---
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: 0.9474485910129474
    - name: Recall
      type: recall
      value: 0.9658385093167702
    - name: F1
      type: f1
      value: 0.9565551710880431
    - name: Accuracy
      type: accuracy
      value: 0.9613752122241087
---

<!-- 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: 0.2047
- Precision: 0.9474
- Recall: 0.9658
- F1: 0.9566
- Accuracy: 0.9614

## 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: 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: 1500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.125  | 250  | 0.7037          | 0.8188    | 0.8665 | 0.8419 | 0.8587   |
| 1.0839        | 6.25   | 500  | 0.3828          | 0.8926    | 0.9293 | 0.9106 | 0.9223   |
| 1.0839        | 9.375  | 750  | 0.2811          | 0.9371    | 0.9596 | 0.9482 | 0.9469   |
| 0.2469        | 12.5   | 1000 | 0.2295          | 0.9401    | 0.9620 | 0.9509 | 0.9529   |
| 0.2469        | 15.625 | 1250 | 0.2106          | 0.9460    | 0.9658 | 0.9558 | 0.9601   |
| 0.1263        | 18.75  | 1500 | 0.2047          | 0.9474    | 0.9658 | 0.9566 | 0.9614   |


### Framework versions

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