---
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: 0.7572254335260116
    - name: Recall
      type: recall
      value: 0.8136645962732919
    - name: F1
      type: f1
      value: 0.784431137724551
    - name: Accuracy
      type: accuracy
      value: 0.7975382003395586
---

<!-- 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: 1.0695
- Precision: 0.7572
- Recall: 0.8137
- F1: 0.7844
- Accuracy: 0.7975

## 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-06
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.7217        | 6.25  | 500  | 1.8788          | 0.5168    | 0.6095 | 0.5593 | 0.5883   |
| 1.7562        | 12.5  | 1000 | 1.4443          | 0.5337    | 0.6576 | 0.5892 | 0.6795   |
| 1.4387        | 18.75 | 1500 | 1.2162          | 0.6981    | 0.7811 | 0.7373 | 0.7746   |
| 1.2728        | 25.0  | 2000 | 1.1030          | 0.7473    | 0.8106 | 0.7777 | 0.7941   |
| 1.1902        | 31.25 | 2500 | 1.0695          | 0.7572    | 0.8137 | 0.7844 | 0.7975   |


### Framework versions

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