---
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_trainer
model-index:
- name: bert_adaptation_referencias_de_vinos
  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. -->

# bert_adaptation_referencias_de_vinos

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: 1.7296

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.06          | 1.0   | 750   | 2.4974          |
| 2.4463        | 2.0   | 1500  | 2.3140          |
| 2.2515        | 3.0   | 2250  | 2.1646          |
| 2.0962        | 4.0   | 3000  | 2.0073          |
| 2.0126        | 5.0   | 3750  | 2.0552          |
| 1.9271        | 6.0   | 4500  | 1.9176          |
| 1.8427        | 7.0   | 5250  | 1.8497          |
| 1.8009        | 8.0   | 6000  | 1.8579          |
| 1.7596        | 9.0   | 6750  | 1.8501          |
| 1.7049        | 10.0  | 7500  | 1.8178          |
| 1.6992        | 11.0  | 8250  | 1.7969          |
| 1.67          | 12.0  | 9000  | 1.8254          |
| 1.6272        | 13.0  | 9750  | 1.7340          |
| 1.6192        | 14.0  | 10500 | 1.7664          |
| 1.6209        | 15.0  | 11250 | 1.7281          |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3