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 # keras-language-modeling
 
-Some code for doing language modeling with Keras, in particular for question-answering tasks. The original paper, which provided inspiriation for this project, can be found [here](http://arxiv.org/pdf/1511.04108.pdf).
+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).
 
 ### Stuff that might be of interest
 
-Most of this stuff is just a project for a class of mine, but in the process of writing it I wrote a few files that might be of interest to other people.
-
- - `attention_lstm.py`: Attentional LSTM, based on the referenced paper, and others. One application used it for [image captioning](http://arxiv.org/pdf/1502.03044.pdf).
- - `keras_attention_model.py`: Implementation of an attentional LSTM architecture for question-answer matching, which closely mimics one of the architectures described in the referenced paper. In particular, the model uses the attentional LSTM implementation in `attention_lstm.py` to generate a sentence embedding for each answer given a particular question, and fits the answer embedding to have a high cosine similarity with the question embedding.
+ - `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 change the `data_path` 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.
 
 ### Data
 
  - L6 from [Yahoo Webscope](http://webscope.sandbox.yahoo.com/)
  - [InsuranceQA data](https://github.com/shuzi/insuranceQA)
+   - [Pythonic version](https://github.com/codekansas/insurance_qa_python)
+