---
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: 4.3701

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3439        | 0.22  | 50   | 4.6661          |
| 4.4287        | 0.44  | 100  | 4.2125          |
| 4.0781        | 0.66  | 150  | 3.7145          |
| 3.8419        | 0.88  | 200  | 3.5926          |
| 3.633         | 1.11  | 250  | 3.4266          |
| 3.1492        | 1.33  | 300  | 3.2833          |
| 3.2018        | 1.55  | 350  | 3.0237          |
| 2.8891        | 1.77  | 400  | 2.9976          |
| 2.6695        | 1.99  | 450  | 3.0654          |
| 2.211         | 2.21  | 500  | 2.7658          |
| 2.2555        | 2.43  | 550  | 2.5009          |
| 2.0293        | 2.65  | 600  | 2.3515          |
| 1.8663        | 2.88  | 650  | 2.1264          |
| 1.6332        | 3.1   | 700  | 2.3934          |
| 1.5298        | 3.32  | 750  | 2.6686          |
| 1.3594        | 3.54  | 800  | 2.0781          |
| 1.3923        | 3.76  | 850  | 2.2174          |
| 1.26          | 3.98  | 900  | 2.5728          |
| 0.9371        | 4.2   | 950  | 2.7164          |
| 0.9891        | 4.42  | 1000 | 2.8637          |
| 1.0822        | 4.65  | 1050 | 2.5435          |
| 0.9355        | 4.87  | 1100 | 2.7256          |
| 0.9527        | 5.09  | 1150 | 2.8934          |
| 0.9454        | 5.31  | 1200 | 3.0235          |
| 0.6458        | 5.53  | 1250 | 3.2280          |
| 0.8697        | 5.75  | 1300 | 2.8636          |
| 0.9365        | 5.97  | 1350 | 2.8955          |
| 0.4425        | 6.19  | 1400 | 3.0859          |
| 0.6329        | 6.42  | 1450 | 3.0695          |
| 0.7564        | 6.64  | 1500 | 2.5050          |
| 0.5747        | 6.86  | 1550 | 3.2825          |
| 0.5451        | 7.08  | 1600 | 3.4123          |
| 0.5432        | 7.3   | 1650 | 3.1163          |
| 0.3738        | 7.52  | 1700 | 2.8969          |
| 0.5026        | 7.74  | 1750 | 2.8579          |
| 0.4245        | 7.96  | 1800 | 3.2212          |
| 0.3145        | 8.19  | 1850 | 3.4482          |
| 0.516         | 8.41  | 1900 | 2.9995          |
| 0.2816        | 8.63  | 1950 | 2.9903          |
| 0.3946        | 8.85  | 2000 | 3.3378          |
| 0.3854        | 9.07  | 2050 | 3.4644          |
| 0.2191        | 9.29  | 2100 | 3.5034          |
| 0.3854        | 9.51  | 2150 | 3.4320          |
| 0.2207        | 9.73  | 2200 | 3.6972          |
| 0.2779        | 9.96  | 2250 | 3.6866          |
| 0.2837        | 10.18 | 2300 | 3.8988          |
| 0.1613        | 10.4  | 2350 | 3.8722          |
| 0.1069        | 10.62 | 2400 | 3.9079          |
| 0.4031        | 10.84 | 2450 | 3.5352          |
| 0.2129        | 11.06 | 2500 | 3.6764          |
| 0.1166        | 11.28 | 2550 | 4.1964          |
| 0.1599        | 11.5  | 2600 | 4.2577          |
| 0.2108        | 11.73 | 2650 | 3.7519          |
| 0.3207        | 11.95 | 2700 | 3.6609          |
| 0.2291        | 12.17 | 2750 | 3.5265          |
| 0.1609        | 12.39 | 2800 | 3.8727          |
| 0.2308        | 12.61 | 2850 | 3.9877          |
| 0.1461        | 12.83 | 2900 | 4.0395          |
| 0.1353        | 13.05 | 2950 | 3.8678          |
| 0.0735        | 13.27 | 3000 | 3.9054          |
| 0.1695        | 13.5  | 3050 | 3.5661          |
| 0.0129        | 13.72 | 3100 | 3.9625          |
| 0.1691        | 13.94 | 3150 | 3.7996          |
| 0.066         | 14.16 | 3200 | 4.2540          |
| 0.0484        | 14.38 | 3250 | 4.0625          |
| 0.099         | 14.6  | 3300 | 4.4666          |
| 0.127         | 14.82 | 3350 | 4.0762          |
| 0.046         | 15.04 | 3400 | 4.1859          |
| 0.0608        | 15.27 | 3450 | 4.4004          |
| 0.1002        | 15.49 | 3500 | 4.3228          |
| 0.0394        | 15.71 | 3550 | 4.4576          |
| 0.0863        | 15.93 | 3600 | 4.4386          |
| 0.0214        | 16.15 | 3650 | 4.5233          |
| 0.0661        | 16.37 | 3700 | 4.4493          |
| 0.0448        | 16.59 | 3750 | 4.3361          |
| 0.0463        | 16.81 | 3800 | 4.4174          |
| 0.0456        | 17.04 | 3850 | 4.4851          |
| 0.0144        | 17.26 | 3900 | 4.4655          |
| 0.0268        | 17.48 | 3950 | 4.4417          |
| 0.0529        | 17.7  | 4000 | 4.3580          |
| 0.0678        | 17.92 | 4050 | 4.2008          |
| 0.0039        | 18.14 | 4100 | 4.2346          |
| 0.0481        | 18.36 | 4150 | 4.2652          |
| 0.0501        | 18.58 | 4200 | 4.2786          |
| 0.0271        | 18.81 | 4250 | 4.2857          |
| 0.0322        | 19.03 | 4300 | 4.3047          |
| 0.0187        | 19.25 | 4350 | 4.3691          |
| 0.0469        | 19.47 | 4400 | 4.3560          |
| 0.0056        | 19.69 | 4450 | 4.3626          |
| 0.0099        | 19.91 | 4500 | 4.3701          |


### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1