---
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: 0.96771714066103
    - name: Recall
      type: recall
      value: 0.9774844720496895
    - name: F1
      type: f1
      value: 0.9725762842796446
    - name: Accuracy
      type: accuracy
      value: 0.9711375212224108
---

<!-- 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: 0.1836
- Precision: 0.9677
- Recall: 0.9775
- F1: 0.9726
- Accuracy: 0.9711

## 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: 6e-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  | 0.5222          | 0.8284    | 0.8773 | 0.8522 | 0.8680   |
| No log        | 2.5   | 200  | 0.2594          | 0.9147    | 0.9488 | 0.9314 | 0.9393   |
| No log        | 3.75  | 300  | 0.2472          | 0.9294    | 0.9511 | 0.9401 | 0.9448   |
| No log        | 5.0   | 400  | 0.1958          | 0.9496    | 0.9651 | 0.9573 | 0.9580   |
| 0.4078        | 6.25  | 500  | 0.2005          | 0.9547    | 0.9658 | 0.9602 | 0.9597   |
| 0.4078        | 7.5   | 600  | 0.2083          | 0.9555    | 0.9674 | 0.9614 | 0.9631   |
| 0.4078        | 8.75  | 700  | 0.2104          | 0.9608    | 0.9697 | 0.9652 | 0.9631   |
| 0.4078        | 10.0  | 800  | 0.1793          | 0.9685    | 0.9775 | 0.9730 | 0.9724   |
| 0.4078        | 11.25 | 900  | 0.1972          | 0.9646    | 0.9744 | 0.9695 | 0.9686   |
| 0.0332        | 12.5  | 1000 | 0.1836          | 0.9677    | 0.9775 | 0.9726 | 0.9711   |


### Framework versions

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