---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-wildreceipt
  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. -->

# layoutlmv3-finetuned-wildreceipt

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3146
- Precision: 0.8798
- Recall: 0.8807
- F1: 0.8802
- Accuracy: 0.9260

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 4000

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.3155  | 100  | 1.3209          | 0.5494    | 0.3560 | 0.4320 | 0.6831   |
| No log        | 0.6309  | 200  | 0.8725          | 0.6581    | 0.5196 | 0.5807 | 0.7703   |
| No log        | 0.9464  | 300  | 0.6947          | 0.7348    | 0.6701 | 0.7009 | 0.8309   |
| No log        | 1.2618  | 400  | 0.5767          | 0.7727    | 0.7133 | 0.7418 | 0.8519   |
| 1.0485        | 1.5773  | 500  | 0.5011          | 0.7667    | 0.7605 | 0.7636 | 0.8673   |
| 1.0485        | 1.8927  | 600  | 0.4996          | 0.7616    | 0.7818 | 0.7716 | 0.8668   |
| 1.0485        | 2.2082  | 700  | 0.4144          | 0.8010    | 0.7989 | 0.7999 | 0.8867   |
| 1.0485        | 2.5237  | 800  | 0.3943          | 0.8162    | 0.8231 | 0.8196 | 0.8936   |
| 1.0485        | 2.8391  | 900  | 0.3821          | 0.8096    | 0.8340 | 0.8216 | 0.8963   |
| 0.418         | 3.1546  | 1000 | 0.3637          | 0.8350    | 0.8428 | 0.8389 | 0.9031   |
| 0.418         | 3.4700  | 1100 | 0.3422          | 0.8518    | 0.8314 | 0.8415 | 0.9081   |
| 0.418         | 3.7855  | 1200 | 0.3425          | 0.8322    | 0.8579 | 0.8449 | 0.9057   |
| 0.418         | 4.1009  | 1300 | 0.3242          | 0.8556    | 0.8541 | 0.8549 | 0.9132   |
| 0.418         | 4.4164  | 1400 | 0.3267          | 0.8442    | 0.8628 | 0.8534 | 0.9117   |
| 0.28          | 4.7319  | 1500 | 0.3144          | 0.8565    | 0.8603 | 0.8584 | 0.9152   |
| 0.28          | 5.0473  | 1600 | 0.3102          | 0.8707    | 0.8617 | 0.8661 | 0.9182   |
| 0.28          | 5.3628  | 1700 | 0.3291          | 0.8677    | 0.8615 | 0.8646 | 0.9169   |
| 0.28          | 5.6782  | 1800 | 0.2999          | 0.8587    | 0.8717 | 0.8652 | 0.9182   |
| 0.28          | 5.9937  | 1900 | 0.3128          | 0.8596    | 0.8680 | 0.8638 | 0.9184   |
| 0.2106        | 6.3091  | 2000 | 0.3111          | 0.8667    | 0.8725 | 0.8696 | 0.9207   |
| 0.2106        | 6.6246  | 2100 | 0.3053          | 0.8716    | 0.8679 | 0.8697 | 0.9207   |
| 0.2106        | 6.9401  | 2200 | 0.3017          | 0.8683    | 0.8756 | 0.8719 | 0.9221   |
| 0.2106        | 7.2555  | 2300 | 0.2986          | 0.8800    | 0.8702 | 0.8751 | 0.9236   |
| 0.2106        | 7.5710  | 2400 | 0.3012          | 0.8747    | 0.8717 | 0.8732 | 0.9230   |
| 0.1724        | 7.8864  | 2500 | 0.3014          | 0.8696    | 0.8776 | 0.8736 | 0.9232   |
| 0.1724        | 8.2019  | 2600 | 0.3066          | 0.8763    | 0.8725 | 0.8744 | 0.9232   |
| 0.1724        | 8.5174  | 2700 | 0.3181          | 0.8696    | 0.8794 | 0.8745 | 0.9228   |
| 0.1724        | 8.8328  | 2800 | 0.3065          | 0.8724    | 0.8816 | 0.8770 | 0.9237   |
| 0.1724        | 9.1483  | 2900 | 0.3041          | 0.8753    | 0.8818 | 0.8785 | 0.9259   |
| 0.1393        | 9.4637  | 3000 | 0.3205          | 0.8685    | 0.8812 | 0.8748 | 0.9238   |
| 0.1393        | 9.7792  | 3100 | 0.3050          | 0.8705    | 0.8838 | 0.8771 | 0.9253   |
| 0.1393        | 10.0946 | 3200 | 0.3158          | 0.8746    | 0.8838 | 0.8792 | 0.9246   |
| 0.1393        | 10.4101 | 3300 | 0.3086          | 0.8785    | 0.8779 | 0.8782 | 0.9258   |
| 0.1393        | 10.7256 | 3400 | 0.3108          | 0.8801    | 0.8822 | 0.8811 | 0.9274   |
| 0.1222        | 11.0410 | 3500 | 0.3146          | 0.8798    | 0.8807 | 0.8802 | 0.9260   |
| 0.1222        | 11.3565 | 3600 | 0.3111          | 0.8790    | 0.8813 | 0.8802 | 0.9261   |
| 0.1222        | 11.6719 | 3700 | 0.3161          | 0.8769    | 0.8848 | 0.8808 | 0.9261   |
| 0.1222        | 11.9874 | 3800 | 0.3133          | 0.8769    | 0.8823 | 0.8796 | 0.9262   |
| 0.1222        | 12.3028 | 3900 | 0.3137          | 0.8767    | 0.8829 | 0.8798 | 0.9260   |
| 0.1115        | 12.6183 | 4000 | 0.3135          | 0.8766    | 0.8833 | 0.8799 | 0.9262   |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1