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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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<!-- 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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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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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| 0.0 | 0.22 | 50 | nan | |
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| 0.0 | 0.44 | 100 | nan | |
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| 0.0 | 0.66 | 150 | nan | |
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| 0.0 | 0.88 | 200 | nan | |
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| 0.0 | 1.11 | 250 | nan | |
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| 0.0 | 1.33 | 300 | nan | |
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| 0.0 | 1.55 | 350 | nan | |
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| 0.0 | 1.77 | 400 | nan | |
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| 0.0 | 1.99 | 450 | nan | |
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| 0.0 | 2.21 | 500 | nan | |
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| 0.0 | 2.43 | 550 | nan | |
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| 0.0 | 2.65 | 600 | nan | |
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| 0.0 | 2.88 | 650 | nan | |
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| 0.0 | 3.1 | 700 | nan | |
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| 0.0 | 3.32 | 750 | nan | |
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| 0.0 | 3.54 | 800 | nan | |
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| 0.0 | 3.76 | 850 | nan | |
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| 0.0 | 3.98 | 900 | nan | |
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| 0.0 | 4.2 | 950 | nan | |
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| 0.0 | 4.42 | 1000 | nan | |
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| 0.0 | 4.65 | 1050 | nan | |
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| 0.0 | 4.87 | 1100 | nan | |
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| 0.0 | 5.09 | 1150 | nan | |
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| 0.0 | 5.31 | 1200 | nan | |
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| 0.0 | 5.53 | 1250 | nan | |
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| 0.0 | 5.75 | 1300 | nan | |
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| 0.0 | 5.97 | 1350 | nan | |
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| 0.0 | 6.19 | 1400 | nan | |
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| 0.0 | 6.42 | 1450 | nan | |
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| 0.0 | 6.64 | 1500 | nan | |
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| 0.0 | 6.86 | 1550 | nan | |
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| 0.0 | 7.08 | 1600 | nan | |
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| 0.0 | 7.3 | 1650 | nan | |
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| 0.0 | 7.52 | 1700 | nan | |
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| 0.0 | 7.74 | 1750 | nan | |
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| 0.0 | 7.96 | 1800 | nan | |
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| 0.0 | 8.19 | 1850 | nan | |
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| 0.0 | 8.41 | 1900 | nan | |
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| 0.0 | 8.63 | 1950 | nan | |
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| 0.0 | 8.85 | 2000 | nan | |
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| 0.0 | 9.07 | 2050 | nan | |
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| 0.0 | 9.29 | 2100 | nan | |
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| 0.0 | 9.51 | 2150 | nan | |
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| 0.0 | 9.73 | 2200 | nan | |
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| 0.0 | 9.96 | 2250 | nan | |
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| 0.0 | 10.18 | 2300 | nan | |
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| 0.0 | 10.4 | 2350 | nan | |
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| 0.0 | 10.62 | 2400 | nan | |
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| 0.0 | 10.84 | 2450 | nan | |
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| 0.0 | 11.06 | 2500 | nan | |
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| 0.0 | 11.28 | 2550 | nan | |
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| 0.0 | 11.5 | 2600 | nan | |
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| 0.0 | 11.73 | 2650 | nan | |
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| 0.0 | 11.95 | 2700 | nan | |
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| 0.0 | 12.17 | 2750 | nan | |
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| 0.0 | 12.39 | 2800 | nan | |
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| 0.0 | 12.61 | 2850 | nan | |
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| 0.0 | 12.83 | 2900 | nan | |
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| 0.0 | 13.05 | 2950 | nan | |
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| 0.0 | 13.27 | 3000 | nan | |
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| 0.0 | 13.5 | 3050 | nan | |
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| 0.0 | 13.72 | 3100 | nan | |
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| 0.0 | 13.94 | 3150 | nan | |
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| 0.0 | 14.16 | 3200 | nan | |
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| 0.0 | 14.38 | 3250 | nan | |
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| 0.0 | 14.6 | 3300 | nan | |
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| 0.0 | 14.82 | 3350 | nan | |
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| 0.0 | 15.04 | 3400 | nan | |
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| 0.0 | 15.27 | 3450 | nan | |
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| 0.0 | 15.49 | 3500 | nan | |
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| 0.0 | 15.71 | 3550 | nan | |
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| 0.0 | 15.93 | 3600 | nan | |
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| 0.0 | 16.15 | 3650 | nan | |
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| 0.0 | 16.37 | 3700 | nan | |
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| 0.0 | 16.59 | 3750 | nan | |
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| 0.0 | 16.81 | 3800 | nan | |
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| 0.0 | 17.04 | 3850 | nan | |
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| 0.0 | 17.26 | 3900 | nan | |
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| 0.0 | 17.48 | 3950 | nan | |
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| 0.0 | 17.7 | 4000 | nan | |
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| 0.0 | 17.92 | 4050 | nan | |
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| 0.0 | 18.14 | 4100 | nan | |
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| 0.0 | 18.36 | 4150 | nan | |
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| 0.0 | 18.58 | 4200 | nan | |
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| 0.0 | 18.81 | 4250 | nan | |
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| 0.0 | 19.03 | 4300 | nan | |
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| 0.0 | 19.25 | 4350 | nan | |
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| 0.0 | 19.47 | 4400 | nan | |
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| 0.0 | 19.69 | 4450 | nan | |
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| 0.0 | 19.91 | 4500 | nan | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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