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IMDb Sentiment Analysis with Transformer

Overview

This repository contains code for sentiment analysis on the IMDb dataset using a Transformer model implemented with TensorFlow and Keras.

Components

  1. Data Loading and Preprocessing: IMDb dataset is loaded and preprocessed. Sequences are padded to a maximum length.

  2. Tokenization: Text data is tokenized using the Tokenizer provided by Keras.

  3. Transformer Model: A Transformer model is constructed with multiple layers, each containing multi-head self-attention and feed-forward neural network blocks.

  4. Training: The model is compiled and trained on the IMDb dataset.

  5. Text Analysis Widget: A widget is provided for users to input text for sentiment analysis. The model predicts whether the input text has a positive or negative sentiment.

Dependencies

  • TensorFlow 2.x
  • NumPy
  • ipywidgets (for the interactive text analysis widget)

Usage

  1. Clone the Repository:

    git clone <repository-url>

  2. Install Dependencies::

    pip install -r requirements.txt

Run the Code:

Execute the script containing the code. Ensure you have access to a GPU if training the model.

Use the Text Analysis Widget:

After running the code, an interactive widget will be displayed where you can input text for sentiment analysis. Click the "Analyze" button to get the sentiment analysis result.

Code Structure

  • transformer_model: Defines the Transformer model architecture.
  • train: Script for training the model on the IMDb dataset.
  • text_analysis_widget: Contains the code for the interactive text analysis widget.

Acknowledgments

  • The Transformer model architecture is inspired by the original paper "Attention is All You Need" by Vaswani et al.
  • IMDb dataset is obtained from Keras datasets.