PyMT5: multi-mode translation of natural language and Python code with transformers
Colin B. Clement, Dawn Drain, Jonathan Timcheck, A. Svyatkovskiy, Neel Sundaresan · 2020
Simultaneously modeling source code and natural language has many exciting applications in automated software development and understanding.Pursuant to achieving such technology, we introduce PYMT5, the PYTHON method text-to-text transfer transformer, which is trained to translate between all pairs of PYTHON method feature combinations: a single model that can both predict whole methods from natural language documentation strings (docstrings) and summarize code into docstrings of any common style.We present an analysis and modeling effort of a large-scale parallel corpus of 26 million PYTHON methods and 7.7 million method-docstring pairs, demonstrating that for docstring and method generation, PYMT5 outperforms similarlysized auto-regressive language models (GPT2) which were English pre-trained or randomly initialized.On the CODE-SEARCHNET test set, our best model predicts 92.1% syntactically correct method bodies, achieved a BLEU score of 8.59 for method generation and 16.3 for docstring * Corresponding author † Work done during a Microsoft internship generation (summarization), and achieved a ROUGE-L F-score of 24.8 for method generation and 36.7 for docstring generation.