---
library_name: transformers
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/cord-sroie
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-base-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: 0.9105022831050228
    - name: Recall
      type: recall
      value: 0.9447998104714522
    - name: F1
      type: f1
      value: 0.9273340309266364
    - name: Accuracy
      type: accuracy
      value: 0.9738126147097005
---

<!-- 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-base-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: 0.0936
- Precision: 0.9105
- Recall: 0.9448
- F1: 0.9273
- Accuracy: 0.9738

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 4000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.7937 | 100  | 0.4396          | 0.6271    | 0.6015 | 0.6140 | 0.9064   |
| No log        | 1.5873 | 200  | 0.2500          | 0.8669    | 0.8394 | 0.8529 | 0.9508   |
| No log        | 2.3810 | 300  | 0.1517          | 0.8682    | 0.9050 | 0.8862 | 0.9634   |
| No log        | 3.1746 | 400  | 0.1346          | 0.8694    | 0.9339 | 0.9005 | 0.9645   |
| 0.6691        | 3.9683 | 500  | 0.0943          | 0.9369    | 0.9325 | 0.9347 | 0.9778   |
| 0.6691        | 4.7619 | 600  | 0.0922          | 0.9049    | 0.9491 | 0.9265 | 0.9742   |
| 0.6691        | 5.5556 | 700  | 0.1106          | 0.8913    | 0.9540 | 0.9216 | 0.9717   |
| 0.6691        | 6.3492 | 800  | 0.0875          | 0.9091    | 0.9552 | 0.9316 | 0.9755   |
| 0.6691        | 7.1429 | 900  | 0.0958          | 0.8977    | 0.9623 | 0.9289 | 0.9743   |
| 0.1055        | 7.9365 | 1000 | 0.0936          | 0.9105    | 0.9448 | 0.9273 | 0.9738   |
| 0.1055        | 8.7302 | 1100 | 0.1035          | 0.9289    | 0.9415 | 0.9352 | 0.9766   |
| 0.1055        | 9.5238 | 1200 | 0.1115          | 0.9081    | 0.9507 | 0.9289 | 0.9739   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3