---
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: nan

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0           | 0.22  | 50   | nan             |
| 0.0           | 0.44  | 100  | nan             |
| 0.0           | 0.66  | 150  | nan             |
| 0.0           | 0.88  | 200  | nan             |
| 0.0           | 1.11  | 250  | nan             |
| 0.0           | 1.33  | 300  | nan             |
| 0.0           | 1.55  | 350  | nan             |
| 0.0           | 1.77  | 400  | nan             |
| 0.0           | 1.99  | 450  | nan             |
| 0.0           | 2.21  | 500  | nan             |
| 0.0           | 2.43  | 550  | nan             |
| 0.0           | 2.65  | 600  | nan             |
| 0.0           | 2.88  | 650  | nan             |
| 0.0           | 3.1   | 700  | nan             |
| 0.0           | 3.32  | 750  | nan             |
| 0.0           | 3.54  | 800  | nan             |
| 0.0           | 3.76  | 850  | nan             |
| 0.0           | 3.98  | 900  | nan             |
| 0.0           | 4.2   | 950  | nan             |
| 0.0           | 4.42  | 1000 | nan             |
| 0.0           | 4.65  | 1050 | nan             |
| 0.0           | 4.87  | 1100 | nan             |
| 0.0           | 5.09  | 1150 | nan             |
| 0.0           | 5.31  | 1200 | nan             |
| 0.0           | 5.53  | 1250 | nan             |
| 0.0           | 5.75  | 1300 | nan             |
| 0.0           | 5.97  | 1350 | nan             |
| 0.0           | 6.19  | 1400 | nan             |
| 0.0           | 6.42  | 1450 | nan             |
| 0.0           | 6.64  | 1500 | nan             |
| 0.0           | 6.86  | 1550 | nan             |
| 0.0           | 7.08  | 1600 | nan             |
| 0.0           | 7.3   | 1650 | nan             |
| 0.0           | 7.52  | 1700 | nan             |
| 0.0           | 7.74  | 1750 | nan             |
| 0.0           | 7.96  | 1800 | nan             |
| 0.0           | 8.19  | 1850 | nan             |
| 0.0           | 8.41  | 1900 | nan             |
| 0.0           | 8.63  | 1950 | nan             |
| 0.0           | 8.85  | 2000 | nan             |
| 0.0           | 9.07  | 2050 | nan             |
| 0.0           | 9.29  | 2100 | nan             |
| 0.0           | 9.51  | 2150 | nan             |
| 0.0           | 9.73  | 2200 | nan             |
| 0.0           | 9.96  | 2250 | nan             |
| 0.0           | 10.18 | 2300 | nan             |
| 0.0           | 10.4  | 2350 | nan             |
| 0.0           | 10.62 | 2400 | nan             |
| 0.0           | 10.84 | 2450 | nan             |
| 0.0           | 11.06 | 2500 | nan             |
| 0.0           | 11.28 | 2550 | nan             |
| 0.0           | 11.5  | 2600 | nan             |
| 0.0           | 11.73 | 2650 | nan             |
| 0.0           | 11.95 | 2700 | nan             |
| 0.0           | 12.17 | 2750 | nan             |
| 0.0           | 12.39 | 2800 | nan             |
| 0.0           | 12.61 | 2850 | nan             |
| 0.0           | 12.83 | 2900 | nan             |
| 0.0           | 13.05 | 2950 | nan             |
| 0.0           | 13.27 | 3000 | nan             |
| 0.0           | 13.5  | 3050 | nan             |
| 0.0           | 13.72 | 3100 | nan             |
| 0.0           | 13.94 | 3150 | nan             |
| 0.0           | 14.16 | 3200 | nan             |
| 0.0           | 14.38 | 3250 | nan             |
| 0.0           | 14.6  | 3300 | nan             |
| 0.0           | 14.82 | 3350 | nan             |
| 0.0           | 15.04 | 3400 | nan             |
| 0.0           | 15.27 | 3450 | nan             |
| 0.0           | 15.49 | 3500 | nan             |
| 0.0           | 15.71 | 3550 | nan             |
| 0.0           | 15.93 | 3600 | nan             |
| 0.0           | 16.15 | 3650 | nan             |
| 0.0           | 16.37 | 3700 | nan             |
| 0.0           | 16.59 | 3750 | nan             |
| 0.0           | 16.81 | 3800 | nan             |
| 0.0           | 17.04 | 3850 | nan             |
| 0.0           | 17.26 | 3900 | nan             |
| 0.0           | 17.48 | 3950 | nan             |
| 0.0           | 17.7  | 4000 | nan             |
| 0.0           | 17.92 | 4050 | nan             |
| 0.0           | 18.14 | 4100 | nan             |
| 0.0           | 18.36 | 4150 | nan             |
| 0.0           | 18.58 | 4200 | nan             |
| 0.0           | 18.81 | 4250 | nan             |
| 0.0           | 19.03 | 4300 | nan             |
| 0.0           | 19.25 | 4350 | nan             |
| 0.0           | 19.47 | 4400 | nan             |
| 0.0           | 19.69 | 4450 | nan             |
| 0.0           | 19.91 | 4500 | nan             |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2