---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9097

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3187        | 0.22  | 50   | 4.6843          |
| 4.5031        | 0.44  | 100  | 4.1274          |
| 4.128         | 0.66  | 150  | 3.7921          |
| 3.9091        | 0.88  | 200  | 3.6115          |
| 3.6889        | 1.11  | 250  | 3.5696          |
| 3.1707        | 1.33  | 300  | 3.1811          |
| 3.0354        | 1.55  | 350  | 2.8562          |
| 2.7856        | 1.77  | 400  | 2.7444          |
| 2.4529        | 1.99  | 450  | 2.7386          |
| 1.9655        | 2.21  | 500  | 2.6058          |
| 1.9712        | 2.43  | 550  | 2.3241          |
| 1.9305        | 2.65  | 600  | 2.2414          |
| 1.7701        | 2.88  | 650  | 2.1765          |
| 1.3844        | 3.1   | 700  | 2.7470          |
| 1.4493        | 3.32  | 750  | 2.3821          |
| 1.2487        | 3.54  | 800  | 2.0868          |
| 1.3189        | 3.76  | 850  | 2.5860          |
| 1.3709        | 3.98  | 900  | 2.4280          |
| 1.1087        | 4.2   | 950  | 2.5985          |
| 0.9481        | 4.42  | 1000 | 3.3342          |
| 1.0106        | 4.65  | 1050 | 3.0318          |
| 0.9703        | 4.87  | 1100 | 2.7671          |
| 0.788         | 5.09  | 1150 | 3.0565          |
| 0.6952        | 5.31  | 1200 | 3.3631          |
| 0.8017        | 5.53  | 1250 | 3.2544          |
| 0.6489        | 5.75  | 1300 | 3.0772          |
| 0.7419        | 5.97  | 1350 | 2.6226          |
| 0.558         | 6.19  | 1400 | 3.5186          |
| 0.5727        | 6.42  | 1450 | 3.3600          |
| 0.5322        | 6.64  | 1500 | 3.5181          |
| 0.6472        | 6.86  | 1550 | 3.6967          |
| 0.5805        | 7.08  | 1600 | 3.2425          |
| 0.4877        | 7.3   | 1650 | 3.4871          |
| 0.273         | 7.52  | 1700 | 3.5272          |
| 0.5527        | 7.74  | 1750 | 3.0758          |
| 0.2936        | 7.96  | 1800 | 3.3492          |
| 0.2585        | 8.19  | 1850 | 3.4836          |
| 0.5038        | 8.41  | 1900 | 3.3159          |
| 0.3943        | 8.63  | 1950 | 3.3977          |
| 0.3129        | 8.85  | 2000 | 3.8042          |
| 0.2977        | 9.07  | 2050 | 3.7062          |
| 0.2695        | 9.29  | 2100 | 3.7420          |
| 0.363         | 9.51  | 2150 | 3.6655          |
| 0.1834        | 9.73  | 2200 | 3.7858          |
| 0.3347        | 9.96  | 2250 | 3.9257          |
| 0.2159        | 10.18 | 2300 | 3.9075          |
| 0.2691        | 10.4  | 2350 | 3.8001          |
| 0.2965        | 10.62 | 2400 | 3.6177          |
| 0.3078        | 10.84 | 2450 | 3.8440          |
| 0.1397        | 11.06 | 2500 | 4.0490          |
| 0.052         | 11.28 | 2550 | 4.2137          |
| 0.1741        | 11.5  | 2600 | 4.2273          |
| 0.2956        | 11.73 | 2650 | 3.8075          |
| 0.2098        | 11.95 | 2700 | 4.1653          |
| 0.1466        | 12.17 | 2750 | 4.2080          |
| 0.1378        | 12.39 | 2800 | 4.0473          |
| 0.1864        | 12.61 | 2850 | 4.0665          |
| 0.1938        | 12.83 | 2900 | 4.1019          |
| 0.2332        | 13.05 | 2950 | 3.9249          |
| 0.0486        | 13.27 | 3000 | 4.2374          |
| 0.223         | 13.5  | 3050 | 4.1405          |
| 0.1387        | 13.72 | 3100 | 4.1833          |
| 0.1447        | 13.94 | 3150 | 4.0949          |
| 0.0265        | 14.16 | 3200 | 4.2652          |
| 0.0253        | 14.38 | 3250 | 4.5294          |
| 0.1781        | 14.6  | 3300 | 4.5310          |
| 0.0676        | 14.82 | 3350 | 4.3897          |
| 0.0692        | 15.04 | 3400 | 4.3176          |
| 0.1093        | 15.27 | 3450 | 4.3505          |
| 0.0219        | 15.49 | 3500 | 4.4436          |
| 0.0834        | 15.71 | 3550 | 4.4736          |
| 0.0728        | 15.93 | 3600 | 4.5234          |
| 0.0612        | 16.15 | 3650 | 4.6432          |
| 0.0844        | 16.37 | 3700 | 4.5729          |
| 0.0269        | 16.59 | 3750 | 4.7230          |
| 0.037         | 16.81 | 3800 | 4.7512          |
| 0.0532        | 17.04 | 3850 | 4.8247          |
| 0.0042        | 17.26 | 3900 | 4.8263          |
| 0.0684        | 17.48 | 3950 | 4.7495          |
| 0.0058        | 17.7  | 4000 | 4.7748          |
| 0.0158        | 17.92 | 4050 | 4.8827          |
| 0.0059        | 18.14 | 4100 | 4.8746          |
| 0.0217        | 18.36 | 4150 | 4.8981          |
| 0.0725        | 18.58 | 4200 | 4.9908          |
| 0.0399        | 18.81 | 4250 | 4.8712          |
| 0.0478        | 19.03 | 4300 | 4.9006          |
| 0.0212        | 19.25 | 4350 | 4.9160          |
| 0.0269        | 19.47 | 4400 | 4.9076          |
| 0.0317        | 19.69 | 4450 | 4.9081          |
| 0.0233        | 19.91 | 4500 | 4.9097          |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1