---
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: 4.9529

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2903        | 0.22  | 50   | 4.6096          |
| 4.441         | 0.44  | 100  | 4.1809          |
| 4.1512        | 0.66  | 150  | 3.8270          |
| 3.9297        | 0.88  | 200  | 3.6180          |
| 3.7006        | 1.11  | 250  | 3.3508          |
| 3.1238        | 1.33  | 300  | 3.4886          |
| 3.177         | 1.55  | 350  | 3.0878          |
| 2.8817        | 1.77  | 400  | 2.8975          |
| 2.6113        | 1.99  | 450  | 3.1366          |
| 2.9929        | 2.21  | 500  | 4.2811          |
| 2.8507        | 2.43  | 550  | 3.1442          |
| 2.6294        | 2.65  | 600  | 2.7537          |
| 2.9134        | 2.88  | 650  | 4.0845          |
| 2.7527        | 3.1   | 700  | 2.6888          |
| 2.2184        | 3.32  | 750  | 2.6068          |
| 1.9832        | 3.54  | 800  | 2.3920          |
| 1.8607        | 3.76  | 850  | 2.3026          |
| 1.6756        | 3.98  | 900  | 2.4535          |
| 1.594         | 4.2   | 950  | 2.3539          |
| 1.3695        | 4.42  | 1000 | 2.9487          |
| 1.4473        | 4.65  | 1050 | 2.3269          |
| 1.0998        | 4.87  | 1100 | 2.5812          |
| 1.043         | 5.09  | 1150 | 2.7785          |
| 1.0655        | 5.31  | 1200 | 3.1658          |
| 1.2366        | 5.53  | 1250 | 3.5025          |
| 1.1033        | 5.75  | 1300 | 3.0308          |
| 1.1406        | 5.97  | 1350 | 2.4193          |
| 0.7332        | 6.19  | 1400 | 3.0098          |
| 0.7752        | 6.42  | 1450 | 3.0226          |
| 0.9816        | 6.64  | 1500 | 3.1292          |
| 0.794         | 6.86  | 1550 | 3.4569          |
| 0.6923        | 7.08  | 1600 | 3.5805          |
| 0.4034        | 7.3   | 1650 | 3.9237          |
| 0.4836        | 7.52  | 1700 | 3.4433          |
| 0.6216        | 7.74  | 1750 | 3.1084          |
| 0.6027        | 7.96  | 1800 | 3.5491          |
| 0.4783        | 8.19  | 1850 | 3.7448          |
| 0.4513        | 8.41  | 1900 | 3.4646          |
| 0.4544        | 8.63  | 1950 | 3.7954          |
| 0.5161        | 8.85  | 2000 | 3.7831          |
| 0.1872        | 9.07  | 2050 | 3.6736          |
| 0.506         | 9.29  | 2100 | 3.7390          |
| 0.2257        | 9.51  | 2150 | 3.9423          |
| 0.2648        | 9.73  | 2200 | 3.7982          |
| 0.3953        | 9.96  | 2250 | 3.2984          |
| 0.1601        | 10.18 | 2300 | 3.6460          |
| 0.2689        | 10.4  | 2350 | 3.9842          |
| 0.2762        | 10.62 | 2400 | 3.2707          |
| 0.3091        | 10.84 | 2450 | 3.4759          |
| 0.2036        | 11.06 | 2500 | 3.7818          |
| 0.1104        | 11.28 | 2550 | 3.8338          |
| 0.1555        | 11.5  | 2600 | 3.7824          |
| 0.2794        | 11.73 | 2650 | 3.7954          |
| 0.2728        | 11.95 | 2700 | 3.5966          |
| 0.2168        | 12.17 | 2750 | 4.2583          |
| 0.1133        | 12.39 | 2800 | 4.3897          |
| 0.293         | 12.61 | 2850 | 3.9776          |
| 0.1307        | 12.83 | 2900 | 4.4287          |
| 0.2012        | 13.05 | 2950 | 4.0434          |
| 0.1583        | 13.27 | 3000 | 3.8509          |
| 0.1016        | 13.5  | 3050 | 3.9090          |
| 0.0329        | 13.72 | 3100 | 4.2917          |
| 0.1034        | 13.94 | 3150 | 4.3789          |
| 0.0928        | 14.16 | 3200 | 4.4046          |
| 0.1318        | 14.38 | 3250 | 4.2611          |
| 0.1015        | 14.6  | 3300 | 4.4932          |
| 0.1499        | 14.82 | 3350 | 4.4150          |
| 0.1858        | 15.04 | 3400 | 4.1948          |
| 0.1402        | 15.27 | 3450 | 4.3734          |
| 0.0584        | 15.49 | 3500 | 4.3949          |
| 0.0288        | 15.71 | 3550 | 4.6144          |
| 0.0554        | 15.93 | 3600 | 4.8472          |
| 0.0853        | 16.15 | 3650 | 4.7406          |
| 0.0111        | 16.37 | 3700 | 5.0774          |
| 0.1094        | 16.59 | 3750 | 4.9672          |
| 0.0102        | 16.81 | 3800 | 4.9885          |
| 0.0884        | 17.04 | 3850 | 5.0612          |
| 0.0318        | 17.26 | 3900 | 5.1363          |
| 0.1083        | 17.48 | 3950 | 4.7403          |
| 0.0891        | 17.7  | 4000 | 4.6907          |
| 0.0495        | 17.92 | 4050 | 4.7827          |
| 0.015         | 18.14 | 4100 | 5.0118          |
| 0.0554        | 18.36 | 4150 | 4.9823          |
| 0.084         | 18.58 | 4200 | 4.9539          |
| 0.0714        | 18.81 | 4250 | 4.8877          |
| 0.0573        | 19.03 | 4300 | 4.9120          |
| 0.012         | 19.25 | 4350 | 4.9568          |
| 0.0381        | 19.47 | 4400 | 4.9459          |
| 0.0126        | 19.69 | 4450 | 4.9544          |
| 0.0591        | 19.91 | 4500 | 4.9529          |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.13.3