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layoutlmv3-finetuned-registros_v2

Token Classification
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layoutlmv3-finetuned-registros_v2
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---
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_v2
  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: 0.860632183908046
    - name: Recall
      type: recall
      value: 0.9374021909233177
    - name: F1
      type: f1
      value: 0.8973782771535581
    - name: Accuracy
      type: accuracy
      value: 0.9816688664026983
---

<!-- 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_v2

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: 0.1473
- Precision: 0.8606
- Recall: 0.9374
- F1: 0.8974
- 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: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 10.87 | 250  | 0.4204          | 0.4257    | 0.4351 | 0.4303 | 0.9104   |
| 0.6077        | 21.74 | 500  | 0.2246          | 0.7957    | 0.8654 | 0.8291 | 0.9683   |
| 0.6077        | 32.61 | 750  | 0.1636          | 0.8438    | 0.9218 | 0.8811 | 0.9765   |
| 0.1638        | 43.48 | 1000 | 0.1473          | 0.8606    | 0.9374 | 0.8974 | 0.9817   |


### Framework versions

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