---
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: 0.9511823035850496
    - name: Recall
      type: recall
      value: 0.968167701863354
    - name: F1
      type: f1
      value: 0.9595998460946518
    - name: Accuracy
      type: accuracy
      value: 0.9596774193548387
---

<!-- 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: 0.2305
- Precision: 0.9512
- Recall: 0.9682
- F1: 0.9596
- Accuracy: 0.9597

## 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: 2500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.125  | 250  | 0.6703          | 0.8163    | 0.8626 | 0.8388 | 0.8502   |
| 1.0387        | 6.25   | 500  | 0.3617          | 0.8935    | 0.9317 | 0.9122 | 0.9253   |
| 1.0387        | 9.375  | 750  | 0.2860          | 0.9320    | 0.9581 | 0.9449 | 0.9423   |
| 0.2107        | 12.5   | 1000 | 0.2416          | 0.9429    | 0.9620 | 0.9523 | 0.9525   |
| 0.2107        | 15.625 | 1250 | 0.2120          | 0.9550    | 0.9713 | 0.9630 | 0.9631   |
| 0.0887        | 18.75  | 1500 | 0.2211          | 0.9474    | 0.9658 | 0.9566 | 0.9588   |
| 0.0887        | 21.875 | 1750 | 0.2304          | 0.9474    | 0.9658 | 0.9566 | 0.9576   |
| 0.0515        | 25.0   | 2000 | 0.2344          | 0.9526    | 0.9682 | 0.9603 | 0.9584   |
| 0.0515        | 28.125 | 2250 | 0.2300          | 0.9490    | 0.9674 | 0.9581 | 0.9601   |
| 0.0366        | 31.25  | 2500 | 0.2305          | 0.9512    | 0.9682 | 0.9596 | 0.9597   |


### Framework versions

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