---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-large-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-large-cord

This model is a fine-tuned version of [microsoft/layoutlmv3-large](https://huggingface.co/microsoft/layoutlmv3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1616
- Precision: 0.9526
- Recall: 0.9482
- F1: 0.9504
- Accuracy: 0.9677

## 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: 2
- eval_batch_size: 2
- 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        | 0.25  | 100  | 0.5321          | 0.7584    | 0.7859 | 0.7719 | 0.8224   |
| No log        | 0.5   | 200  | 0.4949          | 0.8091    | 0.8354 | 0.8221 | 0.8683   |
| No log        | 0.75  | 300  | 0.3478          | 0.8668    | 0.8648 | 0.8658 | 0.8916   |
| No log        | 1.0   | 400  | 0.5194          | 0.75      | 0.7117 | 0.7304 | 0.8513   |
| 0.6065        | 1.25  | 500  | 0.3052          | 0.9059    | 0.9003 | 0.9031 | 0.9341   |
| 0.6065        | 1.5   | 600  | 0.2427          | 0.9245    | 0.9173 | 0.9209 | 0.9443   |
| 0.6065        | 1.75  | 700  | 0.2372          | 0.9174    | 0.9181 | 0.9177 | 0.9477   |
| 0.6065        | 2.0   | 800  | 0.2044          | 0.9247    | 0.9212 | 0.9230 | 0.9494   |
| 0.6065        | 2.25  | 900  | 0.1847          | 0.9442    | 0.9413 | 0.9427 | 0.9613   |
| 0.1862        | 2.5   | 1000 | 0.1616          | 0.9526    | 0.9482 | 0.9504 | 0.9677   |


### Framework versions

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