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keras-language-modeling

Some code for doing language modeling with Keras, in particular for question-answering tasks. I wrote a very long blog post that explains how a lot of this works, which can be found here.

Stuff that might be of interest

Getting Started

# Install Keras (may also need dependencies)
git clone https://github.com/fchollet/keras
cd keras
sudo python setup.py install

# Clone InsuranceQA dataset
git clone https://github.com/codekansas/insurance_qa_python
export INSURANCE_QA=$(pwd)/insurance_qa_python

# Run insurance_qa_eval.py
git clone https://github.com/codekansas/keras-language-modeling
cd keras-language-modeling/
python insurance_qa_eval.py

Alternatively, I wrote a script to get started on a Google Cloud Platform instance (Ubuntu 16.04) which can be run via

cd ~
git clone https://github.com/codekansas/keras-language-modeling
cd keras-language-modeling
source install.py

I've been working on making these models available out-of-the-box. You need to install the Git branch of Keras (and maybe make some modifications) in order to run some of these models; the Keras project can be found here.

The runnable program is insurance_qa_eval.py. This will create a models/ directory which will store a history of the model's weights as it is created. You need to set an environment variable to tell it where the INSURANCE_QA dataset is.

Finally, my setup (which I think is pretty common) is to have an SSD with my operating system, and an HDD with larger data files. So I would recommend creating a models/ symlink from the project directory to somewhere in your HDD, if you have a similar setup.

Serving to a port

I added a command line argument that uses Flask to serve to a port. Once you've installed Flask, you can run:

python insurance_qa_eval.py serve

This is useful in combination with ngrok for monitoring training progress away from your desktop.

Additionally

Data