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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
