clasificador-muchocine

This model is a fine-tuned version of mrm8488/electricidad-base-discriminator on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0056
• Accuracy: 0.9985

Model description

This model is a fine-tuned version of a larger pretrained model, adapted to perform a text classification task by predicting the appropriate rating label (0–4) for a review.

The model processes the input text and predicts one of five possible labels based on the highest probability.

Intended uses & limitations

• Automatic classification of viewers' opinions in movie reviews.
• This model is not intended for other types of text classification tasks.

Other limitations:

• Class imbalance in the dataset could lead the model to be biased toward majority classes.

Training and evaluation data

The model was trained on a labeled dataset of Spanish movie reviews, available on: https://huggingface.co/datasets/rubrix/frases_muchocine Each review is associated with one of five rating labels.

Training procedure

The dataset was shuffled and split into training and evaluation subsets, using approximately 80% of the data for training and 20% for evaluation.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6739	1.0	654	0.1099	0.9673
0.0926	2.0	1308	0.0571	0.9849
0.0237	3.0	1962	0.0056	0.9985

Framework versions

• Transformers 4.57.3
• Pytorch 2.9.0+cu126
• Datasets 4.4.2
• Tokenizers 0.22.1