---
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_v3
  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: 0.8168168168168168
    - name: Recall
      type: recall
      value: 0.8802588996763754
    - name: F1
      type: f1
      value: 0.8473520249221185
    - name: Accuracy
      type: accuracy
      value: 0.9726255293405929
---

<!-- 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_v3

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: 0.1730
- Precision: 0.8168
- Recall: 0.8803
- F1: 0.8474
- Accuracy: 0.9726

## 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 10.87 | 250  | 0.4505          | 0.2272    | 0.2460 | 0.2362 | 0.8929   |
| 0.641         | 21.74 | 500  | 0.2718          | 0.6121    | 0.6715 | 0.6404 | 0.9374   |
| 0.641         | 32.61 | 750  | 0.1971          | 0.7854    | 0.8528 | 0.8177 | 0.9669   |
| 0.2206        | 43.48 | 1000 | 0.1730          | 0.8168    | 0.8803 | 0.8474 | 0.9726   |


### Framework versions

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