Added all code and created README

README.md

@@ -1,2 +1,19 @@
-# deep-semantic-code-search
+# Deep Semantic Code Search
 Deep Semantic Code Search aims to explore a joint embedding space for code and description vectors and then use it for a code search application
+
+
+In these experiments, there are 2 parts:
+
+1. The first one uses an approach suggested in [1] and we train their architecture on our own python dataset.
+2. The second approach expands on the first one through methodology suggested in [2] and we achieve reasonably good results.
+
+We can clearly observe that semantic information is captured the results:
+
+![Query Results](screenshot.png) 
+
+
+### References:
+
+[1] https://guxd.github.io/papers/deepcs.pdf
+
+[2] https://towardsdatascience.com/semantic-code-search-3cd6d244a39c

code_summarization_transfer_learning/README.md

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+## Code summarization using transfer learning
+
+
+### How to run?
+
+These notebooks should be run sequentially using the docker containers provided below.
+
+1. The first notebook fetches and creates the dataset.
+2. The second notebook vectorizes the code sequence and description sequence and trains 3 seq2seq models:
+   * Seq2Seq model from function tokens -> docstring
+   * Seq2Seq model from api seq -> docstring
+   * Seq2Seq model from method name -> docstring
+3. This notebook trains an AWD LSTM model for docstring using FastAI's implementation.
+4. This notebooks trains the final joint embedder from code to docstring vectors.
+5. In this notebook, we build a search engine that uses the trained networks to output query results.
+6. This notebook evaluates the model.
+
+In order to run these sets of notebooks (1 - 6), we would highly suggest using these docker containers:
+
+#### Docker Containers
+
+ - [hamelsmu/ml-gpu](https://hub.docker.com/r/hamelsmu/ml-gpu/): Use this container for any *gpu* bound parts.
+
+ - [hamelsmu/ml-cpu](https://hub.docker.com/r/hamelsmu/ml-cpu/): Use this container for any *cpu* bound parts.