readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: Clasificacion_sentimientos
    results: []
  ---
  
  <!-- 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. -->
  
  # Clasificacion_sentimientos
  
  This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/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
  