---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-invoice
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: layoutlmv3
      type: layoutlmv3
      config: InvoiceExtraction
      split: test
      args: InvoiceExtraction
    metrics:
    - name: Precision
      type: precision
      value: 0.9698412698412698
    - name: Recall
      type: recall
      value: 0.9591836734693877
    - name: F1
      type: f1
      value: 0.9644830307813733
    - name: Accuracy
      type: accuracy
      value: 0.9708383961117861
---

<!-- 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-invoice

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1889
- Precision: 0.9698
- Recall: 0.9592
- F1: 0.9645
- Accuracy: 0.9708

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 3500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 5.32  | 250  | 0.3826          | 0.9471    | 0.9278 | 0.9374 | 0.9465   |
| 0.8433        | 10.64 | 500  | 0.1720          | 0.9697    | 0.9560 | 0.9628 | 0.9684   |
| 0.8433        | 15.96 | 750  | 0.1631          | 0.9714    | 0.9608 | 0.9661 | 0.9684   |
| 0.0347        | 21.28 | 1000 | 0.1548          | 0.9746    | 0.9639 | 0.9692 | 0.9733   |
| 0.0347        | 26.6  | 1250 | 0.1700          | 0.9698    | 0.9576 | 0.9637 | 0.9672   |
| 0.0116        | 31.91 | 1500 | 0.1812          | 0.9667    | 0.9576 | 0.9621 | 0.9648   |
| 0.0116        | 37.23 | 1750 | 0.1513          | 0.9683    | 0.9592 | 0.9637 | 0.9721   |
| 0.0066        | 42.55 | 2000 | 0.1555          | 0.9730    | 0.9623 | 0.9676 | 0.9757   |
| 0.0066        | 47.87 | 2250 | 0.1729          | 0.9714    | 0.9592 | 0.9652 | 0.9708   |
| 0.0048        | 53.19 | 2500 | 0.1854          | 0.9761    | 0.9623 | 0.9692 | 0.9721   |
| 0.0048        | 58.51 | 2750 | 0.1863          | 0.9714    | 0.9592 | 0.9652 | 0.9696   |
| 0.0037        | 63.83 | 3000 | 0.1813          | 0.9761    | 0.9623 | 0.9692 | 0.9733   |
| 0.0037        | 69.15 | 3250 | 0.1903          | 0.9698    | 0.9592 | 0.9645 | 0.9708   |
| 0.0034        | 74.47 | 3500 | 0.1889          | 0.9698    | 0.9592 | 0.9645 | 0.9708   |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0