---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-binary-cordv2
  results: []
---

<!-- 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-binary-cordv2

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0062
- Precision: 0.9890
- Recall: 0.9963
- F1: 0.9926
- Accuracy: 0.9986

## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.5625  | 250  | 0.0103          | 0.9779    | 0.9852 | 0.9815 | 0.9963   |
| 0.0581        | 3.125   | 500  | 0.0132          | 0.9781    | 0.9926 | 0.9853 | 0.9973   |
| 0.0581        | 4.6875  | 750  | 0.0066          | 0.9781    | 0.9926 | 0.9853 | 0.9977   |
| 0.0097        | 6.25    | 1000 | 0.0185          | 0.9704    | 0.9704 | 0.9704 | 0.9954   |
| 0.0097        | 7.8125  | 1250 | 0.0130          | 0.9745    | 0.9889 | 0.9816 | 0.9968   |
| 0.0048        | 9.375   | 1500 | 0.0072          | 0.9890    | 0.9963 | 0.9926 | 0.9986   |
| 0.0048        | 10.9375 | 1750 | 0.0044          | 0.9781    | 0.9926 | 0.9853 | 0.9982   |
| 0.0029        | 12.5    | 2000 | 0.0092          | 0.9779    | 0.9852 | 0.9815 | 0.9973   |
| 0.0029        | 14.0625 | 2250 | 0.0080          | 0.9853    | 0.9926 | 0.9889 | 0.9982   |
| 0.0013        | 15.625  | 2500 | 0.0062          | 0.9890    | 0.9963 | 0.9926 | 0.9986   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1