---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- data_cedulas_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cedulas_v3
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: data_cedulas_layoutv3
      type: data_cedulas_layoutv3
      config: default
      split: test
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8991596638655462
    - name: Recall
      type: recall
      value: 0.9067796610169492
    - name: F1
      type: f1
      value: 0.9029535864978903
    - name: Accuracy
      type: accuracy
      value: 0.9816565809379728
---

<!-- 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-cedulas_v3

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cedulas_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0832
- Precision: 0.8992
- Recall: 0.9068
- F1: 0.9030
- Accuracy: 0.9817

## 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: 2500

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 3.12  | 250  | 0.7409          | 0.2850    | 0.2729 | 0.2788 | 0.8614   |
| 0.9048        | 6.25  | 500  | 0.3660          | 0.6222    | 0.6559 | 0.6386 | 0.9393   |
| 0.9048        | 9.38  | 750  | 0.2132          | 0.7492    | 0.7593 | 0.7542 | 0.9544   |
| 0.2923        | 12.5  | 1000 | 0.1467          | 0.7830    | 0.7949 | 0.7889 | 0.9661   |
| 0.2923        | 15.62 | 1250 | 0.1172          | 0.8114    | 0.8237 | 0.8175 | 0.9701   |
| 0.1445        | 18.75 | 1500 | 0.1013          | 0.8560    | 0.8763 | 0.8660 | 0.9766   |
| 0.1445        | 21.88 | 1750 | 0.0952          | 0.8811    | 0.8915 | 0.8863 | 0.9794   |
| 0.0956        | 25.0  | 2000 | 0.0876          | 0.8923    | 0.8983 | 0.8953 | 0.9807   |
| 0.0956        | 28.12 | 2250 | 0.0840          | 0.9005    | 0.9051 | 0.9028 | 0.9811   |
| 0.0766        | 31.25 | 2500 | 0.0832          | 0.8992    | 0.9068 | 0.9030 | 0.9817   |


### Framework versions

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