Golnaz-spa / Transformer-Network

Applying Transformer Network for Top 5 simple Tag prediction

Transformer Network for Stack Overflow Tag Prediction

This project demonstrates building and training a Transformer network to predict tags for Stack Overflow posts. The Transformer model, known for its efficiency in handling sequence data, is adapted here for a multi-label classification task. The process involves data preprocessing, model construction, training, and evaluation phases, leveraging TensorFlow and Keras for model implementation.

Project Workflow

1. Data Import and Preprocessing:

   • Load and preprocess text data, including tokenization and removing punctuation.
   • Split the dataset into training, validation, and testing sets to evaluate the model's performance.

2. Vectorization:

   • Use TextVectorization layers to convert text data into numerical format, preparing it for the Transformer model.

3. Model Building:

   • Construct the Transformer model with custom encoder and decoder blocks, incorporating multi-head attention mechanisms.
   • Implement positional encoding to maintain the order of words in text sequences.

4. Training:

   • Compile and train the Transformer model on the prepared datasets, using categorical cross-entropy as the loss function and RMSprop as the optimizer.

5. Evaluation and Prediction:

   • Evaluate the model's performance on the validation dataset.
   • Implement a function to decode predictions from the model, translating numerical data back into readable tags.

6. Results Analysis:

   • Analyze the accuracy and loss of the model on the test dataset, providing insights into its performance.

Key Components

• Data Preprocessing: Techniques include tokenization, removing stopwords, and standardization to prepare text data for modeling.
• Transformer Model: Custom implementation featuring encoder and decoder blocks, multi-head attention, and positional embeddings for handling sequential text data.
• Model Training and Evaluation: Utilizing TensorFlow and Keras for efficient model training and evaluation, with metrics such as accuracy to gauge performance.
• Prediction: Decoding function to interpret the model's output, generating predicted tags for new text inputs.

Usage

To replicate or build upon this project:

• Ensure you have TensorFlow, NLTK, and Gensim installed in your environment.
• Prepare your dataset in a similar format to the provided example, with separate columns or fields for text and tags.
• Follow the preprocessing steps to prepare your data, adjusting parameters like vocab_size and sequence_length as necessary for your dataset.
• Customize the Transformer model architecture as needed, possibly experimenting with different dimensions for embeddings or numbers of attention heads.
• Train the model, potentially adjusting epochs and batch size based on your dataset size and compute resources.

Results

The project demonstrates the application of a Transformer network to a real-world text classification task, highlighting its potential for accurately predicting tags based on the content of Stack Overflow posts. The approach can be adapted and extended to other text classification or sequence modeling tasks.