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 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](http://benjaminbolte.com/blog/2016/keras-language-modeling.html).
 
+### 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
+cd keras-language-modeling/
+python insurance_qa_eval.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](https://github.com/fchollet/keras).
+
+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.
+
 ### Stuff that might be of interest
 
  - `attention_lstm.py`: Attentional LSTM, based on one of the papers referenced in the blog post and others. One application used it for [image captioning](http://arxiv.org/pdf/1502.03044.pdf). It is initialized with an attention vector which provides the attention component for the neural network.
  - `insurance_qa_eval.py`: Evaluation framework for the InsuranceQA dataset. To get this working, clone the [data repository](https://github.com/codekansas/insurance_qa_python) and set the `INSURANCE_QA` environment variable to the cloned repository. Changing `config` will adjust how the model is trained.
  - `keras-language-model.py`: The `LanguageModel` class uses the `config` settings to generate a training model and a testing model. The model can be trained by passing a question vector, a ground truth answer vector, and a bad answer vector to `fit`. Then `predict` calculates the similarity between a question and answer. Override the `build` method with whatever language model you want to get a trainable model. Examples are provided at the bottom, including the `EmbeddingModel`, `ConvolutionModel`, and `RecurrentModel`.
- - `word_embeddings.py`: A Word2Vec layer that uses the embeddings generated by Gensim's word2vec model to provide vectors in place of the Keras `Embedding` layer, which could help improve convergence, since fewer parameters need to be learned. Note that this requires generating a separate file with the word2vec weights, so it doesn't fit in very nicely with the Keras architecture.
 
 ### Additionally