---
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: 0.920911104080987
    - name: Recall
      type: recall
      value: 0.9651856763925729
    - name: F1
      type: f1
      value: 0.9425287356321839
    - name: Accuracy
      type: accuracy
      value: 0.9821007282798235
---

<!-- 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: 0.0614
- Precision: 0.9209
- Recall: 0.9652
- F1: 0.9425
- Accuracy: 0.9821

## 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: 3e-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  | 0.0761          | 0.9321    | 0.9324 | 0.9322 | 0.9797   |
| 0.1488        | 4.7619  | 500  | 0.0675          | 0.9074    | 0.9655 | 0.9356 | 0.9799   |
| 0.1488        | 7.1429  | 750  | 0.0629          | 0.9152    | 0.9622 | 0.9381 | 0.9808   |
| 0.0384        | 9.5238  | 1000 | 0.0620          | 0.9175    | 0.9655 | 0.9409 | 0.9816   |
| 0.0384        | 11.9048 | 1250 | 0.0620          | 0.9215    | 0.9652 | 0.9428 | 0.9822   |
| 0.0313        | 14.2857 | 1500 | 0.0614          | 0.9209    | 0.9652 | 0.9425 | 0.9821   |


### Framework versions

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