---
library_name: transformers
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_on_1200
  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_on_1200

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: 4.6669

## 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 |
|:-------------:|:-------:|:----:|:---------------:|
| 5.26          | 0.2212  | 50   | 4.5357          |
| 4.3552        | 0.4425  | 100  | 4.0284          |
| 4.0237        | 0.6637  | 150  | 3.7961          |
| 3.7428        | 0.8850  | 200  | 3.5727          |
| 3.6213        | 1.1062  | 250  | 3.7866          |
| 3.2334        | 1.3274  | 300  | 3.1121          |
| 3.0382        | 1.5487  | 350  | 2.9537          |
| 2.8353        | 1.7699  | 400  | 2.8318          |
| 2.4759        | 1.9912  | 450  | 2.6736          |
| 1.9881        | 2.2124  | 500  | 3.0365          |
| 1.9279        | 2.4336  | 550  | 2.4144          |
| 1.9336        | 2.6549  | 600  | 2.1754          |
| 1.772         | 2.8761  | 650  | 2.1086          |
| 1.5504        | 3.0973  | 700  | 2.7056          |
| 1.4621        | 3.3186  | 750  | 2.8930          |
| 1.4227        | 3.5398  | 800  | 2.4620          |
| 1.3924        | 3.7611  | 850  | 2.1275          |
| 1.3063        | 3.9823  | 900  | 2.2443          |
| 1.0697        | 4.2035  | 950  | 2.6747          |
| 0.9476        | 4.4248  | 1000 | 2.7229          |
| 1.0868        | 4.6460  | 1050 | 2.9257          |
| 0.8726        | 4.8673  | 1100 | 2.7007          |
| 0.9436        | 5.0885  | 1150 | 2.8765          |
| 0.7219        | 5.3097  | 1200 | 2.5301          |
| 0.6919        | 5.5310  | 1250 | 2.9763          |
| 0.491         | 5.7522  | 1300 | 3.1198          |
| 0.5382        | 5.9735  | 1350 | 3.0883          |
| 0.462         | 6.1947  | 1400 | 3.2955          |
| 0.6533        | 6.4159  | 1450 | 3.3370          |
| 0.6477        | 6.6372  | 1500 | 3.3794          |
| 0.4849        | 6.8584  | 1550 | 3.3798          |
| 0.4881        | 7.0796  | 1600 | 3.2085          |
| 0.3952        | 7.3009  | 1650 | 3.2885          |
| 0.161         | 7.5221  | 1700 | 3.6201          |
| 0.6895        | 7.7434  | 1750 | 3.4253          |
| 0.4638        | 7.9646  | 1800 | 3.4787          |
| 0.2186        | 8.1858  | 1850 | 3.7668          |
| 0.2531        | 8.4071  | 1900 | 3.7723          |
| 0.3971        | 8.6283  | 1950 | 3.7131          |
| 0.5665        | 8.8496  | 2000 | 3.5627          |
| 0.3377        | 9.0708  | 2050 | 3.1885          |
| 0.208         | 9.2920  | 2100 | 3.3734          |
| 0.1775        | 9.5133  | 2150 | 4.0609          |
| 0.3295        | 9.7345  | 2200 | 3.7039          |
| 0.2627        | 9.9558  | 2250 | 3.6028          |
| 0.1988        | 10.1770 | 2300 | 3.6288          |
| 0.1772        | 10.3982 | 2350 | 3.5394          |
| 0.0719        | 10.6195 | 2400 | 4.2068          |
| 0.1629        | 10.8407 | 2450 | 4.2701          |
| 0.1921        | 11.0619 | 2500 | 4.0440          |
| 0.164         | 11.2832 | 2550 | 3.9099          |
| 0.1281        | 11.5044 | 2600 | 3.7753          |
| 0.0586        | 11.7257 | 2650 | 3.9491          |
| 0.1436        | 11.9469 | 2700 | 4.2734          |
| 0.0405        | 12.1681 | 2750 | 4.4347          |
| 0.0664        | 12.3894 | 2800 | 4.2338          |
| 0.0864        | 12.6106 | 2850 | 3.8694          |
| 0.103         | 12.8319 | 2900 | 3.9883          |
| 0.0456        | 13.0531 | 2950 | 4.5064          |
| 0.05          | 13.2743 | 3000 | 4.1434          |
| 0.0436        | 13.4956 | 3050 | 4.3928          |
| 0.0798        | 13.7168 | 3100 | 4.5576          |
| 0.0919        | 13.9381 | 3150 | 4.4114          |
| 0.0988        | 14.1593 | 3200 | 4.4998          |
| 0.0332        | 14.3805 | 3250 | 4.3948          |
| 0.0326        | 14.6018 | 3300 | 4.3823          |
| 0.0434        | 14.8230 | 3350 | 4.2468          |
| 0.0926        | 15.0442 | 3400 | 4.3909          |
| 0.027         | 15.2655 | 3450 | 4.5539          |
| 0.047         | 15.4867 | 3500 | 4.5799          |
| 0.0189        | 15.7080 | 3550 | 4.3943          |
| 0.0096        | 15.9292 | 3600 | 4.4218          |
| 0.0467        | 16.1504 | 3650 | 4.6181          |
| 0.0144        | 16.3717 | 3700 | 4.5609          |
| 0.0339        | 16.5929 | 3750 | 4.5994          |
| 0.074         | 16.8142 | 3800 | 4.5598          |
| 0.018         | 17.0354 | 3850 | 4.5528          |
| 0.0043        | 17.2566 | 3900 | 4.6133          |
| 0.0179        | 17.4779 | 3950 | 4.5414          |
| 0.039         | 17.6991 | 4000 | 4.4690          |
| 0.0134        | 17.9204 | 4050 | 4.4789          |
| 0.0094        | 18.1416 | 4100 | 4.5317          |
| 0.004         | 18.3628 | 4150 | 4.5711          |
| 0.0064        | 18.5841 | 4200 | 4.6237          |
| 0.0505        | 18.8053 | 4250 | 4.6148          |
| 0.0312        | 19.0265 | 4300 | 4.6302          |
| 0.0127        | 19.2478 | 4350 | 4.6577          |
| 0.0169        | 19.4690 | 4400 | 4.6685          |
| 0.0192        | 19.6903 | 4450 | 4.6626          |
| 0.0232        | 19.9115 | 4500 | 4.6669          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1