---
base_model: layoutlmv3
tags:
- generated_from_trainer
datasets:
- mp-02/cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/cord
      type: mp-02/cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9640397857689365
    - name: Recall
      type: recall
      value: 0.9782608695652174
    - name: F1
      type: f1
      value: 0.9710982658959537
    - name: Accuracy
      type: accuracy
      value: 0.9741086587436333
---

<!-- 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-cord

This model is a fine-tuned version of [layoutlmv3](https://huggingface.co/layoutlmv3) on the mp-02/cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1874
- Precision: 0.9640
- Recall: 0.9783
- F1: 0.9711
- Accuracy: 0.9741

## 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4341        | 6.25  | 500  | 0.1703          | 0.9601    | 0.9720 | 0.9660 | 0.9656   |
| 0.0487        | 12.5  | 1000 | 0.1762          | 0.9662    | 0.9759 | 0.9710 | 0.9703   |
| 0.0185        | 18.75 | 1500 | 0.1913          | 0.9609    | 0.9720 | 0.9664 | 0.9682   |
| 0.0091        | 25.0  | 2000 | 0.1846          | 0.9693    | 0.9821 | 0.9757 | 0.9758   |
| 0.0038        | 31.25 | 2500 | 0.1874          | 0.9640    | 0.9783 | 0.9711 | 0.9741   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1