---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv2-large-uncased
tags:
- generated_from_trainer
model-index:
- name: layout2_large
  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. -->

# layout2_large

This model is a fine-tuned version of [microsoft/layoutlmv2-large-uncased](https://huggingface.co/microsoft/layoutlmv2-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2238

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6695        | 0.22  | 50   | 2.4612          |
| 2.3221        | 0.44  | 100  | 1.9209          |
| 2.1651        | 0.66  | 150  | 1.7838          |
| 1.7644        | 0.88  | 200  | 1.9484          |
| 1.8272        | 1.11  | 250  | 1.7739          |
| 1.2938        | 1.33  | 300  | 1.7310          |
| 1.477         | 1.55  | 350  | 1.7254          |
| 1.4623        | 1.77  | 400  | 1.4813          |
| 1.2976        | 1.99  | 450  | 1.8113          |
| 0.8746        | 2.21  | 500  | 2.0039          |
| 0.8223        | 2.43  | 550  | 2.5449          |
| 1.0367        | 2.65  | 600  | 2.3888          |
| 1.0897        | 2.88  | 650  | 1.8641          |
| 0.6269        | 3.1   | 700  | 3.3845          |
| 0.7064        | 3.32  | 750  | 3.5282          |
| 0.8204        | 3.54  | 800  | 2.2839          |
| 0.9558        | 3.76  | 850  | 3.1004          |
| 0.8255        | 3.98  | 900  | 2.5468          |
| 0.778         | 4.2   | 950  | 4.6735          |
| 0.8192        | 4.42  | 1000 | 3.7428          |
| 0.798         | 4.65  | 1050 | 3.7837          |
| 0.5329        | 4.87  | 1100 | 3.2764          |
| 0.7568        | 5.09  | 1150 | 3.5623          |
| 0.5929        | 5.31  | 1200 | 3.1992          |
| 0.4427        | 5.53  | 1250 | 4.6837          |
| 0.7004        | 5.75  | 1300 | 2.6854          |
| 0.4315        | 5.97  | 1350 | 3.0840          |
| 0.2394        | 6.19  | 1400 | 4.3777          |
| 0.4605        | 6.42  | 1450 | 4.0313          |
| 0.4959        | 6.64  | 1500 | 3.3943          |
| 0.618         | 6.86  | 1550 | 3.4803          |
| 0.254         | 7.08  | 1600 | 4.0659          |
| 0.2255        | 7.3   | 1650 | 3.5000          |
| 0.3048        | 7.52  | 1700 | 4.0518          |
| 0.5369        | 7.74  | 1750 | 3.9500          |
| 0.3641        | 7.96  | 1800 | 3.7982          |
| 0.1725        | 8.19  | 1850 | 3.8756          |
| 0.3369        | 8.41  | 1900 | 4.1205          |
| 0.3522        | 8.63  | 1950 | 4.3853          |
| 0.4681        | 8.85  | 2000 | 3.2152          |
| 0.0857        | 9.07  | 2050 | 4.1299          |
| 0.36          | 9.29  | 2100 | 3.5658          |
| 0.3325        | 9.51  | 2150 | 3.8848          |
| 0.1645        | 9.73  | 2200 | 3.8714          |
| 0.2046        | 9.96  | 2250 | 3.9900          |
| 0.1267        | 10.18 | 2300 | 4.6510          |
| 0.2284        | 10.4  | 2350 | 4.1686          |
| 0.173         | 10.62 | 2400 | 4.0575          |
| 0.219         | 10.84 | 2450 | 4.2034          |
| 0.1107        | 11.06 | 2500 | 4.5646          |
| 0.0916        | 11.28 | 2550 | 4.9722          |
| 0.1358        | 11.5  | 2600 | 5.0297          |
| 0.0681        | 11.73 | 2650 | 5.2933          |
| 0.0447        | 11.95 | 2700 | 5.4102          |
| 0.0655        | 12.17 | 2750 | 5.8173          |
| 0.0694        | 12.39 | 2800 | 6.3645          |
| 0.1717        | 12.61 | 2850 | 5.4551          |
| 0.0197        | 12.83 | 2900 | 5.3129          |
| 0.0319        | 13.05 | 2950 | 5.2423          |
| 0.0           | 13.27 | 3000 | 5.3664          |
| 0.124         | 13.5  | 3050 | 5.2445          |
| 0.0025        | 13.72 | 3100 | 5.5092          |
| 0.1243        | 13.94 | 3150 | 5.3060          |
| 0.071         | 14.16 | 3200 | 5.3991          |
| 0.0301        | 14.38 | 3250 | 5.2129          |
| 0.0059        | 14.6  | 3300 | 5.3309          |
| 0.1027        | 14.82 | 3350 | 4.6790          |
| 0.0001        | 15.04 | 3400 | 5.4300          |
| 0.0           | 15.27 | 3450 | 5.4420          |
| 0.0046        | 15.49 | 3500 | 5.7107          |
| 0.0488        | 15.71 | 3550 | 5.3521          |
| 0.0797        | 15.93 | 3600 | 4.9206          |
| 0.06          | 16.15 | 3650 | 4.6152          |
| 0.0295        | 16.37 | 3700 | 4.6441          |
| 0.0126        | 16.59 | 3750 | 4.8442          |
| 0.0           | 16.81 | 3800 | 4.8838          |
| 0.0004        | 17.04 | 3850 | 5.0888          |
| 0.0           | 17.26 | 3900 | 5.1360          |
| 0.0003        | 17.48 | 3950 | 5.4019          |
| 0.0583        | 17.7  | 4000 | 5.0542          |
| 0.0           | 17.92 | 4050 | 4.9908          |
| 0.0001        | 18.14 | 4100 | 5.1099          |
| 0.0194        | 18.36 | 4150 | 5.1146          |
| 0.0002        | 18.58 | 4200 | 5.2076          |
| 0.0           | 18.81 | 4250 | 5.3454          |
| 0.0249        | 19.03 | 4300 | 5.3082          |
| 0.0329        | 19.25 | 4350 | 5.2274          |
| 0.0           | 19.47 | 4400 | 5.2129          |
| 0.0           | 19.69 | 4450 | 5.2155          |
| 0.0009        | 19.91 | 4500 | 5.2238          |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.0+cu118
- Datasets 2.17.1
- Tokenizers 0.13.2