In this project, I fine-tuned a BERT model specifically to classify sentiment in Amazon product reviews, refining its performance with targeted adjustments based on review data. Using the Hugging Face Transformers library, I trained the model to identify and categorize sentiments accurately. Once trained, I deployed it to the Hugging Face Model Hub, making it accessible for sentiment analysis tasks in other applications. This project combines data preparation, model tuning, and deployment, demonstrating practical machine-learning skills focused on real-world product review insights.
Data Preparation: Processed a substantial dataset of Amazon product reviews, ensuring data quality by filtering for verified reviews and handling missing values.
Model Fine-Tuning: Adapted a pre-trained BERT model for sentiment analysis, adjusting it to accurately classify sentiments within the context of product reviews.
Efficient Tokenization: Employed BERT's tokenizer to convert textual data into a format suitable for model training, optimizing for sequence length and computational efficiency.
Model Deployment: Successfully deployed the fine-tuned model to the Hugging Face Model Hub, facilitating easy access and integration for various applications.
Performance Evaluation: Assessed the model's accuracy and effectiveness in sentiment classification, ensuring it meets the desired performance criteria.