Finetune documentation
====================================

.. module:: finetune

Finetune is a python library designed to make finetuning pre-trained language models
for custom natural language processing tasks a breeze.

It ships with pre-trained model weights
from `"Improving Language Understanding by Generative Pre-Training" <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf>`_
and builds off the `OpenAI/finetune-language-model repository <https://github.com/openai/finetune-transformer-lm>`_.

Source code for finetune is available `on github <https://github.com/IndicoDataSolutions/finetune-language-model>`_.


Installation
============
Finetune can be installed directly from PyPI by using `pip`

.. code-block:: bash

    pip install finetune


or installed directly from source:

.. code-block:: bash

    git clone https://github.com/IndicoDataSolutions/finetune
    cd finetune
    python3 setup.py develop

You can optionally run the provided test suite to ensure installation completed successfully.

.. code-block:: bash

    nosetests


Finetune Quickstart Guide
=========================

Finetuning the base language model is as easy as calling :meth:`LanguageModelClassifier.fit`:

.. code-block:: python3

    model = LanguageModelClassifier()   # load base model
    model.fit(trainX, trainY)           # finetune base model on custom data
    predictions = model.predict(testX)  # predict on unseen examples
    model.save(path)                    # serialize the model to disk

Easily reload saved models from disk by using :meth:`LanguageModelClassifier.load`:

.. code-block:: python3

    model = LanguageModelClassifier.load(path)
    predictions = model.predict(testX)


Finetune API Reference
======================
.. autoclass:: finetune.LanguageModelClassifier
    :inherited-members: