---
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: 0.9672131147540983
    - name: Recall
      type: recall
      value: 0.9776304888152444
    - name: F1
      type: f1
      value: 0.9723939019365472
    - name: Accuracy
      type: accuracy
      value: 0.9766697163769442
---

<!-- 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: 0.1292
- Precision: 0.9672
- Recall: 0.9776
- F1: 0.9724
- Accuracy: 0.9767

## 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  | 0.6018          | 0.8218    | 0.8633 | 0.8420 | 0.8577   |
| 1.0098        | 6.25   | 500  | 0.2695          | 0.9205    | 0.9495 | 0.9347 | 0.9451   |
| 1.0098        | 9.375  | 750  | 0.1813          | 0.9528    | 0.9693 | 0.9610 | 0.9639   |
| 0.1993        | 12.5   | 1000 | 0.1557          | 0.9616    | 0.9743 | 0.9679 | 0.9739   |
| 0.1993        | 15.625 | 1250 | 0.1749          | 0.9608    | 0.9743 | 0.9675 | 0.9703   |
| 0.0787        | 18.75  | 1500 | 0.1482          | 0.9616    | 0.9743 | 0.9679 | 0.9730   |
| 0.0787        | 21.875 | 1750 | 0.1288          | 0.9640    | 0.9751 | 0.9695 | 0.9762   |
| 0.0433        | 25.0   | 2000 | 0.1292          | 0.9672    | 0.9776 | 0.9724 | 0.9767   |
| 0.0433        | 28.125 | 2250 | 0.1372          | 0.9623    | 0.9735 | 0.9679 | 0.9735   |
| 0.031         | 31.25  | 2500 | 0.1408          | 0.9631    | 0.9743 | 0.9687 | 0.9730   |


### Framework versions

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