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Intent Classification using LSTM 🤖

Python TensorFlow NLP

Intent Classification with Neural Networks is an NLP project that uses Long Short-Term Memory (LSTM) networks to classify user queries into predefined categories.

Features 🌟

  • Utilizes GloVe embeddings for high-quality word representations.
  • Employs LSTM networks to capture long-term dependencies in text data.
  • Offers a detailed pipeline from text preprocessing to model evaluation.
  • Includes multiple model configurations to explore the impact of hyperparameters.

Setup and Installation 🛠️

  1. Clone the repository.
  2. Install the required Python libraries.
  3. Download and set up the GloVe embeddings.
  4. Prepare the dataset by running the preprocessing scripts.

Dataset 📁

The project is tested on a publicly available intent classification dataset, structured with text inputs and intent labels.

Model Training and Evaluation 🚀

  • The model training process involves multiple steps including data preprocessing, feature extraction, and training LSTM models.
  • Various configurations with different hyperparameters (like hidden dimensions) are tested to find the best performing model.
  • Evaluation metrics such as accuracy, precision, recall, and F1-score are calculated to assess the model performance.

Results and Discussion 📊

  • The project includes detailed analysis of the model performance, showcasing the effectiveness of LSTM models in handling text classification tasks.
  • Visualizations like confusion matrices are provided to give insights into model predictions.

License 📜

The project is open-sourced under the MIT License.

Acknowledgements 🙌

  • Thanks to the Stanford NLP Group for providing the GloVe embeddings.
  • The intent classification dataset contributors for providing a rich dataset for analysis.

For more details, visit the GitHub repository.