---
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
model-index:
- name: ClasificadorV2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ClasificadorV2

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1411
- Accuracy: 0.5708
- Off By One Accuracy: 0.9434
- F1: 0.5724
- Recall: 0.5708

## 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: 2e-05
- train_batch_size: 50
- eval_batch_size: 50
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Off By One Accuracy | F1     | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------------:|:------:|:------:|
| 1.2177        | 0.3333 | 500  | 1.0309          | 0.5426   | 0.93                | 0.5414 | 0.5426 |
| 1.011         | 0.6667 | 1000 | 0.9836          | 0.565    | 0.9336              | 0.5485 | 0.565  |
| 0.9833        | 1.0    | 1500 | 0.9664          | 0.5752   | 0.9448              | 0.5704 | 0.5752 |
| 0.9004        | 1.3333 | 2000 | 0.9566          | 0.5728   | 0.9476              | 0.5743 | 0.5728 |
| 0.8974        | 1.6667 | 2500 | 0.9583          | 0.5782   | 0.9472              | 0.5784 | 0.5782 |
| 0.8912        | 2.0    | 3000 | 0.9480          | 0.5816   | 0.9498              | 0.5768 | 0.5816 |
| 0.7935        | 2.3333 | 3500 | 0.9768          | 0.582    | 0.9472              | 0.5800 | 0.582  |
| 0.7898        | 2.6667 | 4000 | 0.9831          | 0.5716   | 0.9426              | 0.5715 | 0.5716 |
| 0.7801        | 3.0    | 4500 | 0.9969          | 0.5736   | 0.9514              | 0.5759 | 0.5736 |
| 0.6714        | 3.3333 | 5000 | 1.0782          | 0.5826   | 0.9392              | 0.5795 | 0.5826 |
| 0.6783        | 3.6667 | 5500 | 1.0672          | 0.5724   | 0.9456              | 0.5752 | 0.5724 |
| 0.6764        | 4.0    | 6000 | 1.0762          | 0.567    | 0.9458              | 0.5708 | 0.567  |
| 0.5986        | 4.3333 | 6500 | 1.1349          | 0.5698   | 0.9412              | 0.5684 | 0.5698 |
| 0.5887        | 4.6667 | 7000 | 1.1335          | 0.5706   | 0.9398              | 0.5716 | 0.5706 |
| 0.5798        | 5.0    | 7500 | 1.1411          | 0.5708   | 0.9434              | 0.5724 | 0.5708 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1