---
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: 0.8791872597473915
    - name: Recall
      type: recall
      value: 0.8814865794907089
    - name: F1
      type: f1
      value: 0.8803354182418035
    - name: Accuracy
      type: accuracy
      value: 0.9270261366132221
---

<!-- 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: 0.3081
- Precision: 0.8792
- Recall: 0.8815
- F1: 0.8803
- Accuracy: 0.9270

## 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  | 1.3430          | 0.5041    | 0.1959 | 0.2821 | 0.6414   |
| No log        | 0.63  | 200  | 0.8931          | 0.6739    | 0.5367 | 0.5975 | 0.7786   |
| No log        | 0.95  | 300  | 0.6793          | 0.7332    | 0.6410 | 0.6840 | 0.8273   |
| No log        | 1.26  | 400  | 0.5804          | 0.7659    | 0.7090 | 0.7364 | 0.8507   |
| 1.0357        | 1.58  | 500  | 0.4876          | 0.7919    | 0.7551 | 0.7731 | 0.8723   |
| 1.0357        | 1.89  | 600  | 0.4417          | 0.8009    | 0.7997 | 0.8003 | 0.8857   |
| 1.0357        | 2.21  | 700  | 0.3937          | 0.8256    | 0.8200 | 0.8228 | 0.8973   |
| 1.0357        | 2.52  | 800  | 0.3904          | 0.8143    | 0.8321 | 0.8231 | 0.8958   |
| 1.0357        | 2.84  | 900  | 0.3638          | 0.8462    | 0.8211 | 0.8334 | 0.9010   |
| 0.3989        | 3.15  | 1000 | 0.3586          | 0.8386    | 0.8447 | 0.8417 | 0.9055   |
| 0.3989        | 3.47  | 1100 | 0.3227          | 0.8382    | 0.8564 | 0.8472 | 0.9104   |
| 0.3989        | 3.79  | 1200 | 0.3120          | 0.8538    | 0.8522 | 0.8530 | 0.9119   |
| 0.3989        | 4.1   | 1300 | 0.3283          | 0.8498    | 0.8559 | 0.8528 | 0.9117   |
| 0.3989        | 4.42  | 1400 | 0.3084          | 0.8595    | 0.8606 | 0.8600 | 0.9165   |
| 0.2727        | 4.73  | 1500 | 0.3026          | 0.8552    | 0.8666 | 0.8609 | 0.9159   |
| 0.2727        | 5.05  | 1600 | 0.3052          | 0.8633    | 0.8537 | 0.8585 | 0.9165   |
| 0.2727        | 5.36  | 1700 | 0.3052          | 0.8505    | 0.8747 | 0.8625 | 0.9165   |
| 0.2727        | 5.68  | 1800 | 0.3040          | 0.8579    | 0.8690 | 0.8634 | 0.9164   |
| 0.2727        | 5.99  | 1900 | 0.2926          | 0.8717    | 0.8696 | 0.8707 | 0.9205   |
| 0.2059        | 6.31  | 2000 | 0.3004          | 0.8646    | 0.8753 | 0.8699 | 0.9207   |
| 0.2059        | 6.62  | 2100 | 0.2973          | 0.8711    | 0.8742 | 0.8726 | 0.9215   |
| 0.2059        | 6.94  | 2200 | 0.3010          | 0.8650    | 0.8761 | 0.8705 | 0.9214   |
| 0.2059        | 7.26  | 2300 | 0.3028          | 0.8654    | 0.8760 | 0.8706 | 0.9214   |
| 0.2059        | 7.57  | 2400 | 0.2956          | 0.8769    | 0.8769 | 0.8769 | 0.9260   |
| 0.1617        | 7.89  | 2500 | 0.2871          | 0.8746    | 0.8778 | 0.8762 | 0.9266   |
| 0.1617        | 8.2   | 2600 | 0.3092          | 0.8632    | 0.8797 | 0.8714 | 0.9226   |
| 0.1617        | 8.52  | 2700 | 0.3042          | 0.8834    | 0.8738 | 0.8786 | 0.9265   |
| 0.1617        | 8.83  | 2800 | 0.3092          | 0.8672    | 0.8793 | 0.8732 | 0.9224   |
| 0.1617        | 9.15  | 2900 | 0.3014          | 0.8738    | 0.8841 | 0.8789 | 0.9256   |
| 0.1359        | 9.46  | 3000 | 0.3038          | 0.8763    | 0.8760 | 0.8762 | 0.9249   |
| 0.1359        | 9.78  | 3100 | 0.3087          | 0.8730    | 0.8797 | 0.8763 | 0.9241   |
| 0.1359        | 10.09 | 3200 | 0.3021          | 0.8740    | 0.8812 | 0.8776 | 0.9251   |
| 0.1359        | 10.41 | 3300 | 0.2975          | 0.8790    | 0.8836 | 0.8812 | 0.9268   |
| 0.1359        | 10.73 | 3400 | 0.3121          | 0.8734    | 0.8809 | 0.8771 | 0.9254   |
| 0.1192        | 11.04 | 3500 | 0.3111          | 0.8812    | 0.8794 | 0.8803 | 0.9260   |
| 0.1192        | 11.36 | 3600 | 0.3101          | 0.8785    | 0.8790 | 0.8788 | 0.9261   |
| 0.1192        | 11.67 | 3700 | 0.3082          | 0.8790    | 0.8829 | 0.8809 | 0.9275   |
| 0.1192        | 11.99 | 3800 | 0.3081          | 0.8822    | 0.8830 | 0.8826 | 0.9276   |
| 0.1192        | 12.3  | 3900 | 0.3100          | 0.8800    | 0.8809 | 0.8805 | 0.9269   |
| 0.1065        | 12.62 | 4000 | 0.3081          | 0.8792    | 0.8815 | 0.8803 | 0.9270   |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3