---
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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8270

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2536        | 0.22  | 50   | 4.4585          |
| 4.2935        | 0.44  | 100  | 4.1809          |
| 4.0821        | 0.66  | 150  | 3.7822          |
| 3.8393        | 0.88  | 200  | 3.4892          |
| 3.4613        | 1.11  | 250  | 3.4728          |
| 2.9792        | 1.33  | 300  | 3.0083          |
| 2.9501        | 1.55  | 350  | 2.9516          |
| 2.8889        | 1.77  | 400  | 2.7119          |
| 2.4728        | 1.99  | 450  | 2.6027          |
| 1.9523        | 2.21  | 500  | 2.5411          |
| 1.9312        | 2.43  | 550  | 2.5406          |
| 1.8471        | 2.65  | 600  | 2.4484          |
| 1.7456        | 2.88  | 650  | 1.9633          |
| 1.4404        | 3.1   | 700  | 2.5037          |
| 1.3567        | 3.32  | 750  | 2.4763          |
| 1.3247        | 3.54  | 800  | 2.3191          |
| 1.2739        | 3.76  | 850  | 2.4416          |
| 1.2548        | 3.98  | 900  | 2.3970          |
| 0.8073        | 4.2   | 950  | 2.3073          |
| 0.8992        | 4.42  | 1000 | 2.6353          |
| 0.8439        | 4.65  | 1050 | 2.7353          |
| 0.9051        | 4.87  | 1100 | 2.4587          |
| 0.7201        | 5.09  | 1150 | 3.1759          |
| 0.6394        | 5.31  | 1200 | 3.2409          |
| 0.6142        | 5.53  | 1250 | 3.0543          |
| 0.6385        | 5.75  | 1300 | 2.5017          |
| 0.5985        | 5.97  | 1350 | 3.1824          |
| 0.2824        | 6.19  | 1400 | 3.3628          |
| 0.4695        | 6.42  | 1450 | 2.9493          |
| 0.6322        | 6.64  | 1500 | 2.8337          |
| 0.5114        | 6.86  | 1550 | 3.2387          |
| 0.4555        | 7.08  | 1600 | 3.3994          |
| 0.3308        | 7.3   | 1650 | 3.3562          |
| 0.2209        | 7.52  | 1700 | 3.5050          |
| 0.4407        | 7.74  | 1750 | 3.8571          |
| 0.442         | 7.96  | 1800 | 3.5946          |
| 0.2722        | 8.19  | 1850 | 3.6526          |
| 0.423         | 8.41  | 1900 | 3.1283          |
| 0.316         | 8.63  | 1950 | 3.4300          |
| 0.3435        | 8.85  | 2000 | 3.6418          |
| 0.2815        | 9.07  | 2050 | 3.4021          |
| 0.2051        | 9.29  | 2100 | 3.7000          |
| 0.2442        | 9.51  | 2150 | 3.4389          |
| 0.1833        | 9.73  | 2200 | 3.7243          |
| 0.2408        | 9.96  | 2250 | 3.6520          |
| 0.1971        | 10.18 | 2300 | 3.5589          |
| 0.196         | 10.4  | 2350 | 3.7747          |
| 0.2511        | 10.62 | 2400 | 3.5574          |
| 0.1473        | 10.84 | 2450 | 3.7469          |
| 0.1141        | 11.06 | 2500 | 3.7303          |
| 0.1058        | 11.28 | 2550 | 4.0495          |
| 0.1845        | 11.5  | 2600 | 3.8454          |
| 0.1548        | 11.73 | 2650 | 4.2611          |
| 0.1009        | 11.95 | 2700 | 4.1706          |
| 0.1412        | 12.17 | 2750 | 4.2072          |
| 0.0809        | 12.39 | 2800 | 4.2862          |
| 0.1126        | 12.61 | 2850 | 4.0895          |
| 0.0736        | 12.83 | 2900 | 4.2500          |
| 0.0525        | 13.05 | 2950 | 4.3110          |
| 0.0621        | 13.27 | 3000 | 4.1005          |
| 0.1199        | 13.5  | 3050 | 4.1956          |
| 0.0837        | 13.72 | 3100 | 4.6910          |
| 0.1912        | 13.94 | 3150 | 4.2988          |
| 0.0891        | 14.16 | 3200 | 4.3609          |
| 0.0478        | 14.38 | 3250 | 4.3829          |
| 0.0727        | 14.6  | 3300 | 4.2299          |
| 0.1063        | 14.82 | 3350 | 3.9861          |
| 0.0392        | 15.04 | 3400 | 4.1840          |
| 0.1334        | 15.27 | 3450 | 4.3275          |
| 0.0178        | 15.49 | 3500 | 4.3959          |
| 0.0917        | 15.71 | 3550 | 4.4633          |
| 0.0477        | 15.93 | 3600 | 4.5239          |
| 0.0058        | 16.15 | 3650 | 4.5556          |
| 0.0719        | 16.37 | 3700 | 4.5381          |
| 0.0412        | 16.59 | 3750 | 4.5468          |
| 0.0051        | 16.81 | 3800 | 4.6192          |
| 0.0437        | 17.04 | 3850 | 4.6033          |
| 0.0062        | 17.26 | 3900 | 4.7104          |
| 0.0374        | 17.48 | 3950 | 4.5678          |
| 0.0113        | 17.7  | 4000 | 4.6266          |
| 0.0616        | 17.92 | 4050 | 4.6180          |
| 0.0082        | 18.14 | 4100 | 4.7478          |
| 0.0034        | 18.36 | 4150 | 4.7665          |
| 0.0395        | 18.58 | 4200 | 4.7568          |
| 0.0108        | 18.81 | 4250 | 4.7385          |
| 0.015         | 19.03 | 4300 | 4.8266          |
| 0.1061        | 19.25 | 4350 | 4.8090          |
| 0.0181        | 19.47 | 4400 | 4.8226          |
| 0.0055        | 19.69 | 4450 | 4.8228          |
| 0.0134        | 19.91 | 4500 | 4.8270          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.10.1
- Tokenizers 0.14.1