---
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: 0.963984674329502
    - name: Recall
      type: recall
      value: 0.9767080745341615
    - name: F1
      type: f1
      value: 0.9703046664095644
    - name: Accuracy
      type: accuracy
      value: 0.9690152801358234
---

<!-- 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: 0.2087
- Precision: 0.9640
- Recall: 0.9767
- F1: 0.9703
- Accuracy: 0.9690

## 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: 5
- eval_batch_size: 5
- 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        | 1.5625  | 250  | 0.2302          | 0.9594    | 0.9720 | 0.9657 | 0.9656   |
| 0.041         | 3.125   | 500  | 0.2176          | 0.9542    | 0.9705 | 0.9623 | 0.9618   |
| 0.041         | 4.6875  | 750  | 0.1903          | 0.9573    | 0.9736 | 0.9654 | 0.9682   |
| 0.0302        | 6.25    | 1000 | 0.2027          | 0.9602    | 0.9744 | 0.9672 | 0.9660   |
| 0.0302        | 7.8125  | 1250 | 0.2174          | 0.9670    | 0.9775 | 0.9722 | 0.9703   |
| 0.019         | 9.375   | 1500 | 0.2018          | 0.9640    | 0.9775 | 0.9707 | 0.9711   |
| 0.019         | 10.9375 | 1750 | 0.2084          | 0.9677    | 0.9783 | 0.9730 | 0.9694   |
| 0.0115        | 12.5    | 2000 | 0.2087          | 0.9640    | 0.9767 | 0.9703 | 0.9690   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1