---
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: 0.963084495488105
    - name: Recall
      type: recall
      value: 0.9726594863297432
    - name: F1
      type: f1
      value: 0.9678483099752679
    - name: Accuracy
      type: accuracy
      value: 0.9688929551692589
---

<!-- 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: 0.1351
- Precision: 0.9631
- Recall: 0.9727
- F1: 0.9678
- Accuracy: 0.9689

## 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: 2000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.125  | 250  | 0.5767          | 0.8636    | 0.8915 | 0.8773 | 0.8925   |
| 1.0201        | 6.25   | 500  | 0.2739          | 0.9275    | 0.9536 | 0.9404 | 0.9465   |
| 1.0201        | 9.375  | 750  | 0.1894          | 0.9462    | 0.9611 | 0.9536 | 0.9602   |
| 0.1892        | 12.5   | 1000 | 0.1522          | 0.9592    | 0.9727 | 0.9659 | 0.9689   |
| 0.1892        | 15.625 | 1250 | 0.1537          | 0.9535    | 0.9677 | 0.9605 | 0.9652   |
| 0.0813        | 18.75  | 1500 | 0.1351          | 0.9631    | 0.9727 | 0.9678 | 0.9689   |
| 0.0813        | 21.875 | 1750 | 0.1406          | 0.9607    | 0.9718 | 0.9662 | 0.9689   |
| 0.0535        | 25.0   | 2000 | 0.1396          | 0.9599    | 0.9710 | 0.9654 | 0.9675   |


### Framework versions

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