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Query-Classifier

A BERT-based classifier that distinguishes between Gmail and Calendar queries. For calendar queries, it can also extract a time range from the user's input using natural language expressions.

Features

  • Classifies queries as either gmail or calendar.
  • Extracts time ranges from calendar queries using natural language expressions.
  • Interactive widget for live testing of queries.
  • Automated evaluation with detailed test case reporting.
  • Confusion matrix and metrics for model performance.

System Specs

  • OS: Arch Linux

  • CPU: AMD Ryzen 7 7840HS

  • GPU: NVIDIA GeForce RTX 4060

  • RAM: 32GB

  • Python: 3.10.X

  • CUDA: 12.9

  • PyTorch: 2.5.1, (GPU-CUDA) 11.8

Python Libraries

  • torch
  • transformers
  • pandas
  • scikit-learn
  • tqdm
  • seaborn
  • matplotlib
  • dateparser
  • ipywidgets

Install all dependencies with:

pip install torch transformers pandas scikit-learn tqdm seaborn matplotlib dateparser ipywidgets

Usage

  1. Prepare Data:
    Place your labeled queries in labeled-queries.csv (see example in the repo).

  2. Run the Notebook:
    Open queryclassifier-final.ipynb or upgrade.ipynb in Jupyter or VS Code.

  3. Train the Model:
    The notebook will guide you through data loading, training, and evaluation.

  4. Test the Model:

    • Use the interactive widget to test custom queries.
    • Run the automated test cell to evaluate on test-set.csv.

File Structure

  • queryclassifier-final.ipynb — Main notebook for training, evaluation, and testing.
  • labeled-queries.csv — Labeled data for training/validation/testing.
  • test-set.csv — Diverse test cases for evaluation.
  • README.md — Project documentation.

Example Queries

  • Gmail:

    • "Show me my emails"
    • "Find emails from Alice"
    • "Did I get any mail today?"

  • Calendar:

    • "What events do I have this week?"
    • "Show calendar for June 2025"
    • "What's on my agenda?"

Notes

  • The time extraction utility is rule-based and works for common time expressions (e.g., "this week", "next month", "June 2025").
  • Ambiguous or mixed queries (e.g., "Show me my calendar and emails") are challenging and may require more advanced modeling or labeling.
  • For best results, use a GPU for training.

Note:

The interactive widget for live query testing requires the ipywidgets library and a Jupyter Notebook environment (such as JupyterLab, Jupyter Notebook, or VS Code with the Jupyter extension).

If you run the notebook in a plain terminal or an environment that does not support widgets, the interactive features will not work.

To enable widgets:

pip install ipywidgets
#JupyterLab
jupyter labextension install @jupyter-widgets/jupyterlab-manager
#VScode
code --install-extension ms-python.python
code --install-extension ms-toolsai.jupyter
code --install-extension ms-python.vscode-pylance
code --install-extension ms-toolsai.jupyter-keymap
code --install-extension ms-toolsai.jupyter-renderers

For classic Jupyter Notebook, just installing ipywidgets is usually sufficient.

References

Author

OperatorSDG (Sourojeet Dasgupta)