---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-base-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-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: 4.6228

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2897        | 0.22  | 50   | 4.5700          |
| 4.4152        | 0.44  | 100  | 4.4259          |
| 4.1658        | 0.66  | 150  | 3.7699          |
| 3.7752        | 0.88  | 200  | 3.5137          |
| 3.4951        | 1.11  | 250  | 3.2858          |
| 3.0566        | 1.33  | 300  | 3.1827          |
| 2.9219        | 1.55  | 350  | 3.0013          |
| 2.6689        | 1.77  | 400  | 2.8707          |
| 2.5179        | 1.99  | 450  | 2.8395          |
| 2.0212        | 2.21  | 500  | 2.5494          |
| 1.9111        | 2.43  | 550  | 2.4910          |
| 1.897         | 2.65  | 600  | 2.3390          |
| 1.7719        | 2.88  | 650  | 2.0315          |
| 1.3732        | 3.1   | 700  | 2.6837          |
| 1.3554        | 3.32  | 750  | 2.7709          |
| 1.2142        | 3.54  | 800  | 2.6627          |
| 1.1214        | 3.76  | 850  | 2.6243          |
| 1.1927        | 3.98  | 900  | 2.4302          |
| 0.8986        | 4.2   | 950  | 2.5084          |
| 0.8782        | 4.42  | 1000 | 2.6108          |
| 0.9384        | 4.65  | 1050 | 2.5729          |
| 0.9106        | 4.87  | 1100 | 2.8295          |
| 0.7983        | 5.09  | 1150 | 3.3324          |
| 0.7627        | 5.31  | 1200 | 3.0111          |
| 0.8101        | 5.53  | 1250 | 3.0340          |
| 0.8363        | 5.75  | 1300 | 2.6495          |
| 0.8682        | 5.97  | 1350 | 3.0019          |
| 0.636         | 6.19  | 1400 | 3.2153          |
| 0.5614        | 6.42  | 1450 | 2.9601          |
| 0.664         | 6.64  | 1500 | 3.1723          |
| 0.6052        | 6.86  | 1550 | 3.8548          |
| 0.5859        | 7.08  | 1600 | 3.2841          |
| 0.5383        | 7.3   | 1650 | 3.2616          |
| 0.3317        | 7.52  | 1700 | 3.6498          |
| 0.5176        | 7.74  | 1750 | 3.1792          |
| 0.323         | 7.96  | 1800 | 3.9586          |
| 0.2828        | 8.19  | 1850 | 3.3414          |
| 0.3408        | 8.41  | 1900 | 3.4490          |
| 0.4341        | 8.63  | 1950 | 3.6120          |
| 0.4256        | 8.85  | 2000 | 3.6485          |
| 0.2488        | 9.07  | 2050 | 3.2907          |
| 0.2399        | 9.29  | 2100 | 3.9223          |
| 0.3902        | 9.51  | 2150 | 3.4605          |
| 0.1764        | 9.73  | 2200 | 3.4834          |
| 0.3641        | 9.96  | 2250 | 3.6385          |
| 0.0802        | 10.18 | 2300 | 4.1041          |
| 0.1922        | 10.4  | 2350 | 4.0973          |
| 0.1943        | 10.62 | 2400 | 3.8264          |
| 0.1944        | 10.84 | 2450 | 4.0448          |
| 0.1396        | 11.06 | 2500 | 4.0736          |
| 0.1399        | 11.28 | 2550 | 4.1645          |
| 0.1739        | 11.5  | 2600 | 4.0905          |
| 0.0859        | 11.73 | 2650 | 4.2965          |
| 0.1819        | 11.95 | 2700 | 3.9382          |
| 0.1614        | 12.17 | 2750 | 4.2804          |
| 0.1406        | 12.39 | 2800 | 4.4033          |
| 0.1474        | 12.61 | 2850 | 4.3500          |
| 0.0857        | 12.83 | 2900 | 4.6170          |
| 0.1197        | 13.05 | 2950 | 4.0885          |
| 0.1087        | 13.27 | 3000 | 4.1931          |
| 0.0654        | 13.5  | 3050 | 4.4273          |
| 0.1081        | 13.72 | 3100 | 4.3433          |
| 0.2075        | 13.94 | 3150 | 4.1598          |
| 0.0807        | 14.16 | 3200 | 4.1951          |
| 0.0338        | 14.38 | 3250 | 4.2540          |
| 0.0918        | 14.6  | 3300 | 4.4138          |
| 0.1703        | 14.82 | 3350 | 3.9894          |
| 0.0176        | 15.04 | 3400 | 4.3193          |
| 0.1292        | 15.27 | 3450 | 4.4866          |
| 0.0484        | 15.49 | 3500 | 4.2460          |
| 0.0703        | 15.71 | 3550 | 4.2828          |
| 0.1076        | 15.93 | 3600 | 4.4895          |
| 0.0245        | 16.15 | 3650 | 4.5421          |
| 0.0779        | 16.37 | 3700 | 4.5335          |
| 0.0553        | 16.59 | 3750 | 4.5308          |
| 0.0626        | 16.81 | 3800 | 4.4731          |
| 0.0175        | 17.04 | 3850 | 4.4889          |
| 0.0038        | 17.26 | 3900 | 4.4956          |
| 0.0074        | 17.48 | 3950 | 4.6014          |
| 0.0761        | 17.7  | 4000 | 4.5396          |
| 0.0095        | 17.92 | 4050 | 4.5511          |
| 0.0634        | 18.14 | 4100 | 4.5970          |
| 0.0043        | 18.36 | 4150 | 4.6040          |
| 0.0863        | 18.58 | 4200 | 4.6277          |
| 0.02          | 18.81 | 4250 | 4.5889          |
| 0.0176        | 19.03 | 4300 | 4.6318          |
| 0.0062        | 19.25 | 4350 | 4.6496          |
| 0.008         | 19.47 | 4400 | 4.6139          |
| 0.0035        | 19.69 | 4450 | 4.6159          |
| 0.0137        | 19.91 | 4500 | 4.6228          |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3