---
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: 5.3353

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.153         | 0.22  | 50   | 5.3909          |
| 0.2793        | 0.44  | 100  | 5.0150          |
| 0.2634        | 0.66  | 150  | 4.6620          |
| 0.5192        | 0.88  | 200  | 4.7826          |
| 0.3096        | 1.11  | 250  | 4.9532          |
| 0.2638        | 1.33  | 300  | 5.2584          |
| 0.4727        | 1.55  | 350  | 4.0943          |
| 0.2763        | 1.77  | 400  | 4.8408          |
| 1.0425        | 1.99  | 450  | 5.0344          |
| 0.4477        | 2.21  | 500  | 4.9084          |
| 0.3266        | 2.43  | 550  | 5.0996          |
| 0.3085        | 2.65  | 600  | 4.4858          |
| 0.4648        | 2.88  | 650  | 4.0630          |
| 0.1845        | 3.1   | 700  | 5.3969          |
| 0.1616        | 3.32  | 750  | 4.8225          |
| 0.1752        | 3.54  | 800  | 5.2945          |
| 0.1877        | 3.76  | 850  | 5.2358          |
| 0.3172        | 3.98  | 900  | 5.2205          |
| 0.1627        | 4.2   | 950  | 4.9991          |
| 0.2548        | 4.42  | 1000 | 4.6917          |
| 0.1566        | 4.65  | 1050 | 5.1266          |
| 0.2616        | 4.87  | 1100 | 4.3241          |
| 0.1199        | 5.09  | 1150 | 4.9821          |
| 0.1372        | 5.31  | 1200 | 5.0838          |
| 0.1198        | 5.53  | 1250 | 5.0156          |
| 0.0558        | 5.75  | 1300 | 4.8638          |
| 0.1331        | 5.97  | 1350 | 4.9492          |
| 0.0689        | 6.19  | 1400 | 4.6926          |
| 0.0912        | 6.42  | 1450 | 4.5153          |
| 0.0495        | 6.64  | 1500 | 4.6969          |
| 0.0853        | 6.86  | 1550 | 4.7690          |
| 0.1072        | 7.08  | 1600 | 4.6783          |
| 0.034         | 7.3   | 1650 | 4.7351          |
| 0.2999        | 7.52  | 1700 | 4.5185          |
| 0.0763        | 7.74  | 1750 | 4.5825          |
| 0.0799        | 7.96  | 1800 | 4.7218          |
| 0.0343        | 8.19  | 1850 | 5.1508          |
| 0.0396        | 8.41  | 1900 | 5.4893          |
| 0.033         | 8.63  | 1950 | 5.5167          |
| 0.0295        | 8.85  | 2000 | 5.6252          |
| 0.2303        | 9.07  | 2050 | 4.7031          |
| 0.088         | 9.29  | 2100 | 4.7323          |
| 0.0666        | 9.51  | 2150 | 4.8688          |
| 0.0597        | 9.73  | 2200 | 5.6007          |
| 0.0615        | 9.96  | 2250 | 5.5403          |
| 0.1003        | 10.18 | 2300 | 5.3198          |
| 0.0457        | 10.4  | 2350 | 5.4828          |
| 0.0391        | 10.62 | 2400 | 5.5312          |
| 0.0325        | 10.84 | 2450 | 5.7410          |
| 0.0147        | 11.06 | 2500 | 5.8749          |
| 0.1013        | 11.28 | 2550 | 5.6522          |
| 0.001         | 11.5  | 2600 | 5.7776          |
| 0.0002        | 11.73 | 2650 | 5.8431          |
| 0.03          | 11.95 | 2700 | 5.9751          |
| 0.0452        | 12.17 | 2750 | 5.6928          |
| 0.0002        | 12.39 | 2800 | 5.6264          |
| 0.0109        | 12.61 | 2850 | 5.2688          |
| 0.0801        | 12.83 | 2900 | 5.2780          |
| 0.0216        | 13.05 | 2950 | 5.3691          |
| 0.0002        | 13.27 | 3000 | 5.5237          |
| 0.0092        | 13.5  | 3050 | 5.3662          |
| 0.0124        | 13.72 | 3100 | 5.4474          |
| 0.0515        | 13.94 | 3150 | 5.3623          |
| 0.0032        | 14.16 | 3200 | 5.4168          |
| 0.0051        | 14.38 | 3250 | 5.2897          |
| 0.0002        | 14.6  | 3300 | 5.3205          |
| 0.014         | 14.82 | 3350 | 5.2114          |
| 0.0004        | 15.04 | 3400 | 5.2342          |
| 0.0104        | 15.27 | 3450 | 5.2562          |
| 0.0107        | 15.49 | 3500 | 5.1112          |
| 0.0002        | 15.71 | 3550 | 5.1515          |
| 0.0002        | 15.93 | 3600 | 5.2054          |
| 0.0002        | 16.15 | 3650 | 5.1968          |
| 0.0003        | 16.37 | 3700 | 5.3196          |
| 0.0246        | 16.59 | 3750 | 5.3111          |
| 0.0054        | 16.81 | 3800 | 5.3335          |
| 0.0001        | 17.04 | 3850 | 5.3488          |
| 0.0243        | 17.26 | 3900 | 5.2597          |
| 0.0217        | 17.48 | 3950 | 5.2834          |
| 0.0002        | 17.7  | 4000 | 5.2947          |
| 0.0002        | 17.92 | 4050 | 5.3131          |
| 0.0001        | 18.14 | 4100 | 5.3240          |
| 0.0016        | 18.36 | 4150 | 5.3129          |
| 0.0133        | 18.58 | 4200 | 5.3241          |
| 0.0002        | 18.81 | 4250 | 5.3382          |
| 0.0159        | 19.03 | 4300 | 5.3764          |
| 0.003         | 19.25 | 4350 | 5.3776          |
| 0.0516        | 19.47 | 4400 | 5.3389          |
| 0.016         | 19.69 | 4450 | 5.3275          |
| 0.0105        | 19.91 | 4500 | 5.3353          |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.5
- Tokenizers 0.13.3