---
library_name: transformers
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-sroie
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/sroie
      type: mp-02/sroie
    metrics:
    - name: Precision
      type: precision
      value: 0.9265995453069178
    - name: Recall
      type: recall
      value: 0.9459549071618037
    - name: F1
      type: f1
      value: 0.936177194421657
    - name: Accuracy
      type: accuracy
      value: 0.9808185454918453
---

<!-- 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-sroie

This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0717
- Precision: 0.9266
- Recall: 0.9460
- F1: 0.9362
- Accuracy: 0.9808

## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 2.3810  | 250  | 0.1477          | 0.8827    | 0.8654 | 0.8739 | 0.9628   |
| 0.3014        | 4.7619  | 500  | 0.0899          | 0.9138    | 0.9274 | 0.9205 | 0.9759   |
| 0.3014        | 7.1429  | 750  | 0.0765          | 0.9257    | 0.9377 | 0.9316 | 0.9795   |
| 0.0669        | 9.5238  | 1000 | 0.0717          | 0.9266    | 0.9460 | 0.9362 | 0.9808   |
| 0.0669        | 11.9048 | 1250 | 0.0713          | 0.9216    | 0.9476 | 0.9344 | 0.9803   |
| 0.0562        | 14.2857 | 1500 | 0.0712          | 0.9203    | 0.9493 | 0.9346 | 0.9803   |


### Framework versions

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