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Simultaneously modeling source code and natural language has many excitingapplications in automated software development and understanding. Pursuant toachieving such technology, we introduce PyMT5, the Python method text-to-texttransfer transformer, which is trained to translate between all pairs of Pythonmethod feature combinations: a single model that can both predict whole methodsfrom natural language documentation strings (docstrings) and summarize codeinto docstrings of any common style. We present an analysis and modeling effortof a large-scale parallel corpus of 26 million Python methods and 7.7 millionmethod-docstring pairs, demonstrating that for docstring and method generation,PyMT5 outperforms similarly-sized auto-regressive language models (GPT2) whichwere English pre-trained or randomly initialized. On the CodeSearchNet testset, our best model predicts 92.1% syntactically correct method bodies,achieved a BLEU score of 8.59 for method generation and 16.3 for docstringgeneration (summarization), and achieved a ROUGE-L F-score of 24.8 for methodgeneration and 36.7 for docstring generation.

PyMT5: multi-mode translation of natural language and Python code with transformers