| --- |
| license: mit |
| datasets: |
| - yelp_review_full |
| language: |
| - en |
| metrics: |
| - accuracy |
| - f1 |
| library_name: transformers |
| --- |
| # Model Card |
|
|
| ## Sentiment Analysis of Restaurant Reviews from Yelp Dataset |
|
|
| ### Overview |
|
|
| - **Task**: Sentiment classification of restaurant reviews from the Yelp dataset. |
| - **Model**: Fine-tuned BERT (Bidirectional Encoder Representations from Transformers) for sequence classification. |
| - **Training Dataset**: Yelp dataset containing restaurant reviews. |
| - **Training Framework**: PyTorch and Transformers library. |
|
|
| ### Model Details |
|
|
| - **Pre-trained Model**: BERT-base-uncased. |
| - **Input**: Cleaned and preprocessed restaurant reviews. |
| - **Output**: Binary classification (positive or negative sentiment). |
| - **Tokenization**: BERT tokenizer with a maximum sequence length of 240 tokens. |
| - **Optimizer**: AdamW with a learning rate of 3e-5. |
| - **Learning Rate Scheduler**: Linear scheduler with no warmup steps. |
| - **Loss Function**: CrossEntropyLoss. |
| - **Batch Size**: 16. |
| - **Number of Epochs**: 2. |
|
|
| ### Data Preprocessing |
|
|
| 1. Loaded Yelp reviews dataset and business dataset. |
| 2. Merged datasets on the "business_id" column. |
| 3. Removed unnecessary columns and duplicates. |
| 4. Translated star ratings into binary sentiment labels (positive or negative). |
| 5. Upsampled the minority class (negative sentiment) to address imbalanced data. |
| 6. Cleaned text data by removing non-letters, converting to lowercase, and tokenizing. |
| |
| ### Model Training |
| |
| 1. Split the dataset into training (70%), validation (15%), and test (15%) sets. |
| 2. Tokenized, padded, and truncated input sequences. |
| 3. Created attention masks to differentiate real tokens from padding. |
| 4. Fine-tuned BERT using the specified hyperparameters. |
| 5. Tracked training and validation accuracy and loss for each epoch. |
| |
| ### Model Evaluation |
| |
| 1. Achieved high accuracy and F1 scores on both the validation and test sets. |
| 2. Generalization observed, as the accuracy on the test set was similar to the validation set. |
| 3. The model showed improvement in validation loss, indicating no overfitting. |
| |
| ### Model Deployment |
| |
| 1. Saved the trained model and tokenizer. |
| 2. Published the model and tokenizer to the Hugging Face Model Hub. |
| 3. Demonstrated how to load and use the model for making predictions. |
| |
| ### Model Performance |
| |
| - **Validation Accuracy**: ≈ 97.5% - 97.8% |
| - **Test Accuracy**: ≈ 97.8% |
| - **F1 Score**: ≈ 97.8% - 97.9% |
| |
| ### Limitations |
| |
| - Excluding stopwords may impact contextual understanding, but it was necessary to handle token length limitations. |
| - Performance may vary on reviews in languages other than English. |
| |
| ### Conclusion |
| |
| The fine-tuned BERT model demonstrates robust sentiment analysis on Yelp restaurant reviews. Its high accuracy and F1 scores indicate effectiveness in capturing sentiment from user-generated content. The model is suitable for deployment in applications requiring sentiment classification for restaurant reviews. |
