Coursera_Reviews_Sentiment_Analysis_DistillBERT

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NourhanAbosaeed/Coursera_Reviews_Sentiment_Analysis_DistillBERT

This model is a fine-tuned version of distilbert-base-uncased on an a dataset from Cousera courses reviews, It is publicly available on Kaggle since 2017.

After data preprocessing and model training, It achieves the following results on the evaluation set:

• Train Loss: 0.4934
• Validation Loss: 0.6018
• Train Accuracy: 0.7498
• Epoch: 2

Considering the imbalanced nature of the data, metrics such as recall, precision, and F1 score were employed for evaluation:-

The model achieves these results on the test set:

          precision    recall  f1-score   support

       0       0.37      0.59      0.46      1928
       1       0.71      0.74      0.72      1022
       2       0.91      0.79      0.85      8712

accuracy                           0.75      11662
macro avg       0.67      0.71     0.68      11662
weighted avg    0.81      0.75     0.77      11662

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

After cleaning the data, It becomes 93291 training size, 11661 for validation and 11662 for test sets.

There are 3 lables positive, negative and netural.

The data have imbalanced nature so I have used class weights during training.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 17490, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Framework versions

• Transformers 4.35.1
• TensorFlow 2.14.0
• Datasets 2.14.7
• Tokenizers 0.14.1