---
library_name: transformers
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: 0.9609494640122511
    - name: Recall
      type: recall
      value: 0.9743788819875776
    - name: F1
      type: f1
      value: 0.9676175790285273
    - 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: 0.1800
- Precision: 0.9609
- Recall: 0.9744
- F1: 0.9676
- 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: 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.25  | 100  | 0.5802          | 0.8140    | 0.8696 | 0.8408 | 0.8574   |
| No log        | 2.5   | 200  | 0.2946          | 0.9013    | 0.9433 | 0.9219 | 0.9329   |
| No log        | 3.75  | 300  | 0.2259          | 0.9409    | 0.9635 | 0.9521 | 0.9571   |
| No log        | 5.0   | 400  | 0.2496          | 0.9376    | 0.9565 | 0.9470 | 0.9482   |
| 0.4497        | 6.25  | 500  | 0.2174          | 0.9399    | 0.9596 | 0.9497 | 0.9546   |
| 0.4497        | 7.5   | 600  | 0.1812          | 0.9535    | 0.9713 | 0.9623 | 0.9648   |
| 0.4497        | 8.75  | 700  | 0.1699          | 0.9587    | 0.9720 | 0.9653 | 0.9699   |
| 0.4497        | 10.0  | 800  | 0.1810          | 0.9625    | 0.9752 | 0.9688 | 0.9690   |
| 0.4497        | 11.25 | 900  | 0.1789          | 0.9647    | 0.9767 | 0.9707 | 0.9694   |
| 0.0416        | 12.5  | 1000 | 0.1800          | 0.9609    | 0.9744 | 0.9676 | 0.9690   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1