---
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: 0.963984674329502
    - name: Recall
      type: recall
      value: 0.9767080745341615
    - name: F1
      type: f1
      value: 0.9703046664095644
    - name: Accuracy
      type: accuracy
      value: 0.9702886247877759
---

<!-- 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: 0.2044
- Precision: 0.9640
- Recall: 0.9767
- F1: 0.9703
- Accuracy: 0.9703

## 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: 5e-05
- train_batch_size: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.7241  | 100  | 0.4595          | 0.8310    | 0.8859 | 0.8576 | 0.8786   |
| No log        | 3.4483  | 200  | 0.2637          | 0.9284    | 0.9565 | 0.9423 | 0.9469   |
| No log        | 5.1724  | 300  | 0.2096          | 0.9513    | 0.9697 | 0.9604 | 0.9626   |
| No log        | 6.8966  | 400  | 0.2016          | 0.9512    | 0.9689 | 0.96   | 0.9622   |
| 0.3892        | 8.6207  | 500  | 0.2418          | 0.9453    | 0.9658 | 0.9555 | 0.9593   |
| 0.3892        | 10.3448 | 600  | 0.2149          | 0.9579    | 0.9713 | 0.9645 | 0.9660   |
| 0.3892        | 12.0690 | 700  | 0.2090          | 0.9608    | 0.9713 | 0.9660 | 0.9652   |
| 0.3892        | 13.7931 | 800  | 0.2202          | 0.9580    | 0.9728 | 0.9653 | 0.9673   |
| 0.3892        | 15.5172 | 900  | 0.2217          | 0.9595    | 0.9744 | 0.9669 | 0.9682   |
| 0.0278        | 17.2414 | 1000 | 0.2044          | 0.9640    | 0.9767 | 0.9703 | 0.9703   |


### Framework versions

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