Update README.md

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/)