---
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-cord
  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-cord

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: 0.1589
- Precision: 0.9433
- Recall: 0.9521
- F1: 0.9477
- Accuracy: 0.9669

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.5   | 100  | 0.6487          | 0.7825    | 0.8006 | 0.7914 | 0.8330   |
| No log        | 1.0   | 200  | 0.4266          | 0.8496    | 0.8686 | 0.8590 | 0.8925   |
| No log        | 1.5   | 300  | 0.2553          | 0.9008    | 0.9057 | 0.9033 | 0.9341   |
| No log        | 2.0   | 400  | 0.2496          | 0.8960    | 0.9057 | 0.9008 | 0.9295   |
| 0.5667        | 2.5   | 500  | 0.2016          | 0.9274    | 0.9374 | 0.9324 | 0.9554   |
| 0.5667        | 3.0   | 600  | 0.1806          | 0.9387    | 0.9467 | 0.9427 | 0.9609   |
| 0.5667        | 3.5   | 700  | 0.1667          | 0.9424    | 0.9474 | 0.9449 | 0.9630   |
| 0.5667        | 4.0   | 800  | 0.1735          | 0.9452    | 0.9467 | 0.9459 | 0.9639   |
| 0.5667        | 4.5   | 900  | 0.1657          | 0.9456    | 0.9529 | 0.9492 | 0.9660   |
| 0.1025        | 5.0   | 1000 | 0.1589          | 0.9433    | 0.9521 | 0.9477 | 0.9669   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.0
- Datasets 2.16.1
- Tokenizers 0.15.0