From 10ef778f60dd0f6610bb8d5286507b9684c2b4de Mon Sep 17 00:00:00 2001 From: Benjamin Bolte Date: Fri, 5 Aug 2016 02:55:20 -0700 Subject: [PATCH] Update README.md --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index e384e8f..4799ae4 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,10 @@ Some code for doing language modeling with Keras, in particular for question-ans - `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 + + - The official implementation can be found [here](https://github.com/white127/insuranceQA-cnn-lstm) + ### Data - L6 from [Yahoo Webscope](http://webscope.sandbox.yahoo.com/)