---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- invoices
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: invoices
      type: invoices
      config: sroie
      split: train
      args: sroie
    metrics:
    - name: Precision
      type: precision
      value: 0.975
    - name: Recall
      type: recall
      value: 0.975
    - name: F1
      type: f1
      value: 0.975
    - name: Accuracy
      type: accuracy
      value: 0.975
---

<!-- 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 invoices dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2299
- Precision: 0.975
- Recall: 0.975
- F1: 0.975
- Accuracy: 0.975

## 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        | 14.29  | 100  | 0.1616          | 0.975     | 0.975  | 0.975 | 0.975    |
| No log        | 28.57  | 200  | 0.1909          | 0.975     | 0.975  | 0.975 | 0.975    |
| No log        | 42.86  | 300  | 0.2046          | 0.975     | 0.975  | 0.975 | 0.975    |
| No log        | 57.14  | 400  | 0.2134          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.1239        | 71.43  | 500  | 0.2299          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.1239        | 85.71  | 600  | 0.2309          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.1239        | 100.0  | 700  | 0.2342          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.1239        | 114.29 | 800  | 0.2407          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.1239        | 128.57 | 900  | 0.2428          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0007        | 142.86 | 1000 | 0.2449          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0007        | 157.14 | 1100 | 0.2465          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0007        | 171.43 | 1200 | 0.2488          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0007        | 185.71 | 1300 | 0.2515          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0007        | 200.0  | 1400 | 0.2525          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0004        | 214.29 | 1500 | 0.2540          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0004        | 228.57 | 1600 | 0.2557          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0004        | 242.86 | 1700 | 0.2564          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0004        | 257.14 | 1800 | 0.2570          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0004        | 271.43 | 1900 | 0.2573          | 0.975     | 0.975  | 0.975 | 0.975    |
| 0.0003        | 285.71 | 2000 | 0.2574          | 0.975     | 0.975  | 0.975 | 0.975    |


### Framework versions

- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1