Language modeling using Transformer Networks (#129)

* Add Github language modeling problem

* Rename folders, update README with datagen and train scripts

* Fix linting

code_search/README.md

@@ -80,16 +80,37 @@ step. It uses `tensor2tensor`.
 
 * Generate `TFRecords` for training
 ```
-(venv3) $ t2t-datagen --t2t_usr_dir=summarizer/gh_function_summarizer --problem=github_function_summarizer \
+(venv3) $ t2t-datagen --t2t_usr_dir=language_task/t2t_problems --problem=github_function_summarizer \
                       --data_dir=~/data --tmp_dir=/tmp
 ```
 
 * Train transduction model using `Tranformer Networks` and a base hyper-parameters set
 ```
-(venv3) $ t2t-trainer --t2t_usr_dir=summarizer/gh_function_summarizer --problem=github_function_summarizer \
+(venv3) $ t2t-trainer --t2t_usr_dir=language_task/t2t_problems --problem=github_function_summarizer \
                       --data_dir=~/data --model=transformer --hparams_set=transformer_base --output_dir=~/train
 ```
 
+## 3. Docstrings Language Model
+
+This part trains a language model based on the docstrings in the dataset and uses `tensor2tensor`
+
+* Install dependencies
+```
+(venv3) $ pip install -r summarizer/requirements.txt
+```
+
+* Generate `TFRecords` for training
+```
+(venv3) $ t2t-datagen --t2t_usr_dir=language_task/t2t_problems --problem=github_docstring_language_model \
+                      --data_dir=~/data --tmp_dir=/tmp
+```
+
+* Train language model using `Tranformer Networks` and a custom hyper-parameters set
+```
+(venv3) $ t2t-trainer --t2t_usr_dir=language_task/t2t_problems --problem=github_docstring_language_model \
+                      --data_dir=~/data --model=transformer --hparams_set=transformer_gh_lm --output_dir=~/train
+```
+
 # Acknowledgements
 
-This project derives from [hamelsmu/code_search](https://github.com/hamelsmu/code_search).
+This project derives from [hamelsmu/code_search](https://github.com/hamelsmu/code_search).

code_search/language_task/t2t_problems/__init__.py

@@ -1 +1,2 @@
 from . import function_summarizer
+from . import docstring_lm

code_search/language_task/t2t_problems/docstring_lm.py

@@ -0,0 +1,26 @@
+import os
+from tensor2tensor.utils import registry
+from tensor2tensor.data_generators import text_problems
+from tensor2tensor.models import transformer
+
+
+@registry.register_problem
+class GithubDocstringLanguageModel(text_problems.Text2SelfProblem):
+  # pylint: disable=abstract-method
+
+  """This class defines the Language Modeling problem for Github docstrings"""
+
+  @property
+  def is_generate_per_split(self):
+    return False
+
+  def generate_samples(self, data_dir, _tmp_dir, dataset_split):  #pylint: disable=no-self-use
+    docstrings_file_path = os.path.join(data_dir, '{}.docstring'.format(dataset_split))
+
+    return text_problems.text2self_txt_iterator(docstrings_file_path)
+
+@registry.register_hparams
+def transformer_gh_lm():
+  hparams = transformer.transformer_base()
+  # TODO(sanyamkapoor): change language model embedding size
+  return hparams

code_search/language_task/t2t_problems/function_summarizer.py

@@ -1,17 +1,19 @@
 import os
-
 from tensor2tensor.utils import registry
 from tensor2tensor.data_generators import text_problems
 
+
 @registry.register_problem
 class GithubFunctionSummarizer(text_problems.Text2TextProblem):
+  # pylint: disable=abstract-method
+
   """This class defines the problem of converting Python function code to docstring"""
 
   @property
   def is_generate_per_split(self):
     return False
 
-  def generate_samples(self, data_dir, _tmp_dir, dataset_split): # pylint: disable=no-self-use
+  def generate_samples(self, data_dir, _tmp_dir, dataset_split):  #pylint: disable=no-self-use
     """This method returns the generator to return {"inputs": [text], "targets": [text]} dict"""
 
     # TODO(sanyamkapoor): Merge with validation set file "valid.{function|docstring}"