---
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- not-lain/sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-sroie
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: not-lain/sroie
      type: not-lain/sroie
    metrics:
    - name: Precision
      type: precision
      value: 0.9435600578871202
    - name: Recall
      type: recall
      value: 0.9421965317919075
    - name: F1
      type: f1
      value: 0.9428778018799712
    - name: Accuracy
      type: accuracy
      value: 0.9952312354080771
---

<!-- 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 not-lain/sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Precision: 0.9436
- Recall: 0.9422
- F1: 0.9429
- Accuracy: 0.9952

## 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: 5e-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: 1000

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.5873  | 100  | 0.0429          | 0.8634    | 0.8772 | 0.8703 | 0.9896   |
| No log        | 3.1746  | 200  | 0.0222          | 0.9079    | 0.9408 | 0.9241 | 0.9947   |
| No log        | 4.7619  | 300  | 0.0285          | 0.9232    | 0.9379 | 0.9305 | 0.9939   |
| No log        | 6.3492  | 400  | 0.0291          | 0.9393    | 0.9393 | 0.9393 | 0.9947   |
| 0.0408        | 7.9365  | 500  | 0.0295          | 0.9223    | 0.9436 | 0.9329 | 0.9945   |
| 0.0408        | 9.5238  | 600  | 0.0356          | 0.9223    | 0.9436 | 0.9329 | 0.9936   |
| 0.0408        | 11.1111 | 700  | 0.0318          | 0.9397    | 0.9451 | 0.9424 | 0.9952   |
| 0.0408        | 12.6984 | 800  | 0.0311          | 0.9312    | 0.9393 | 0.9353 | 0.9948   |
| 0.0408        | 14.2857 | 900  | 0.0324          | 0.9394    | 0.9408 | 0.9401 | 0.9950   |
| 0.0035        | 15.8730 | 1000 | 0.0319          | 0.9436    | 0.9422 | 0.9429 | 0.9952   |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1