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# 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: 5.2024

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2725        | 0.22  | 50   | 4.6295          |
| 4.532         | 0.44  | 100  | 4.2254          |
| 4.0834        | 0.66  | 150  | 3.8568          |
| 3.9787        | 0.88  | 200  | 3.6523          |
| 3.6224        | 1.11  | 250  | 3.8143          |
| 3.2397        | 1.33  | 300  | 3.1685          |
| 3.0931        | 1.55  | 350  | 3.0822          |
| 2.9593        | 1.77  | 400  | 2.9521          |
| 2.5904        | 1.99  | 450  | 2.7995          |
| 2.1358        | 2.21  | 500  | 2.4827          |
| 2.0137        | 2.43  | 550  | 2.3651          |
| 1.951         | 2.65  | 600  | 2.2448          |
| 1.684         | 2.88  | 650  | 2.3455          |
| 1.6708        | 3.1   | 700  | 2.5003          |
| 1.3927        | 3.32  | 750  | 2.3116          |
| 1.5237        | 3.54  | 800  | 2.6236          |
| 1.2826        | 3.76  | 850  | 2.4859          |
| 1.5274        | 3.98  | 900  | 2.1857          |
| 1.0727        | 4.2   | 950  | 2.5041          |
| 0.9465        | 4.42  | 1000 | 2.7958          |
| 1.0889        | 4.65  | 1050 | 2.3797          |
| 0.9121        | 4.87  | 1100 | 2.7570          |
| 0.8847        | 5.09  | 1150 | 3.0968          |
| 0.8864        | 5.31  | 1200 | 2.8488          |
| 0.8693        | 5.53  | 1250 | 2.6848          |
| 0.5451        | 5.75  | 1300 | 3.3272          |
| 0.8121        | 5.97  | 1350 | 3.6097          |
| 0.6214        | 6.19  | 1400 | 3.1954          |
| 0.5576        | 6.42  | 1450 | 3.2427          |
| 0.5576        | 6.64  | 1500 | 3.4471          |
| 0.4858        | 6.86  | 1550 | 3.2469          |
| 0.5947        | 7.08  | 1600 | 3.2522          |
| 0.4889        | 7.3   | 1650 | 3.3459          |
| 0.3126        | 7.52  | 1700 | 3.9616          |
| 0.3291        | 7.74  | 1750 | 3.9943          |
| 0.5337        | 7.96  | 1800 | 3.6498          |
| 0.2384        | 8.19  | 1850 | 4.2966          |
| 0.3566        | 8.41  | 1900 | 4.1365          |
| 0.3539        | 8.63  | 1950 | 4.1291          |
| 0.3219        | 8.85  | 2000 | 4.3024          |
| 0.2307        | 9.07  | 2050 | 4.1780          |
| 0.1922        | 9.29  | 2100 | 4.4078          |
| 0.1721        | 9.51  | 2150 | 4.2569          |
| 0.1541        | 9.73  | 2200 | 4.2138          |
| 0.3044        | 9.96  | 2250 | 4.2793          |
| 0.2642        | 10.18 | 2300 | 4.2676          |
| 0.1725        | 10.4  | 2350 | 4.0887          |
| 0.2223        | 10.62 | 2400 | 3.9813          |
| 0.2107        | 10.84 | 2450 | 4.1089          |
| 0.3262        | 11.06 | 2500 | 3.9025          |
| 0.383         | 11.28 | 2550 | 4.1511          |
| 0.1437        | 11.5  | 2600 | 4.1774          |
| 0.2707        | 11.73 | 2650 | 4.1749          |
| 0.1692        | 11.95 | 2700 | 4.3012          |
| 0.1651        | 12.17 | 2750 | 4.3723          |
| 0.1388        | 12.39 | 2800 | 4.3273          |
| 0.1072        | 12.61 | 2850 | 4.7238          |
| 0.1748        | 12.83 | 2900 | 4.3425          |
| 0.1053        | 13.05 | 2950 | 4.0696          |
| 0.1929        | 13.27 | 3000 | 4.6322          |
| 0.028         | 13.5  | 3050 | 4.4843          |
| 0.0207        | 13.72 | 3100 | 4.9324          |
| 0.0662        | 13.94 | 3150 | 4.9421          |
| 0.0644        | 14.16 | 3200 | 4.8991          |
| 0.0321        | 14.38 | 3250 | 4.7757          |
| 0.0567        | 14.6  | 3300 | 4.9158          |
| 0.0552        | 14.82 | 3350 | 5.0722          |
| 0.0695        | 15.04 | 3400 | 5.0160          |
| 0.081         | 15.27 | 3450 | 5.1969          |
| 0.084         | 15.49 | 3500 | 5.2285          |
| 0.0372        | 15.71 | 3550 | 5.2621          |
| 0.1193        | 15.93 | 3600 | 5.1806          |
| 0.0166        | 16.15 | 3650 | 5.2799          |
| 0.029         | 16.37 | 3700 | 5.2543          |
| 0.048         | 16.59 | 3750 | 5.1176          |
| 0.143         | 16.81 | 3800 | 5.1800          |
| 0.033         | 17.04 | 3850 | 5.1635          |
| 0.0424        | 17.26 | 3900 | 5.1982          |
| 0.004         | 17.48 | 3950 | 5.2322          |
| 0.0143        | 17.7  | 4000 | 5.2242          |
| 0.0261        | 17.92 | 4050 | 5.3110          |
| 0.0076        | 18.14 | 4100 | 5.3329          |
| 0.0036        | 18.36 | 4150 | 5.3355          |
| 0.0182        | 18.58 | 4200 | 5.3223          |
| 0.0466        | 18.81 | 4250 | 5.2396          |
| 0.0036        | 19.03 | 4300 | 5.2409          |
| 0.0278        | 19.25 | 4350 | 5.2128          |
| 0.015         | 19.47 | 4400 | 5.2227          |
| 0.0394        | 19.69 | 4450 | 5.2018          |
| 0.0034        | 19.91 | 4500 | 5.2024          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "base_model": "microsoft/layoutlmv2-base-uncased", "model-index": [{"name": "layoutlmv2-base-uncased_finetuned_docvqa", "results": []}]}
transformers tensorboard safetensors layoutlmv2 document-question-answering generated_from_trainer base_model:microsoft/layoutlmv2-base-uncased license:cc-by-nc-sa-4.0 endpoints_compatible region:us