--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: NourhanAbosaeed/Coursera_Reviews_Sentiment_Analysis_DistillBERT results: [] language: - en library_name: transformers --- # NourhanAbosaeed/Coursera_Reviews_Sentiment_Analysis_DistillBERT This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./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 | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6870 | 0.6382 | 0.7505 | 0 | | 0.5836 | 0.5976 | 0.7583 | 1 | | 0.4934 | 0.6018 | 0.7498 | 2 | ### Framework versions - Transformers 4.35.1 - TensorFlow 2.14.0 - Datasets 2.14.7 - Tokenizers 0.14.1