---
tags:
- generated_from_trainer
datasets:
- sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-sroie
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: sroie
      type: sroie
      args: sroie
    metrics:
    - name: Precision
      type: precision
      value: 0.9362154500354358
    - name: Recall
      type: recall
      value: 0.9517291066282421
    - name: F1
      type: f1
      value: 0.9439085387638442
    - name: Accuracy
      type: accuracy
      value: 0.9951776838044365
---

<!-- 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 [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0288
- Precision: 0.9362
- Recall: 0.9517
- F1: 0.9439
- 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: 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        | 0.32  | 100  | 0.1063          | 0.6851    | 0.6599 | 0.6723 | 0.9739   |
| No log        | 0.64  | 200  | 0.0583          | 0.7849    | 0.7860 | 0.7855 | 0.9843   |
| No log        | 0.96  | 300  | 0.0475          | 0.8463    | 0.8610 | 0.8536 | 0.9884   |
| No log        | 1.28  | 400  | 0.0437          | 0.8566    | 0.8739 | 0.8652 | 0.9894   |
| 0.1215        | 1.6   | 500  | 0.0424          | 0.8616    | 0.9063 | 0.8834 | 0.9895   |
| 0.1215        | 1.92  | 600  | 0.0332          | 0.8702    | 0.9323 | 0.9002 | 0.9924   |
| 0.1215        | 2.24  | 700  | 0.0318          | 0.8979    | 0.9373 | 0.9172 | 0.9932   |
| 0.1215        | 2.56  | 800  | 0.0316          | 0.9092    | 0.9445 | 0.9265 | 0.9936   |
| 0.1215        | 2.88  | 900  | 0.0295          | 0.8982    | 0.9467 | 0.9218 | 0.9937   |
| 0.0286        | 3.19  | 1000 | 0.0329          | 0.8685    | 0.9517 | 0.9082 | 0.9930   |
| 0.0286        | 3.51  | 1100 | 0.0289          | 0.9298    | 0.9352 | 0.9325 | 0.9945   |
| 0.0286        | 3.83  | 1200 | 0.0287          | 0.9202    | 0.9474 | 0.9336 | 0.9946   |
| 0.0286        | 4.15  | 1300 | 0.0301          | 0.9174    | 0.9524 | 0.9346 | 0.9947   |
| 0.0286        | 4.47  | 1400 | 0.0268          | 0.9212    | 0.9431 | 0.9320 | 0.9946   |
| 0.017         | 4.79  | 1500 | 0.0307          | 0.9236    | 0.9488 | 0.9360 | 0.9944   |
| 0.017         | 5.11  | 1600 | 0.0286          | 0.9335    | 0.9503 | 0.9418 | 0.9951   |
| 0.017         | 5.43  | 1700 | 0.0287          | 0.9284    | 0.9618 | 0.9448 | 0.9951   |
| 0.017         | 5.75  | 1800 | 0.0278          | 0.9334    | 0.9496 | 0.9414 | 0.9952   |
| 0.017         | 6.07  | 1900 | 0.0289          | 0.9337    | 0.9539 | 0.9437 | 0.9952   |
| 0.0111        | 6.39  | 2000 | 0.0288          | 0.9362    | 0.9517 | 0.9439 | 0.9952   |


### Framework versions

- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1