---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- mp-02/cord
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-base-cord2
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/cord
      type: mp-02/cord
    metrics:
    - name: Precision
      type: precision
      value: 0.9466882067851373
    - name: Recall
      type: recall
      value: 0.9614438063986874
    - name: F1
      type: f1
      value: 0.9540089540089539
    - name: Accuracy
      type: accuracy
      value: 0.9611161939615737
---

<!-- 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-base-cord2

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the mp-02/cord dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1856
- Precision: 0.9467
- Recall: 0.9614
- F1: 0.9540
- Accuracy: 0.9611

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 3000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 100  | 1.2612          | 0.6788    | 0.7629 | 0.7184 | 0.7685   |
| No log        | 2.0   | 200  | 0.5621          | 0.8674    | 0.8802 | 0.8738 | 0.8916   |
| No log        | 3.0   | 300  | 0.3639          | 0.8846    | 0.9114 | 0.8978 | 0.9186   |
| No log        | 4.0   | 400  | 0.3197          | 0.9153    | 0.9393 | 0.9271 | 0.9410   |
| 0.8719        | 5.0   | 500  | 0.2304          | 0.9357    | 0.9549 | 0.9452 | 0.9543   |
| 0.8719        | 6.0   | 600  | 0.2069          | 0.9389    | 0.9573 | 0.9480 | 0.9556   |
| 0.8719        | 7.0   | 700  | 0.2081          | 0.9459    | 0.9606 | 0.9532 | 0.9593   |
| 0.8719        | 8.0   | 800  | 0.1901          | 0.9532    | 0.9688 | 0.9609 | 0.9666   |
| 0.8719        | 9.0   | 900  | 0.1559          | 0.9515    | 0.9647 | 0.9580 | 0.9671   |
| 0.136         | 10.0  | 1000 | 0.1856          | 0.9467    | 0.9614 | 0.9540 | 0.9611   |
| 0.136         | 11.0  | 1100 | 0.2020          | 0.9537    | 0.9631 | 0.9584 | 0.9629   |
| 0.136         | 12.0  | 1200 | 0.1908          | 0.9552    | 0.9631 | 0.9592 | 0.9620   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3