.. ml_toolkit documentation master file, created by sphinx-quickstart on Wed Apr 30 16:46:23 2025. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Welcome to ml_toolkit's documentation! ====================================== The ``ml_toolkit`` contains many functions to streamline machine learning usecases across different domains. The main modules are: - **llm**: Contains a series of functions to use LLMs within Databricks notebooks. - **text_classification**: Contains functions to perform supervised training for text classification tasks. - **text_augmentation**: Contains functions that use LLMs to perform text augmentation, used to enhance supervised training scenarios. .. attention:: The ``text_classification`` and ``text_augmentation`` modules are bespoke-specific and only available within the corporate and rnd workspaces. Getting Started ================== Installing from Databricks -------------------------- * In Databricks notebooks, the ``ml_toolkit`` operates the same way as a repo-based library * Add this to the top of your notebook as a cell .. code-block:: bash :caption: Install library dependencies %run /setup_ml_toolkit * Then, in subsequent cells: .. code-block:: python :caption: Use the toolkit import ml_toolkit .. toctree:: :maxdepth: 2 :caption: Contents: llm Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` Contact Us! =========== This package is maintained by Platform Engineering. If you have any issues, contact us via our `Jira Form `_.