---
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: 0.9587155963302753
    - name: Recall
      type: recall
      value: 0.9736024844720497
    - name: F1
      type: f1
      value: 0.9661016949152543
    - name: Accuracy
      type: accuracy
      value: 0.9656196943972836
---

<!-- 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: 0.2067
- Precision: 0.9587
- Recall: 0.9736
- F1: 0.9661
- Accuracy: 0.9656

## 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: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.7313  | 250  | 0.6302          | 0.8384    | 0.8742 | 0.8559 | 0.8633   |
| 1.0061        | 7.4627  | 500  | 0.3168          | 0.9144    | 0.9457 | 0.9298 | 0.9372   |
| 1.0061        | 11.1940 | 750  | 0.2460          | 0.9444    | 0.9620 | 0.9531 | 0.9533   |
| 0.1885        | 14.9254 | 1000 | 0.2109          | 0.9534    | 0.9689 | 0.9611 | 0.9622   |
| 0.1885        | 18.6567 | 1250 | 0.2030          | 0.9571    | 0.9705 | 0.9638 | 0.9618   |
| 0.0721        | 22.3881 | 1500 | 0.1985          | 0.9562    | 0.9666 | 0.9614 | 0.9626   |
| 0.0721        | 26.1194 | 1750 | 0.2002          | 0.9557    | 0.9705 | 0.9630 | 0.9656   |
| 0.04          | 29.8507 | 2000 | 0.2086          | 0.9579    | 0.9720 | 0.9649 | 0.9643   |
| 0.04          | 33.5821 | 2250 | 0.2032          | 0.9587    | 0.9736 | 0.9661 | 0.9660   |
| 0.0313        | 37.3134 | 2500 | 0.2067          | 0.9587    | 0.9736 | 0.9661 | 0.9656   |


### Framework versions

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