500 | 4.5570 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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+ - Tokenizers 0.13.3
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