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---
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 |