---
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: 4.4423

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2693        | 0.22  | 50   | 4.4222          |
| 4.3703        | 0.44  | 100  | 4.1079          |
| 4.1363        | 0.66  | 150  | 3.9209          |
| 3.7332        | 0.88  | 200  | 3.6332          |
| 3.4591        | 1.11  | 250  | 3.5577          |
| 3.1781        | 1.33  | 300  | 3.1319          |
| 3.3388        | 1.55  | 350  | 3.0866          |
| 2.8356        | 1.77  | 400  | 2.7820          |
| 2.4286        | 1.99  | 450  | 2.8378          |
| 2.0496        | 2.21  | 500  | 2.5224          |
| 1.9469        | 2.43  | 550  | 2.5281          |
| 1.8342        | 2.65  | 600  | 2.5674          |
| 1.6589        | 2.88  | 650  | 2.2914          |
| 1.6939        | 3.1   | 700  | 2.4427          |
| 1.3883        | 3.32  | 750  | 2.5626          |
| 1.3944        | 3.54  | 800  | 2.3736          |
| 1.2459        | 3.76  | 850  | 2.7994          |
| 1.5218        | 3.98  | 900  | 2.5390          |
| 1.1471        | 4.2   | 950  | 2.5951          |
| 0.8888        | 4.42  | 1000 | 2.7430          |
| 0.971         | 4.65  | 1050 | 2.5219          |
| 1.0425        | 4.87  | 1100 | 2.5474          |
| 0.7665        | 5.09  | 1150 | 2.9321          |
| 0.8039        | 5.31  | 1200 | 2.7369          |
| 0.6426        | 5.53  | 1250 | 3.1309          |
| 0.6628        | 5.75  | 1300 | 3.1167          |
| 0.906         | 5.97  | 1350 | 3.8550          |
| 0.6223        | 6.19  | 1400 | 3.4892          |
| 0.6274        | 6.42  | 1450 | 3.2927          |
| 0.4732        | 6.64  | 1500 | 3.4192          |
| 0.5962        | 6.86  | 1550 | 3.2867          |
| 0.6761        | 7.08  | 1600 | 3.0610          |
| 0.4096        | 7.3   | 1650 | 3.5926          |
| 0.457         | 7.52  | 1700 | 3.2824          |
| 0.3721        | 7.74  | 1750 | 3.4383          |
| 0.4547        | 7.96  | 1800 | 3.4794          |
| 0.4231        | 8.19  | 1850 | 3.7591          |
| 0.3292        | 8.41  | 1900 | 3.8104          |
| 0.4401        | 8.63  | 1950 | 3.7450          |
| 0.446         | 8.85  | 2000 | 3.5815          |
| 0.3362        | 9.07  | 2050 | 3.6245          |
| 0.1832        | 9.29  | 2100 | 3.7162          |
| 0.2085        | 9.51  | 2150 | 3.8565          |
| 0.3248        | 9.73  | 2200 | 3.4577          |
| 0.4722        | 9.96  | 2250 | 3.6518          |
| 0.2575        | 10.18 | 2300 | 3.8701          |
| 0.2336        | 10.4  | 2350 | 3.7511          |
| 0.2864        | 10.62 | 2400 | 3.7999          |
| 0.2091        | 10.84 | 2450 | 3.8716          |
| 0.2371        | 11.06 | 2500 | 3.7909          |
| 0.1582        | 11.28 | 2550 | 4.0463          |
| 0.2519        | 11.5  | 2600 | 3.9798          |
| 0.1223        | 11.73 | 2650 | 4.3331          |
| 0.1838        | 11.95 | 2700 | 4.1601          |
| 0.1204        | 12.17 | 2750 | 4.2846          |
| 0.1797        | 12.39 | 2800 | 4.1595          |
| 0.123         | 12.61 | 2850 | 4.2625          |
| 0.2177        | 12.83 | 2900 | 4.0050          |
| 0.1728        | 13.05 | 2950 | 4.0885          |
| 0.1525        | 13.27 | 3000 | 3.9733          |
| 0.0388        | 13.5  | 3050 | 4.1072          |
| 0.0788        | 13.72 | 3100 | 4.2446          |
| 0.1629        | 13.94 | 3150 | 4.0483          |
| 0.0377        | 14.16 | 3200 | 4.2435          |
| 0.0966        | 14.38 | 3250 | 4.1510          |
| 0.0943        | 14.6  | 3300 | 4.2591          |
| 0.048         | 14.82 | 3350 | 4.1876          |
| 0.097         | 15.04 | 3400 | 4.2489          |
| 0.0188        | 15.27 | 3450 | 4.3612          |
| 0.1163        | 15.49 | 3500 | 4.2931          |
| 0.0754        | 15.71 | 3550 | 4.3306          |
| 0.1044        | 15.93 | 3600 | 4.2243          |
| 0.0316        | 16.15 | 3650 | 4.3932          |
| 0.005         | 16.37 | 3700 | 4.4173          |
| 0.0389        | 16.59 | 3750 | 4.3939          |
| 0.0505        | 16.81 | 3800 | 4.3207          |
| 0.0501        | 17.04 | 3850 | 4.3601          |
| 0.0491        | 17.26 | 3900 | 4.3211          |
| 0.0048        | 17.48 | 3950 | 4.3425          |
| 0.0043        | 17.7  | 4000 | 4.3461          |
| 0.0309        | 17.92 | 4050 | 4.3733          |
| 0.0246        | 18.14 | 4100 | 4.3912          |
| 0.0055        | 18.36 | 4150 | 4.4020          |
| 0.0078        | 18.58 | 4200 | 4.4256          |
| 0.0057        | 18.81 | 4250 | 4.4462          |
| 0.0352        | 19.03 | 4300 | 4.4558          |
| 0.0451        | 19.25 | 4350 | 4.4557          |
| 0.063         | 19.47 | 4400 | 4.4395          |
| 0.0123        | 19.69 | 4450 | 4.4428          |
| 0.0291        | 19.91 | 4500 | 4.4423          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0