---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- violations
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-violations-test
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: violations
      type: violations
      config: ViolationsExtraction
      split: test
      args: ViolationsExtraction
    metrics:
    - name: Precision
      type: precision
      value: 0.9482758620689655
    - name: Recall
      type: recall
      value: 0.9116022099447514
    - name: F1
      type: f1
      value: 0.9295774647887324
    - name: Accuracy
      type: accuracy
      value: 0.9502762430939227
---

<!-- 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-violations-test

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the violations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3685
- Precision: 0.9483
- Recall: 0.9116
- F1: 0.9296
- Accuracy: 0.9503

## 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: 8
- eval_batch_size: 8
- 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        | 9.0909  | 100  | 0.2997          | 0.9543    | 0.9227 | 0.9382 | 0.9558   |
| No log        | 18.1818 | 200  | 0.3729          | 0.9425    | 0.9061 | 0.9239 | 0.9448   |
| No log        | 27.2727 | 300  | 0.3408          | 0.9543    | 0.9227 | 0.9382 | 0.9558   |
| No log        | 36.3636 | 400  | 0.3566          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
| 0.0997        | 45.4545 | 500  | 0.3685          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
| 0.0997        | 54.5455 | 600  | 0.3736          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
| 0.0997        | 63.6364 | 700  | 0.3866          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
| 0.0997        | 72.7273 | 800  | 0.3990          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
| 0.0997        | 81.8182 | 900  | 0.4018          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |
| 0.001         | 90.9091 | 1000 | 0.3979          | 0.9483    | 0.9116 | 0.9296 | 0.9503   |


### Framework versions

- Transformers 4.42.1
- Pytorch 2.3.1+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1