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

# layoutlm_qa

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: 4.7055

## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.2983        | 0.22  | 50   | 4.5220          |
| 4.4844        | 0.44  | 100  | 4.1165          |
| 4.1775        | 0.66  | 150  | 3.8581          |
| 3.8202        | 0.88  | 200  | 3.5512          |
| 3.5174        | 1.11  | 250  | 3.9044          |
| 3.3304        | 1.33  | 300  | 3.3451          |
| 3.1339        | 1.55  | 350  | 3.0255          |
| 2.9657        | 1.77  | 400  | 2.9532          |
| 2.7647        | 1.99  | 450  | 3.0166          |
| 2.3376        | 2.21  | 500  | 2.9174          |
| 1.9903        | 2.43  | 550  | 2.7034          |
| 1.9975        | 2.65  | 600  | 2.4877          |
| 1.8642        | 2.88  | 650  | 2.3439          |
| 1.6613        | 3.1   | 700  | 2.3873          |
| 1.4884        | 3.32  | 750  | 2.1284          |
| 1.3033        | 3.54  | 800  | 2.3192          |
| 1.3821        | 3.76  | 850  | 3.0033          |
| 1.4121        | 3.98  | 900  | 2.3074          |
| 1.0226        | 4.2   | 950  | 2.5772          |
| 0.8721        | 4.42  | 1000 | 2.8909          |
| 1.1364        | 4.65  | 1050 | 2.6966          |
| 1.1504        | 4.87  | 1100 | 2.7247          |
| 0.7333        | 5.09  | 1150 | 3.3075          |
| 0.7097        | 5.31  | 1200 | 3.2459          |
| 0.7138        | 5.53  | 1250 | 3.2652          |
| 0.6852        | 5.75  | 1300 | 3.0537          |
| 0.6396        | 5.97  | 1350 | 3.1964          |
| 0.6756        | 6.19  | 1400 | 3.3380          |
| 0.5771        | 6.42  | 1450 | 3.4396          |
| 0.6753        | 6.64  | 1500 | 3.0820          |
| 0.5361        | 6.86  | 1550 | 3.3736          |
| 0.5659        | 7.08  | 1600 | 3.3211          |
| 0.6637        | 7.3   | 1650 | 3.2642          |
| 0.5321        | 7.52  | 1700 | 3.3275          |
| 0.3525        | 7.74  | 1750 | 3.5490          |
| 0.4964        | 7.96  | 1800 | 3.5147          |
| 0.4882        | 8.19  | 1850 | 3.4210          |
| 0.3879        | 8.41  | 1900 | 3.9024          |
| 0.4991        | 8.63  | 1950 | 3.5269          |
| 0.5084        | 8.85  | 2000 | 3.7400          |
| 0.3502        | 9.07  | 2050 | 3.6098          |
| 0.2492        | 9.29  | 2100 | 3.8580          |
| 0.2889        | 9.51  | 2150 | 3.6365          |
| 0.2672        | 9.73  | 2200 | 3.5260          |
| 0.4289        | 9.96  | 2250 | 3.1862          |
| 0.1803        | 10.18 | 2300 | 3.9092          |
| 0.2014        | 10.4  | 2350 | 3.8147          |
| 0.3197        | 10.62 | 2400 | 3.7593          |
| 0.1503        | 10.84 | 2450 | 3.8731          |
| 0.1766        | 11.06 | 2500 | 3.6034          |
| 0.3074        | 11.28 | 2550 | 3.6639          |
| 0.1637        | 11.5  | 2600 | 3.9461          |
| 0.2674        | 11.73 | 2650 | 3.6418          |
| 0.2074        | 11.95 | 2700 | 3.7350          |
| 0.1034        | 12.17 | 2750 | 4.0971          |
| 0.1438        | 12.39 | 2800 | 3.8840          |
| 0.0739        | 12.61 | 2850 | 3.9797          |
| 0.2329        | 12.83 | 2900 | 4.0602          |
| 0.2348        | 13.05 | 2950 | 3.9343          |
| 0.1119        | 13.27 | 3000 | 4.2030          |
| 0.0955        | 13.5  | 3050 | 4.3291          |
| 0.0787        | 13.72 | 3100 | 4.1507          |
| 0.1446        | 13.94 | 3150 | 4.1370          |
| 0.0202        | 14.16 | 3200 | 4.2964          |
| 0.1201        | 14.38 | 3250 | 4.3851          |
| 0.0783        | 14.6  | 3300 | 4.2924          |
| 0.0536        | 14.82 | 3350 | 4.2803          |
| 0.1042        | 15.04 | 3400 | 4.2722          |
| 0.1374        | 15.27 | 3450 | 4.3609          |
| 0.096         | 15.49 | 3500 | 4.3868          |
| 0.0223        | 15.71 | 3550 | 4.3771          |
| 0.0573        | 15.93 | 3600 | 4.4002          |
| 0.0688        | 16.15 | 3650 | 4.4771          |
| 0.0052        | 16.37 | 3700 | 4.5400          |
| 0.0128        | 16.59 | 3750 | 4.5740          |
| 0.0913        | 16.81 | 3800 | 4.6113          |
| 0.0783        | 17.04 | 3850 | 4.2686          |
| 0.0344        | 17.26 | 3900 | 4.3120          |
| 0.0064        | 17.48 | 3950 | 4.4239          |
| 0.1358        | 17.7  | 4000 | 4.5027          |
| 0.0299        | 17.92 | 4050 | 4.5290          |
| 0.0157        | 18.14 | 4100 | 4.6270          |
| 0.0141        | 18.36 | 4150 | 4.6847          |
| 0.0382        | 18.58 | 4200 | 4.6527          |
| 0.0069        | 18.81 | 4250 | 4.5969          |
| 0.0698        | 19.03 | 4300 | 4.6249          |
| 0.0303        | 19.25 | 4350 | 4.6679          |
| 0.0076        | 19.47 | 4400 | 4.7096          |
| 0.0161        | 19.69 | 4450 | 4.7129          |
| 0.0572        | 19.91 | 4500 | 4.7055          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0