---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- data_registros_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-registros_100
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: data_registros_layoutv3
      type: data_registros_layoutv3
      config: default
      split: test
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.9871382636655949
    - name: Recall
      type: recall
      value: 0.9935275080906149
    - name: F1
      type: f1
      value: 0.9903225806451612
    - name: Accuracy
      type: accuracy
      value: 0.9992192379762649
---

<!-- 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-registros_100

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_registros_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0110
- Precision: 0.9871
- Recall: 0.9935
- F1: 0.9903
- Accuracy: 0.9992

## 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-06
- 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: 3000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 10.87  | 250  | 0.4325          | 0.2663    | 0.2638 | 0.2650 | 0.8982   |
| 0.6304        | 21.74  | 500  | 0.2065          | 0.7715    | 0.8139 | 0.7921 | 0.9622   |
| 0.6304        | 32.61  | 750  | 0.1058          | 0.9048    | 0.9385 | 0.9214 | 0.9866   |
| 0.1413        | 43.48  | 1000 | 0.0600          | 0.9314    | 0.9660 | 0.9484 | 0.9944   |
| 0.1413        | 54.35  | 1250 | 0.0377          | 0.9451    | 0.9741 | 0.9594 | 0.9969   |
| 0.0558        | 65.22  | 1500 | 0.0277          | 0.9697    | 0.9838 | 0.9767 | 0.9981   |
| 0.0558        | 76.09  | 1750 | 0.0199          | 0.9792    | 0.9903 | 0.9847 | 0.9988   |
| 0.0307        | 86.96  | 2000 | 0.0160          | 0.9824    | 0.9919 | 0.9871 | 0.9989   |
| 0.0307        | 97.83  | 2250 | 0.0147          | 0.9823    | 0.9903 | 0.9863 | 0.9988   |
| 0.0211        | 108.7  | 2500 | 0.0122          | 0.9871    | 0.9935 | 0.9903 | 0.9992   |
| 0.0211        | 119.57 | 2750 | 0.0113          | 0.9871    | 0.9935 | 0.9903 | 0.9992   |
| 0.0174        | 130.43 | 3000 | 0.0110          | 0.9871    | 0.9935 | 0.9903 | 0.9992   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3