---
tags:
- generated_from_trainer
base_model: dccuchile/bert-base-spanish-wwm-uncased
metrics:
- accuracy
model-index:
- name: Prototipo_5_EMI
  results: []
---

<!-- 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. -->

# Prototipo_5_EMI

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co./dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4215
- Accuracy: 0.538

## 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: 20
- eval_batch_size: 20
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.2459        | 0.1481 | 200  | 1.2168          | 0.4493   |
| 1.1445        | 0.2963 | 400  | 1.0823          | 0.512    |
| 1.1117        | 0.4444 | 600  | 1.0979          | 0.5053   |
| 1.0618        | 0.5926 | 800  | 1.0457          | 0.5273   |
| 1.0343        | 0.7407 | 1000 | 1.0219          | 0.537    |
| 1.1239        | 0.8889 | 1200 | 1.0353          | 0.5257   |
| 0.9012        | 1.0370 | 1400 | 1.0637          | 0.5383   |
| 0.86          | 1.1852 | 1600 | 1.0682          | 0.5333   |
| 0.898         | 1.3333 | 1800 | 1.0341          | 0.5483   |
| 0.929         | 1.4815 | 2000 | 1.0437          | 0.5363   |
| 0.9921        | 1.6296 | 2200 | 0.9968          | 0.5473   |
| 0.9776        | 1.7778 | 2400 | 1.0418          | 0.5553   |
| 0.9166        | 1.9259 | 2600 | 0.9874          | 0.5573   |
| 0.703         | 2.0741 | 2800 | 1.0564          | 0.556    |
| 0.8123        | 2.2222 | 3000 | 1.0582          | 0.561    |
| 0.6727        | 2.3704 | 3200 | 1.0942          | 0.5483   |
| 0.6843        | 2.5185 | 3400 | 1.1128          | 0.558    |
| 0.7528        | 2.6667 | 3600 | 1.0823          | 0.5547   |
| 0.7747        | 2.8148 | 3800 | 1.0744          | 0.5497   |
| 0.7471        | 2.9630 | 4000 | 1.0749          | 0.5527   |
| 0.5774        | 3.1111 | 4200 | 1.1422          | 0.552    |
| 0.6105        | 3.2593 | 4400 | 1.2226          | 0.543    |
| 0.573         | 3.4074 | 4600 | 1.2427          | 0.5417   |
| 0.6047        | 3.5556 | 4800 | 1.2403          | 0.537    |
| 0.5334        | 3.7037 | 5000 | 1.2470          | 0.5413   |
| 0.5688        | 3.8519 | 5200 | 1.2585          | 0.5507   |
| 0.4928        | 4.0    | 5400 | 1.2653          | 0.5437   |
| 0.4314        | 4.1481 | 5600 | 1.3419          | 0.541    |
| 0.4556        | 4.2963 | 5800 | 1.3677          | 0.5413   |
| 0.4815        | 4.4444 | 6000 | 1.3912          | 0.5407   |
| 0.4431        | 4.5926 | 6200 | 1.4004          | 0.5347   |
| 0.4312        | 4.7407 | 6400 | 1.4161          | 0.5397   |
| 0.459         | 4.8889 | 6600 | 1.4215          | 0.538    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1