Create README.md

README.md

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+---
+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.