---
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: 5.0085

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3352        | 0.22  | 50   | 4.5120          |
| 4.3566        | 0.44  | 100  | 4.0171          |
| 3.9989        | 0.66  | 150  | 3.9234          |
| 3.8014        | 0.88  | 200  | 3.5051          |
| 3.5509        | 1.11  | 250  | 3.5408          |
| 3.1372        | 1.33  | 300  | 3.2247          |
| 2.9307        | 1.55  | 350  | 3.1225          |
| 2.928         | 1.77  | 400  | 2.9461          |
| 2.7004        | 1.99  | 450  | 2.5206          |
| 2.1271        | 2.21  | 500  | 2.6079          |
| 2.1387        | 2.43  | 550  | 2.8524          |
| 1.9593        | 2.65  | 600  | 2.8749          |
| 2.0105        | 2.88  | 650  | 2.6666          |
| 1.84          | 3.1   | 700  | 3.0599          |
| 1.9359        | 3.32  | 750  | 3.0472          |
| 1.547         | 3.54  | 800  | 2.2308          |
| 1.4161        | 3.76  | 850  | 2.2889          |
| 2.1804        | 3.98  | 900  | 2.1462          |
| 1.0261        | 4.2   | 950  | 2.9056          |
| 1.392         | 4.42  | 1000 | 3.0021          |
| 1.3816        | 4.65  | 1050 | 2.6913          |
| 1.0117        | 4.87  | 1100 | 2.8484          |
| 1.0094        | 5.09  | 1150 | 2.6936          |
| 0.7316        | 5.31  | 1200 | 2.9901          |
| 0.9172        | 5.53  | 1250 | 2.6366          |
| 0.8608        | 5.75  | 1300 | 2.8584          |
| 0.7116        | 5.97  | 1350 | 3.1944          |
| 0.321         | 6.19  | 1400 | 3.4703          |
| 0.6663        | 6.42  | 1450 | 3.0456          |
| 0.6319        | 6.64  | 1500 | 3.3318          |
| 0.7001        | 6.86  | 1550 | 3.1439          |
| 0.5952        | 7.08  | 1600 | 3.3220          |
| 0.39          | 7.3   | 1650 | 3.8266          |
| 0.434         | 7.52  | 1700 | 3.8287          |
| 0.7599        | 7.74  | 1750 | 3.4079          |
| 0.52          | 7.96  | 1800 | 3.3982          |
| 0.5257        | 8.19  | 1850 | 3.5208          |
| 0.4304        | 8.41  | 1900 | 3.8404          |
| 0.4213        | 8.63  | 1950 | 3.9974          |
| 0.3033        | 8.85  | 2000 | 3.9492          |
| 0.2947        | 9.07  | 2050 | 3.9279          |
| 0.2285        | 9.29  | 2100 | 3.5652          |
| 0.3472        | 9.51  | 2150 | 3.5741          |
| 0.2644        | 9.73  | 2200 | 3.8685          |
| 0.3667        | 9.96  | 2250 | 3.5242          |
| 0.1528        | 10.18 | 2300 | 3.5848          |
| 0.1489        | 10.4  | 2350 | 3.8603          |
| 0.1984        | 10.62 | 2400 | 3.6773          |
| 0.3131        | 10.84 | 2450 | 3.7021          |
| 0.1866        | 11.06 | 2500 | 3.8918          |
| 0.1908        | 11.28 | 2550 | 3.9479          |
| 0.1955        | 11.5  | 2600 | 3.9596          |
| 0.1382        | 11.73 | 2650 | 4.1168          |
| 0.2528        | 11.95 | 2700 | 4.1007          |
| 0.0538        | 12.17 | 2750 | 4.2003          |
| 0.1354        | 12.39 | 2800 | 4.3118          |
| 0.1218        | 12.61 | 2850 | 4.1494          |
| 0.1956        | 12.83 | 2900 | 4.1475          |
| 0.0691        | 13.05 | 2950 | 4.4141          |
| 0.0526        | 13.27 | 3000 | 4.7115          |
| 0.0984        | 13.5  | 3050 | 4.6013          |
| 0.1828        | 13.72 | 3100 | 4.2457          |
| 0.0906        | 13.94 | 3150 | 4.4969          |
| 0.025         | 14.16 | 3200 | 4.6981          |
| 0.0149        | 14.38 | 3250 | 4.8642          |
| 0.123         | 14.6  | 3300 | 4.5326          |
| 0.0876        | 14.82 | 3350 | 4.5953          |
| 0.0771        | 15.04 | 3400 | 4.4175          |
| 0.066         | 15.27 | 3450 | 4.6324          |
| 0.0542        | 15.49 | 3500 | 4.5058          |
| 0.0293        | 15.71 | 3550 | 4.7244          |
| 0.0428        | 15.93 | 3600 | 4.9415          |
| 0.009         | 16.15 | 3650 | 4.9592          |
| 0.0715        | 16.37 | 3700 | 4.9211          |
| 0.0044        | 16.59 | 3750 | 4.9854          |
| 0.0767        | 16.81 | 3800 | 4.7985          |
| 0.0356        | 17.04 | 3850 | 4.7618          |
| 0.0562        | 17.26 | 3900 | 4.9239          |
| 0.0085        | 17.48 | 3950 | 4.9837          |
| 0.0114        | 17.7  | 4000 | 5.0808          |
| 0.0057        | 17.92 | 4050 | 5.0377          |
| 0.0306        | 18.14 | 4100 | 5.0137          |
| 0.0426        | 18.36 | 4150 | 4.9367          |
| 0.0429        | 18.58 | 4200 | 5.0050          |
| 0.0081        | 18.81 | 4250 | 4.9806          |
| 0.0168        | 19.03 | 4300 | 4.9902          |
| 0.0074        | 19.25 | 4350 | 4.9939          |
| 0.0075        | 19.47 | 4400 | 4.9986          |
| 0.0307        | 19.69 | 4450 | 5.0095          |
| 0.02          | 19.91 | 4500 | 5.0085          |


### Framework versions

- Transformers 4.29.1
- Pytorch 1.12.1
- Datasets 2.11.0
- Tokenizers 0.11.0