---
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: 4.5570

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2892        | 0.22  | 50   | 4.6213          |
| 4.4958        | 0.44  | 100  | 4.1766          |
| 4.1602        | 0.66  | 150  | 3.8565          |
| 3.9538        | 0.88  | 200  | 3.5805          |
| 3.5903        | 1.11  | 250  | 3.4594          |
| 3.1555        | 1.33  | 300  | 3.1305          |
| 3.0041        | 1.55  | 350  | 2.9394          |
| 2.8133        | 1.77  | 400  | 2.7611          |
| 2.5517        | 1.99  | 450  | 2.6359          |
| 1.9883        | 2.21  | 500  | 2.6822          |
| 2.1057        | 2.43  | 550  | 2.4430          |
| 2.0872        | 2.65  | 600  | 2.4388          |
| 1.7591        | 2.88  | 650  | 2.3726          |
| 1.5244        | 3.1   | 700  | 2.3325          |
| 1.5257        | 3.32  | 750  | 2.7668          |
| 1.3295        | 3.54  | 800  | 2.2800          |
| 1.3167        | 3.76  | 850  | 2.3108          |
| 1.2051        | 3.98  | 900  | 2.6429          |
| 1.0532        | 4.2   | 950  | 3.1483          |
| 1.0167        | 4.42  | 1000 | 2.2237          |
| 1.1105        | 4.65  | 1050 | 2.3531          |
| 1.006         | 4.87  | 1100 | 2.8026          |
| 0.8193        | 5.09  | 1150 | 3.0897          |
| 0.7502        | 5.31  | 1200 | 3.5147          |
| 0.9308        | 5.53  | 1250 | 2.5877          |
| 0.6495        | 5.75  | 1300 | 2.8749          |
| 0.7933        | 5.97  | 1350 | 2.7216          |
| 0.7199        | 6.19  | 1400 | 3.1257          |
| 0.5458        | 6.42  | 1450 | 3.5710          |
| 0.7406        | 6.64  | 1500 | 2.5855          |
| 0.5537        | 6.86  | 1550 | 2.9661          |
| 0.493         | 7.08  | 1600 | 3.3691          |
| 0.4938        | 7.3   | 1650 | 3.1937          |
| 0.5407        | 7.52  | 1700 | 3.3585          |
| 0.5243        | 7.74  | 1750 | 2.7650          |
| 0.4177        | 7.96  | 1800 | 3.2618          |
| 0.4094        | 8.19  | 1850 | 3.2418          |
| 0.4061        | 8.41  | 1900 | 3.2710          |
| 0.3248        | 8.63  | 1950 | 3.3732          |
| 0.3787        | 8.85  | 2000 | 3.3467          |
| 0.2709        | 9.07  | 2050 | 3.1261          |
| 0.2655        | 9.29  | 2100 | 3.4552          |
| 0.2552        | 9.51  | 2150 | 3.4344          |
| 0.3321        | 9.73  | 2200 | 3.5558          |
| 0.4165        | 9.96  | 2250 | 3.0775          |
| 0.2052        | 10.18 | 2300 | 3.4985          |
| 0.2642        | 10.4  | 2350 | 3.8930          |
| 0.2461        | 10.62 | 2400 | 3.7029          |
| 0.2895        | 10.84 | 2450 | 3.4143          |
| 0.1041        | 11.06 | 2500 | 3.6652          |
| 0.1316        | 11.28 | 2550 | 3.8867          |
| 0.1165        | 11.5  | 2600 | 4.1669          |
| 0.1925        | 11.73 | 2650 | 4.3376          |
| 0.371         | 11.95 | 2700 | 3.9125          |
| 0.1793        | 12.17 | 2750 | 4.0051          |
| 0.1771        | 12.39 | 2800 | 3.8161          |
| 0.2158        | 12.61 | 2850 | 3.8786          |
| 0.1838        | 12.83 | 2900 | 3.7687          |
| 0.1364        | 13.05 | 2950 | 4.1103          |
| 0.0364        | 13.27 | 3000 | 4.1468          |
| 0.2488        | 13.5  | 3050 | 4.1149          |
| 0.1046        | 13.72 | 3100 | 4.1887          |
| 0.1254        | 13.94 | 3150 | 4.0097          |
| 0.033         | 14.16 | 3200 | 4.3023          |
| 0.0373        | 14.38 | 3250 | 4.3530          |
| 0.0911        | 14.6  | 3300 | 4.2621          |
| 0.1014        | 14.82 | 3350 | 3.8815          |
| 0.0238        | 15.04 | 3400 | 4.4097          |
| 0.1141        | 15.27 | 3450 | 4.4720          |
| 0.0278        | 15.49 | 3500 | 4.4407          |
| 0.0673        | 15.71 | 3550 | 4.4176          |
| 0.0691        | 15.93 | 3600 | 4.4863          |
| 0.0379        | 16.15 | 3650 | 4.6924          |
| 0.057         | 16.37 | 3700 | 4.5305          |
| 0.1087        | 16.59 | 3750 | 4.5050          |
| 0.0047        | 16.81 | 3800 | 4.5652          |
| 0.051         | 17.04 | 3850 | 4.5482          |
| 0.0054        | 17.26 | 3900 | 4.5337          |
| 0.0542        | 17.48 | 3950 | 4.5370          |
| 0.0132        | 17.7  | 4000 | 4.4744          |
| 0.0698        | 17.92 | 4050 | 4.4535          |
| 0.0214        | 18.14 | 4100 | 4.4660          |
| 0.033         | 18.36 | 4150 | 4.4818          |
| 0.029         | 18.58 | 4200 | 4.5033          |
| 0.0283        | 18.81 | 4250 | 4.4825          |
| 0.013         | 19.03 | 4300 | 4.4794          |
| 0.0538        | 19.25 | 4350 | 4.5183          |
| 0.0171        | 19.47 | 4400 | 4.5373          |
| 0.0122        | 19.69 | 4450 | 4.5501          |
| 0.0222        | 19.91 | 4500 | 4.5570          |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3