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layoutlmv3-large-cord2

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layoutlmv3-large-cord2
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---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- generated_from_trainer
datasets:
- mp-02/cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-large-cord2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/cord
      type: mp-02/cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9810074318744839
    - name: Recall
      type: recall
      value: 0.9842584921292461
    - name: F1
      type: f1
      value: 0.9826302729528537
    - name: Accuracy
      type: accuracy
      value: 0.9817017383348582
---

<!-- 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-large-cord2

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on the mp-02/cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1205
- Precision: 0.9810
- Recall: 0.9843
- F1: 0.9826
- Accuracy: 0.9817

## 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: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.25  | 100  | 0.4870          | 0.8359    | 0.8691 | 0.8522 | 0.8518   |
| No log        | 2.5   | 200  | 0.1731          | 0.9505    | 0.9702 | 0.9602 | 0.9584   |
| No log        | 3.75  | 300  | 0.1432          | 0.9559    | 0.9693 | 0.9626 | 0.9684   |
| No log        | 5.0   | 400  | 0.0925          | 0.9745    | 0.9809 | 0.9777 | 0.9808   |
| 0.4385        | 6.25  | 500  | 0.1295          | 0.9695    | 0.9760 | 0.9727 | 0.9748   |
| 0.4385        | 7.5   | 600  | 0.1169          | 0.9696    | 0.9785 | 0.9740 | 0.9758   |
| 0.4385        | 8.75  | 700  | 0.1040          | 0.9769    | 0.9826 | 0.9798 | 0.9812   |
| 0.4385        | 10.0  | 800  | 0.1268          | 0.9696    | 0.9785 | 0.9740 | 0.9771   |
| 0.4385        | 11.25 | 900  | 0.1514          | 0.9687    | 0.9735 | 0.9711 | 0.9716   |
| 0.0431        | 12.5  | 1000 | 0.1230          | 0.9794    | 0.9843 | 0.9818 | 0.9812   |
| 0.0431        | 13.75 | 1100 | 0.1327          | 0.9786    | 0.9834 | 0.9810 | 0.9794   |
| 0.0431        | 15.0  | 1200 | 0.1300          | 0.9761    | 0.9809 | 0.9785 | 0.9794   |
| 0.0431        | 16.25 | 1300 | 0.1312          | 0.9802    | 0.9843 | 0.9822 | 0.9812   |
| 0.0431        | 17.5  | 1400 | 0.1358          | 0.9761    | 0.9818 | 0.9789 | 0.9799   |
| 0.0146        | 18.75 | 1500 | 0.1205          | 0.9810    | 0.9843 | 0.9826 | 0.9817   |
| 0.0146        | 20.0  | 1600 | 0.1481          | 0.9753    | 0.9826 | 0.9790 | 0.9785   |
| 0.0146        | 21.25 | 1700 | 0.1710          | 0.9728    | 0.9768 | 0.9748 | 0.9726   |
| 0.0146        | 22.5  | 1800 | 0.1969          | 0.9622    | 0.9693 | 0.9657 | 0.9680   |
| 0.0146        | 23.75 | 1900 | 0.1613          | 0.9745    | 0.9801 | 0.9773 | 0.9780   |
| 0.0084        | 25.0  | 2000 | 0.1713          | 0.9720    | 0.9793 | 0.9757 | 0.9758   |
| 0.0084        | 26.25 | 2100 | 0.1414          | 0.9761    | 0.9826 | 0.9794 | 0.9794   |
| 0.0084        | 27.5  | 2200 | 0.1510          | 0.9737    | 0.9809 | 0.9773 | 0.9780   |
| 0.0084        | 28.75 | 2300 | 0.1435          | 0.9794    | 0.9851 | 0.9822 | 0.9803   |
| 0.0084        | 30.0  | 2400 | 0.1685          | 0.9728    | 0.9793 | 0.9761 | 0.9758   |
| 0.0047        | 31.25 | 2500 | 0.1620          | 0.9728    | 0.9793 | 0.9761 | 0.9762   |
| 0.0047        | 32.5  | 2600 | 0.1549          | 0.9761    | 0.9818 | 0.9789 | 0.9780   |
| 0.0047        | 33.75 | 2700 | 0.1566          | 0.9777    | 0.9826 | 0.9802 | 0.9785   |
| 0.0047        | 35.0  | 2800 | 0.1627          | 0.9769    | 0.9826 | 0.9798 | 0.9785   |
| 0.0047        | 36.25 | 2900 | 0.1580          | 0.9777    | 0.9826 | 0.9802 | 0.9785   |
| 0.0034        | 37.5  | 3000 | 0.1592          | 0.9777    | 0.9826 | 0.9802 | 0.9785   |


### Framework versions

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