SEBIS/code_trans_t5_large_source_code_summarization_python_multitask

The CodeTrans model is based on the t5-large model architecture . It has its own SentencePiece vocabulary model . It could be used to generate the description for the Python function or be fine-tuned on other Python code tasks . The model was trained on a single TPU Pod V3-8 for 80,000 steps, using sequence length 512 (batch size 4096) It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. (We have trained in total 260,000 . steps.) For code documentation tasks, different models achieves,