---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
widget:
- text: "me parece muy mal , se salía el producto por la caja y venían vacios , lo devolvere"
- text: "Correa de buena calidad, con un interior oscuro. Cumple perfectamente su función y se intercambia fácilmente. Una buena opción para cambiar el aspecto del reloj"
- text: "cumple su cometido sin nada que merezca la pena destacar"	
metrics:
- accuracy
model-index:
- name: electricidad-small-finetuned-amazon-review-classification
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: amazon_reviews_multi
      type: amazon_reviews_multi
      args: es
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5832
---

<!-- 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. -->

# electricidad-small-finetuned-amazon-review-classification

This model is a fine-tuned version of [mrm8488/electricidad-small-discriminator](https://huggingface.co/mrm8488/electricidad-small-discriminator) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9506
- Accuracy: 0.5832

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0258        | 1.0   | 6250  | 1.0209          | 0.5502   |
| 0.9668        | 2.0   | 12500 | 0.9960          | 0.565    |
| 0.953         | 3.0   | 18750 | 0.9802          | 0.5704   |
| 0.9201        | 4.0   | 25000 | 0.9831          | 0.567    |
| 0.902         | 5.0   | 31250 | 0.9814          | 0.5672   |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6