---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- generated
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: generated
      type: generated
      config: sroie
      split: train
      args: sroie
    metrics:
    - name: Precision
      type: precision
      value: 0.9959514170040485
    - name: Recall
      type: recall
      value: 0.9979716024340771
    - name: F1
      type: f1
      value: 0.9969604863221885
    - name: Accuracy
      type: accuracy
      value: 0.9995786812723826
---

<!-- 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-invoice

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0028
- Precision: 0.9960
- Recall: 0.9980
- F1: 0.9970
- Accuracy: 0.9996

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.0   | 100  | 0.0502          | 0.97      | 0.9838 | 0.9768 | 0.9968   |
| No log        | 4.0   | 200  | 0.0194          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 6.0   | 300  | 0.0160          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| No log        | 8.0   | 400  | 0.0123          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
| 0.053         | 10.0  | 500  | 0.0089          | 0.9757    | 0.9757 | 0.9757 | 0.9966   |
| 0.053         | 12.0  | 600  | 0.0058          | 0.9959    | 0.9919 | 0.9939 | 0.9992   |
| 0.053         | 14.0  | 700  | 0.0046          | 0.9939    | 0.9919 | 0.9929 | 0.9989   |
| 0.053         | 16.0  | 800  | 0.0037          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.053         | 18.0  | 900  | 0.0068          | 0.9959    | 0.9878 | 0.9919 | 0.9987   |
| 0.0057        | 20.0  | 1000 | 0.0054          | 0.9919    | 0.9959 | 0.9939 | 0.9992   |
| 0.0057        | 22.0  | 1100 | 0.0057          | 0.9919    | 0.9959 | 0.9939 | 0.9992   |
| 0.0057        | 24.0  | 1200 | 0.0049          | 0.9919    | 0.9959 | 0.9939 | 0.9992   |
| 0.0057        | 26.0  | 1300 | 0.0052          | 0.9919    | 0.9959 | 0.9939 | 0.9992   |
| 0.0057        | 28.0  | 1400 | 0.0030          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0022        | 30.0  | 1500 | 0.0028          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0022        | 32.0  | 1600 | 0.0030          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0022        | 34.0  | 1700 | 0.0030          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0022        | 36.0  | 1800 | 0.0037          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0022        | 38.0  | 1900 | 0.0037          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |
| 0.0017        | 40.0  | 2000 | 0.0037          | 0.9960    | 0.9980 | 0.9970 | 0.9996   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1