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---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- wildreceipt
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-wildreceipt
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wildreceipt
      type: wildreceipt
      config: WildReceipt
      split: test
      args: WildReceipt
    metrics:
    - name: Precision
      type: precision
      value: 0.8738394320043692
    - name: Recall
      type: recall
      value: 0.88093599449415
    - name: F1
      type: f1
      value: 0.8773733634930428
    - name: Accuracy
      type: accuracy
      value: 0.9245552383044147
---

<!-- 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 the wildreceipt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3068
- Precision: 0.8738
- Recall: 0.8809
- F1: 0.8774
- Accuracy: 0.9246

## 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        | 0.32  | 100  | 1.3498          | 0.6130    | 0.3126 | 0.4140 | 0.6742   |
| No log        | 0.63  | 200  | 0.8939          | 0.6665    | 0.5317 | 0.5915 | 0.7815   |
| No log        | 0.95  | 300  | 0.7159          | 0.7311    | 0.6425 | 0.6840 | 0.8161   |
| No log        | 1.26  | 400  | 0.5901          | 0.7554    | 0.6690 | 0.7095 | 0.8405   |
| 1.0677        | 1.58  | 500  | 0.5263          | 0.7632    | 0.7232 | 0.7427 | 0.8578   |
| 1.0677        | 1.89  | 600  | 0.4759          | 0.7871    | 0.7777 | 0.7824 | 0.8774   |
| 1.0677        | 2.21  | 700  | 0.4299          | 0.8054    | 0.8070 | 0.8062 | 0.8890   |
| 1.0677        | 2.52  | 800  | 0.4165          | 0.8064    | 0.8311 | 0.8185 | 0.8937   |
| 1.0677        | 2.84  | 900  | 0.3845          | 0.8344    | 0.8300 | 0.8322 | 0.9005   |
| 0.4267        | 3.15  | 1000 | 0.3540          | 0.8433    | 0.8318 | 0.8375 | 0.9056   |
| 0.4267        | 3.47  | 1100 | 0.3429          | 0.8362    | 0.8540 | 0.8450 | 0.9086   |
| 0.4267        | 3.79  | 1200 | 0.3274          | 0.8451    | 0.8545 | 0.8498 | 0.9105   |
| 0.4267        | 4.1   | 1300 | 0.3433          | 0.8397    | 0.8535 | 0.8466 | 0.9092   |
| 0.4267        | 4.42  | 1400 | 0.3181          | 0.8514    | 0.8604 | 0.8559 | 0.9154   |
| 0.2869        | 4.73  | 1500 | 0.3191          | 0.8472    | 0.8637 | 0.8554 | 0.9129   |
| 0.2869        | 5.05  | 1600 | 0.3128          | 0.8613    | 0.8658 | 0.8635 | 0.9182   |
| 0.2869        | 5.36  | 1700 | 0.3121          | 0.8622    | 0.8695 | 0.8658 | 0.9182   |
| 0.2869        | 5.68  | 1800 | 0.3230          | 0.8473    | 0.8661 | 0.8566 | 0.9140   |
| 0.2869        | 5.99  | 1900 | 0.2986          | 0.8729    | 0.8633 | 0.8681 | 0.9209   |
| 0.2134        | 6.31  | 2000 | 0.3032          | 0.8555    | 0.8694 | 0.8624 | 0.9169   |
| 0.2134        | 6.62  | 2100 | 0.3056          | 0.8705    | 0.8710 | 0.8708 | 0.9220   |
| 0.2134        | 6.94  | 2200 | 0.3122          | 0.8630    | 0.8790 | 0.8709 | 0.9217   |
| 0.2134        | 7.26  | 2300 | 0.3047          | 0.8692    | 0.8778 | 0.8734 | 0.9215   |
| 0.2134        | 7.57  | 2400 | 0.3103          | 0.8701    | 0.8780 | 0.8741 | 0.9225   |
| 0.1661        | 7.89  | 2500 | 0.3080          | 0.8712    | 0.8787 | 0.8749 | 0.9226   |
| 0.1661        | 8.2   | 2600 | 0.3011          | 0.8653    | 0.8834 | 0.8743 | 0.9236   |
| 0.1661        | 8.52  | 2700 | 0.3034          | 0.8735    | 0.8798 | 0.8766 | 0.9247   |
| 0.1661        | 8.83  | 2800 | 0.3054          | 0.8698    | 0.8793 | 0.8745 | 0.9238   |
| 0.1661        | 9.15  | 2900 | 0.3105          | 0.8697    | 0.8812 | 0.8754 | 0.9237   |
| 0.1415        | 9.46  | 3000 | 0.3068          | 0.8738    | 0.8809 | 0.8774 | 0.9246   |
| 0.1415        | 9.78  | 3100 | 0.3086          | 0.8730    | 0.8793 | 0.8761 | 0.9229   |
| 0.1415        | 10.09 | 3200 | 0.3013          | 0.8755    | 0.8830 | 0.8792 | 0.9256   |
| 0.1415        | 10.41 | 3300 | 0.3107          | 0.8692    | 0.8815 | 0.8753 | 0.9241   |
| 0.1415        | 10.73 | 3400 | 0.3073          | 0.8759    | 0.8794 | 0.8777 | 0.9261   |
| 0.1239        | 11.04 | 3500 | 0.3109          | 0.8727    | 0.8819 | 0.8773 | 0.9253   |
| 0.1239        | 11.36 | 3600 | 0.3124          | 0.8723    | 0.8790 | 0.8756 | 0.9243   |
| 0.1239        | 11.67 | 3700 | 0.3171          | 0.8724    | 0.8805 | 0.8764 | 0.9241   |
| 0.1239        | 11.99 | 3800 | 0.3081          | 0.8739    | 0.8804 | 0.8771 | 0.9254   |
| 0.1239        | 12.3  | 3900 | 0.3095          | 0.8735    | 0.8798 | 0.8766 | 0.9254   |
| 0.1106        | 12.62 | 4000 | 0.3094          | 0.8740    | 0.8796 | 0.8768 | 0.9254   |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.3
- Tokenizers 0.13.3