🎬 BERT + BiLSTM Sentiment Classifier: Unlocking Text Emotions

Dive into the world of sentiment analysis with this lightweight yet powerful classifier, designed for understanding the emotional tone of text. This project combines the cutting-edge contextual understanding of BERT with the sequential modeling prowess of Bidirectional LSTMs (BiLSTM), all wrapped in an interactive interface powered by Gradio.

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✨ Features: Why This Model Shines

This sentiment classifier is built for efficiency and clarity, offering key advantages:

• Hybrid Powerhouse: Leverages BERT (specifically bert-base-uncased) as a robust contextual embedding extractor, feeding into a BiLSTM layer for capturing long-range dependencies and sequential patterns in text. This hybrid approach allows for nuanced sentiment detection.
• Custom PyTorch Implementation: Built with a custom PyTorch model rather than relying on the Hugging Face Trainer, providing full control and transparency over the training and model architecture.
• Real-time Interaction: Features a Gradio-powered web interface for instant, real-time sentiment predictions directly from your browser. Input your text and see the results immediately!
• Clean & Deployable: The codebase is clean, well-structured, and ready for easy deployment on platforms like Hugging Face Spaces or for quick demonstrations on GitHub.

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🚀 Run Locally: Get Your Sentiment Classifier Up and Running

Follow these steps to set up and run the sentiment classifier on your local machine.

1. Clone the Repository: Start by getting a copy of the project files to your local system.

   git clone [https://github.com/your-username/your-repo-name](https://github.com/your-username/your-repo-name) # Replace with your actual repo URL
   cd your-repo-name # Replace with your actual repo name

2. Create and Activate a Python Virtual Environment: It's highly recommended to use a virtual environment to manage project dependencies. This isolates your project's libraries from others on your system, preventing conflicts.

   python3 -m venv env
   source env/bin/activate  # On macOS/Linux
   # On Windows: .\env\Scripts\activate

3. Install Dependencies: Once your virtual environment is active, install all necessary Python libraries from the requirements.txt file.

   pip install -r requirements.txt

4. Run the Gradio Application: Finally, launch the interactive Gradio interface.

   python app.py

   The application will start, and you'll typically find the web interface accessible in your browser at http://127.0.0.1:7860/.

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