---
library_name: transformers
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/cord-sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord-sroie
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/cord-sroie
      type: mp-02/cord-sroie
    metrics:
    - name: Precision
      type: precision
      value: 0.9551958714520291
    - name: Recall
      type: recall
      value: 0.9647003079838901
    - name: F1
      type: f1
      value: 0.9599245638849601
    - name: Accuracy
      type: accuracy
      value: 0.9859886297506603
---

<!-- 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-cord-sroie

This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord-sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0731
- Precision: 0.9552
- Recall: 0.9647
- F1: 0.9599
- Accuracy: 0.9860

## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.7483  | 250  | 0.3308          | 0.8186    | 0.7941 | 0.8062 | 0.9427   |
| 0.7339        | 3.4965  | 500  | 0.1450          | 0.9013    | 0.9195 | 0.9103 | 0.9722   |
| 0.7339        | 5.2448  | 750  | 0.1009          | 0.9314    | 0.9386 | 0.9350 | 0.9791   |
| 0.1726        | 6.9930  | 1000 | 0.0841          | 0.9445    | 0.9562 | 0.9503 | 0.9839   |
| 0.1726        | 8.7413  | 1250 | 0.0776          | 0.9563    | 0.9533 | 0.9548 | 0.9850   |
| 0.0918        | 10.4895 | 1500 | 0.0782          | 0.9450    | 0.9611 | 0.9530 | 0.9844   |
| 0.0918        | 12.2378 | 1750 | 0.0699          | 0.9539    | 0.9602 | 0.9570 | 0.9856   |
| 0.0587        | 13.9860 | 2000 | 0.0743          | 0.9550    | 0.9611 | 0.9581 | 0.9859   |
| 0.0587        | 15.7343 | 2250 | 0.0726          | 0.9596    | 0.9633 | 0.9615 | 0.9863   |
| 0.046         | 17.4825 | 2500 | 0.0731          | 0.9552    | 0.9647 | 0.9599 | 0.9860   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1