---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-uncased_finetuned_docvqa
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# layoutlmv2-base-uncased_finetuned_docvqa

This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8430

## 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-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3379        | 0.22  | 50   | 4.6257          |
| 4.4305        | 0.44  | 100  | 4.2230          |
| 4.0588        | 0.66  | 150  | 3.9539          |
| 3.7822        | 0.88  | 200  | 3.7040          |
| 3.4957        | 1.11  | 250  | 3.4754          |
| 3.2417        | 1.33  | 300  | 3.1954          |
| 2.8607        | 1.55  | 350  | 2.8809          |
| 2.6602        | 1.77  | 400  | 2.9741          |
| 2.621         | 1.99  | 450  | 2.8658          |
| 2.1733        | 2.21  | 500  | 2.7248          |
| 2.106         | 2.43  | 550  | 2.4072          |
| 1.8389        | 2.65  | 600  | 2.4147          |
| 1.7862        | 2.88  | 650  | 2.2116          |
| 1.4224        | 3.1   | 700  | 2.4379          |
| 1.4773        | 3.32  | 750  | 2.4346          |
| 1.2225        | 3.54  | 800  | 2.5779          |
| 1.5368        | 3.76  | 850  | 2.4343          |
| 1.479         | 3.98  | 900  | 2.1432          |
| 0.7982        | 4.2   | 950  | 2.5897          |
| 0.8336        | 4.42  | 1000 | 2.8477          |
| 1.0647        | 4.65  | 1050 | 2.7111          |
| 0.8795        | 4.87  | 1100 | 2.5601          |
| 0.9265        | 5.09  | 1150 | 2.9547          |
| 0.7111        | 5.31  | 1200 | 3.1621          |
| 0.7244        | 5.53  | 1250 | 2.7862          |
| 0.9501        | 5.75  | 1300 | 2.4007          |
| 0.7424        | 5.97  | 1350 | 2.9918          |
| 0.4422        | 6.19  | 1400 | 3.5247          |
| 0.5952        | 6.42  | 1450 | 2.8743          |
| 0.7173        | 6.64  | 1500 | 2.7440          |
| 0.6311        | 6.86  | 1550 | 2.9658          |
| 0.393         | 7.08  | 1600 | 3.0994          |
| 0.3655        | 7.3   | 1650 | 3.3074          |
| 0.3432        | 7.52  | 1700 | 3.1921          |
| 0.5986        | 7.74  | 1750 | 3.3517          |
| 0.5456        | 7.96  | 1800 | 3.1552          |
| 0.565         | 8.19  | 1850 | 2.9922          |
| 0.3902        | 8.41  | 1900 | 3.6814          |
| 0.3408        | 8.63  | 1950 | 3.2820          |
| 0.241         | 8.85  | 2000 | 3.5644          |
| 0.3172        | 9.07  | 2050 | 3.4752          |
| 0.294         | 9.29  | 2100 | 3.7023          |
| 0.2993        | 9.51  | 2150 | 3.5031          |
| 0.0928        | 9.73  | 2200 | 4.0305          |
| 0.4598        | 9.96  | 2250 | 3.4260          |
| 0.2795        | 10.18 | 2300 | 3.2730          |
| 0.0887        | 10.4  | 2350 | 3.7174          |
| 0.3682        | 10.62 | 2400 | 3.4060          |
| 0.1924        | 10.84 | 2450 | 4.1368          |
| 0.1825        | 11.06 | 2500 | 4.1640          |
| 0.1987        | 11.28 | 2550 | 3.9908          |
| 0.0875        | 11.5  | 2600 | 4.1872          |
| 0.1719        | 11.73 | 2650 | 3.9948          |
| 0.2844        | 11.95 | 2700 | 4.1731          |
| 0.1085        | 12.17 | 2750 | 3.9568          |
| 0.1496        | 12.39 | 2800 | 3.9272          |
| 0.0701        | 12.61 | 2850 | 4.2957          |
| 0.1617        | 12.83 | 2900 | 4.2806          |
| 0.0934        | 13.05 | 2950 | 4.3200          |
| 0.0405        | 13.27 | 3000 | 4.1869          |
| 0.0898        | 13.5  | 3050 | 4.1207          |
| 0.189         | 13.72 | 3100 | 4.4437          |
| 0.0798        | 13.94 | 3150 | 4.6480          |
| 0.1199        | 14.16 | 3200 | 4.4105          |
| 0.0922        | 14.38 | 3250 | 4.4321          |
| 0.1556        | 14.6  | 3300 | 4.3353          |
| 0.1933        | 14.82 | 3350 | 4.0635          |
| 0.0164        | 15.04 | 3400 | 4.1792          |
| 0.064         | 15.27 | 3450 | 4.2202          |
| 0.0914        | 15.49 | 3500 | 4.2382          |
| 0.0287        | 15.71 | 3550 | 4.4255          |
| 0.1054        | 15.93 | 3600 | 4.5788          |
| 0.0306        | 16.15 | 3650 | 4.7566          |
| 0.0297        | 16.37 | 3700 | 4.6610          |
| 0.0529        | 16.59 | 3750 | 4.6494          |
| 0.0729        | 16.81 | 3800 | 4.6314          |
| 0.0388        | 17.04 | 3850 | 4.6675          |
| 0.0207        | 17.26 | 3900 | 4.7816          |
| 0.0889        | 17.48 | 3950 | 4.6941          |
| 0.0058        | 17.7  | 4000 | 4.6818          |
| 0.0068        | 17.92 | 4050 | 4.7755          |
| 0.0222        | 18.14 | 4100 | 4.7658          |
| 0.1152        | 18.36 | 4150 | 4.8247          |
| 0.0181        | 18.58 | 4200 | 4.8290          |
| 0.0349        | 18.81 | 4250 | 4.7989          |
| 0.0165        | 19.03 | 4300 | 4.8208          |
| 0.029         | 19.25 | 4350 | 4.8401          |
| 0.0073        | 19.47 | 4400 | 4.8544          |
| 0.0277        | 19.69 | 4450 | 4.8356          |
| 0.0164        | 19.91 | 4500 | 4.8430          |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2