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--- |
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license: cc-by-nc-sa-4.0 |
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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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<!-- 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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# layoutlmv2-base-uncased_finetuned_docvqa |
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This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.6207 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 5.3769 | 0.22 | 50 | 4.5459 | |
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| 4.3879 | 0.44 | 100 | 4.0099 | |
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| 3.9828 | 0.66 | 150 | 3.8817 | |
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| 3.7387 | 0.88 | 200 | 3.5334 | |
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| 3.4702 | 1.11 | 250 | 3.4670 | |
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| 3.0784 | 1.33 | 300 | 3.5327 | |
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| 2.8014 | 1.55 | 350 | 2.8722 | |
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| 2.673 | 1.77 | 400 | 2.8246 | |
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| 2.4546 | 1.99 | 450 | 2.7050 | |
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| 2.094 | 2.21 | 500 | 2.7639 | |
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| 1.9542 | 2.43 | 550 | 2.3472 | |
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| 1.8966 | 2.65 | 600 | 2.4508 | |
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| 1.7745 | 2.88 | 650 | 2.2813 | |
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| 1.4371 | 3.1 | 700 | 2.5234 | |
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| 1.3257 | 3.32 | 750 | 2.4358 | |
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| 1.3269 | 3.54 | 800 | 2.3044 | |
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| 1.4035 | 3.76 | 850 | 2.3546 | |
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| 1.6189 | 3.98 | 900 | 2.0838 | |
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| 0.9209 | 4.2 | 950 | 2.3836 | |
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| 0.8405 | 4.42 | 1000 | 3.1673 | |
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| 0.9808 | 4.65 | 1050 | 2.8038 | |
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| 0.8978 | 4.87 | 1100 | 2.7652 | |
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| 0.8733 | 5.09 | 1150 | 3.0965 | |
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| 0.7449 | 5.31 | 1200 | 2.9948 | |
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| 0.8173 | 5.53 | 1250 | 2.8631 | |
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| 0.8322 | 5.75 | 1300 | 2.6144 | |
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| 0.7147 | 5.97 | 1350 | 3.2041 | |
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| 0.6495 | 6.19 | 1400 | 3.3711 | |
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| 0.5458 | 6.42 | 1450 | 3.5480 | |
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| 0.5624 | 6.64 | 1500 | 3.3366 | |
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| 0.5736 | 6.86 | 1550 | 2.9356 | |
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| 0.2921 | 7.08 | 1600 | 3.4028 | |
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| 0.3883 | 7.3 | 1650 | 3.4411 | |
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| 0.3614 | 7.52 | 1700 | 3.2267 | |
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| 0.4376 | 7.74 | 1750 | 3.2137 | |
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| 0.4849 | 7.96 | 1800 | 3.6388 | |
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| 0.5035 | 8.19 | 1850 | 4.0089 | |
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| 0.36 | 8.41 | 1900 | 3.7903 | |
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| 0.2238 | 8.63 | 1950 | 3.7131 | |
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| 0.276 | 8.85 | 2000 | 3.8541 | |
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| 0.2661 | 9.07 | 2050 | 3.5220 | |
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| 0.291 | 9.29 | 2100 | 3.9075 | |
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| 0.3767 | 9.51 | 2150 | 3.4267 | |
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| 0.1326 | 9.73 | 2200 | 3.9069 | |
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| 0.2365 | 9.96 | 2250 | 3.5183 | |
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| 0.2676 | 10.18 | 2300 | 3.4975 | |
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| 0.131 | 10.4 | 2350 | 3.8138 | |
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| 0.1818 | 10.62 | 2400 | 3.6213 | |
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| 0.2988 | 10.84 | 2450 | 3.7380 | |
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| 0.0511 | 11.06 | 2500 | 4.1506 | |
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| 0.1909 | 11.28 | 2550 | 3.7369 | |
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| 0.0854 | 11.5 | 2600 | 3.8751 | |
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| 0.1291 | 11.73 | 2650 | 3.7143 | |
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| 0.1896 | 11.95 | 2700 | 4.0645 | |
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| 0.0746 | 12.17 | 2750 | 4.0363 | |
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| 0.1254 | 12.39 | 2800 | 4.0050 | |
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| 0.1588 | 12.61 | 2850 | 4.1739 | |
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| 0.1622 | 12.83 | 2900 | 4.2698 | |
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| 0.047 | 13.05 | 2950 | 4.5366 | |
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| 0.1023 | 13.27 | 3000 | 4.3094 | |
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| 0.1195 | 13.5 | 3050 | 4.3112 | |
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| 0.0753 | 13.72 | 3100 | 4.3137 | |
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| 0.0545 | 13.94 | 3150 | 4.4992 | |
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| 0.0771 | 14.16 | 3200 | 4.4759 | |
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| 0.0648 | 14.38 | 3250 | 4.5531 | |
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| 0.0935 | 14.6 | 3300 | 4.3725 | |
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| 0.1032 | 14.82 | 3350 | 4.3321 | |
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| 0.0199 | 15.04 | 3400 | 4.3527 | |
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| 0.0498 | 15.27 | 3450 | 4.2477 | |
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| 0.0688 | 15.49 | 3500 | 4.2340 | |
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| 0.0271 | 15.71 | 3550 | 4.2386 | |
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| 0.0176 | 15.93 | 3600 | 4.4715 | |
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| 0.0319 | 16.15 | 3650 | 4.5608 | |
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| 0.0061 | 16.37 | 3700 | 4.5767 | |
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| 0.0043 | 16.59 | 3750 | 4.6581 | |
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| 0.071 | 16.81 | 3800 | 4.5622 | |
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| 0.0689 | 17.04 | 3850 | 4.5067 | |
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| 0.0328 | 17.26 | 3900 | 4.4449 | |
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| 0.0784 | 17.48 | 3950 | 4.3684 | |
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| 0.0387 | 17.7 | 4000 | 4.4261 | |
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| 0.0107 | 17.92 | 4050 | 4.5190 | |
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| 0.0056 | 18.14 | 4100 | 4.4963 | |
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| 0.0261 | 18.36 | 4150 | 4.5995 | |
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| 0.0061 | 18.58 | 4200 | 4.6121 | |
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| 0.0354 | 18.81 | 4250 | 4.5794 | |
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| 0.0111 | 19.03 | 4300 | 4.5977 | |
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| 0.022 | 19.25 | 4350 | 4.6151 | |
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| 0.0628 | 19.47 | 4400 | 4.6061 | |
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| 0.0186 | 19.69 | 4450 | 4.6180 | |
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| 0.0077 | 19.91 | 4500 | 4.6207 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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