---
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
datasets:
- data_cartas_layoutv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-letter_100
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: data_cartas_layoutv3
      type: data_cartas_layoutv3
      config: default
      split: test
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.7411894273127754
    - name: Recall
      type: recall
      value: 0.8672680412371134
    - name: F1
      type: f1
      value: 0.7992874109263659
    - name: Accuracy
      type: accuracy
      value: 0.9631952889969916
---

<!-- 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-letter_100

This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cartas_layoutv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1884
- Precision: 0.7412
- Recall: 0.8673
- F1: 0.7993
- Accuracy: 0.9632

## 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        | 3.57  | 250  | 0.4503          | 0.2934    | 0.2242 | 0.2542 | 0.8928   |
| 0.5521        | 7.14  | 500  | 0.2833          | 0.4291    | 0.4639 | 0.4458 | 0.9209   |
| 0.5521        | 10.71 | 750  | 0.2116          | 0.5702    | 0.6753 | 0.6183 | 0.9437   |
| 0.173         | 14.29 | 1000 | 0.1786          | 0.6414    | 0.7835 | 0.7053 | 0.9562   |
| 0.173         | 17.86 | 1250 | 0.1772          | 0.6815    | 0.8492 | 0.7562 | 0.9581   |
| 0.077         | 21.43 | 1500 | 0.1737          | 0.7144    | 0.8737 | 0.7861 | 0.9616   |
| 0.077         | 25.0  | 1750 | 0.1768          | 0.7311    | 0.8724 | 0.7955 | 0.9615   |
| 0.0441        | 28.57 | 2000 | 0.1694          | 0.7726    | 0.8273 | 0.7990 | 0.9646   |
| 0.0441        | 32.14 | 2250 | 0.1874          | 0.7400    | 0.8621 | 0.7964 | 0.9620   |
| 0.0293        | 35.71 | 2500 | 0.1862          | 0.7321    | 0.8698 | 0.7951 | 0.9622   |
| 0.0293        | 39.29 | 2750 | 0.1887          | 0.7332    | 0.8711 | 0.7962 | 0.9620   |
| 0.0237        | 42.86 | 3000 | 0.1884          | 0.7412    | 0.8673 | 0.7993 | 0.9632   |


### Framework versions

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