Clasificacion_sentimientos

This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3399
• Accuracy: 0.9428

Model description

Se entrena un modelo que es capaz de clasificar si es un comentario postivo o negativo.

Intended uses & limitations

More information needed

Training and evaluation data

Se entrenó el modelo usando comentarios de peliculas de la página $https://www.filmaffinity.com/es/main.html$

• Estos comentarios estan en la base de datos alojada en Kaggle, url : https://www.kaggle.com/ricardomoya/criticas-peliculas-filmaffinity-en-espaniol/code

Training procedure

La variable review_rate se usó para clasificar los comentarios positivos y negativos así: Positivos: los rating con 8,9,10. Negativos: Los rating con 3,2,1.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2566	1.0	901	0.5299	0.8935
0.0963	2.0	1802	0.2885	0.9383
0.0133	3.0	2703	0.3546	0.9406
0.0002	4.0	3604	0.3399	0.9428

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6