---
license: cc-by-nc-sa-4.0
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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6207

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.3769        | 0.22  | 50   | 4.5459          |
| 4.3879        | 0.44  | 100  | 4.0099          |
| 3.9828        | 0.66  | 150  | 3.8817          |
| 3.7387        | 0.88  | 200  | 3.5334          |
| 3.4702        | 1.11  | 250  | 3.4670          |
| 3.0784        | 1.33  | 300  | 3.5327          |
| 2.8014        | 1.55  | 350  | 2.8722          |
| 2.673         | 1.77  | 400  | 2.8246          |
| 2.4546        | 1.99  | 450  | 2.7050          |
| 2.094         | 2.21  | 500  | 2.7639          |
| 1.9542        | 2.43  | 550  | 2.3472          |
| 1.8966        | 2.65  | 600  | 2.4508          |
| 1.7745        | 2.88  | 650  | 2.2813          |
| 1.4371        | 3.1   | 700  | 2.5234          |
| 1.3257        | 3.32  | 750  | 2.4358          |
| 1.3269        | 3.54  | 800  | 2.3044          |
| 1.4035        | 3.76  | 850  | 2.3546          |
| 1.6189        | 3.98  | 900  | 2.0838          |
| 0.9209        | 4.2   | 950  | 2.3836          |
| 0.8405        | 4.42  | 1000 | 3.1673          |
| 0.9808        | 4.65  | 1050 | 2.8038          |
| 0.8978        | 4.87  | 1100 | 2.7652          |
| 0.8733        | 5.09  | 1150 | 3.0965          |
| 0.7449        | 5.31  | 1200 | 2.9948          |
| 0.8173        | 5.53  | 1250 | 2.8631          |
| 0.8322        | 5.75  | 1300 | 2.6144          |
| 0.7147        | 5.97  | 1350 | 3.2041          |
| 0.6495        | 6.19  | 1400 | 3.3711          |
| 0.5458        | 6.42  | 1450 | 3.5480          |
| 0.5624        | 6.64  | 1500 | 3.3366          |
| 0.5736        | 6.86  | 1550 | 2.9356          |
| 0.2921        | 7.08  | 1600 | 3.4028          |
| 0.3883        | 7.3   | 1650 | 3.4411          |
| 0.3614        | 7.52  | 1700 | 3.2267          |
| 0.4376        | 7.74  | 1750 | 3.2137          |
| 0.4849        | 7.96  | 1800 | 3.6388          |
| 0.5035        | 8.19  | 1850 | 4.0089          |
| 0.36          | 8.41  | 1900 | 3.7903          |
| 0.2238        | 8.63  | 1950 | 3.7131          |
| 0.276         | 8.85  | 2000 | 3.8541          |
| 0.2661        | 9.07  | 2050 | 3.5220          |
| 0.291         | 9.29  | 2100 | 3.9075          |
| 0.3767        | 9.51  | 2150 | 3.4267          |
| 0.1326        | 9.73  | 2200 | 3.9069          |
| 0.2365        | 9.96  | 2250 | 3.5183          |
| 0.2676        | 10.18 | 2300 | 3.4975          |
| 0.131         | 10.4  | 2350 | 3.8138          |
| 0.1818        | 10.62 | 2400 | 3.6213          |
| 0.2988        | 10.84 | 2450 | 3.7380          |
| 0.0511        | 11.06 | 2500 | 4.1506          |
| 0.1909        | 11.28 | 2550 | 3.7369          |
| 0.0854        | 11.5  | 2600 | 3.8751          |
| 0.1291        | 11.73 | 2650 | 3.7143          |
| 0.1896        | 11.95 | 2700 | 4.0645          |
| 0.0746        | 12.17 | 2750 | 4.0363          |
| 0.1254        | 12.39 | 2800 | 4.0050          |
| 0.1588        | 12.61 | 2850 | 4.1739          |
| 0.1622        | 12.83 | 2900 | 4.2698          |
| 0.047         | 13.05 | 2950 | 4.5366          |
| 0.1023        | 13.27 | 3000 | 4.3094          |
| 0.1195        | 13.5  | 3050 | 4.3112          |
| 0.0753        | 13.72 | 3100 | 4.3137          |
| 0.0545        | 13.94 | 3150 | 4.4992          |
| 0.0771        | 14.16 | 3200 | 4.4759          |
| 0.0648        | 14.38 | 3250 | 4.5531          |
| 0.0935        | 14.6  | 3300 | 4.3725          |
| 0.1032        | 14.82 | 3350 | 4.3321          |
| 0.0199        | 15.04 | 3400 | 4.3527          |
| 0.0498        | 15.27 | 3450 | 4.2477          |
| 0.0688        | 15.49 | 3500 | 4.2340          |
| 0.0271        | 15.71 | 3550 | 4.2386          |
| 0.0176        | 15.93 | 3600 | 4.4715          |
| 0.0319        | 16.15 | 3650 | 4.5608          |
| 0.0061        | 16.37 | 3700 | 4.5767          |
| 0.0043        | 16.59 | 3750 | 4.6581          |
| 0.071         | 16.81 | 3800 | 4.5622          |
| 0.0689        | 17.04 | 3850 | 4.5067          |
| 0.0328        | 17.26 | 3900 | 4.4449          |
| 0.0784        | 17.48 | 3950 | 4.3684          |
| 0.0387        | 17.7  | 4000 | 4.4261          |
| 0.0107        | 17.92 | 4050 | 4.5190          |
| 0.0056        | 18.14 | 4100 | 4.4963          |
| 0.0261        | 18.36 | 4150 | 4.5995          |
| 0.0061        | 18.58 | 4200 | 4.6121          |
| 0.0354        | 18.81 | 4250 | 4.5794          |
| 0.0111        | 19.03 | 4300 | 4.5977          |
| 0.022         | 19.25 | 4350 | 4.6151          |
| 0.0628        | 19.47 | 4400 | 4.6061          |
| 0.0186        | 19.69 | 4450 | 4.6180          |
| 0.0077        | 19.91 | 4500 | 4.6207          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3