---
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-cord
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: mp-02/cord
      type: mp-02/cord
    metrics:
    - name: Precision
      type: precision
      value: 0.970467596390484
    - name: Recall
      type: recall
      value: 0.980115990057995
    - name: F1
      type: f1
      value: 0.975267930750206
    - name: Accuracy
      type: accuracy
      value: 0.973924977127173
---

<!-- 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-cord

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: 0.1373
- Precision: 0.9705
- Recall: 0.9801
- F1: 0.9753
- Accuracy: 0.9739

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.25  | 100  | 0.5589          | 0.8209    | 0.8583 | 0.8392 | 0.8394   |
| No log        | 2.5   | 200  | 0.1936          | 0.9433    | 0.9644 | 0.9537 | 0.9579   |
| No log        | 3.75  | 300  | 0.1456          | 0.9569    | 0.9760 | 0.9664 | 0.9698   |
| No log        | 5.0   | 400  | 0.1368          | 0.9584    | 0.9743 | 0.9663 | 0.9726   |
| 0.4619        | 6.25  | 500  | 0.1448          | 0.9689    | 0.9809 | 0.9749 | 0.9744   |
| 0.4619        | 7.5   | 600  | 0.1286          | 0.9689    | 0.9818 | 0.9753 | 0.9753   |
| 0.4619        | 8.75  | 700  | 0.1311          | 0.9697    | 0.9809 | 0.9753 | 0.9748   |
| 0.4619        | 10.0  | 800  | 0.1335          | 0.9721    | 0.9809 | 0.9765 | 0.9758   |
| 0.4619        | 11.25 | 900  | 0.1355          | 0.9689    | 0.9793 | 0.9740 | 0.9753   |
| 0.0424        | 12.5  | 1000 | 0.1373          | 0.9705    | 0.9801 | 0.9753 | 0.9739   |


### Framework versions

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