Pretrained model on programming language javascript using the t5 small model architecture. It was first released in this repository. This model is trained on tokenized javascript code functions: it works best with tokenized javascript functions.

This CodeTrans model is based on the t5-small model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the code documentation generation task for the javascript function/method.

The model could be used to generate the description for the javascript function or be fine-tuned on other javascript code tasks. It can be used on unparsed and untokenized javascript code. However, if the javascript code is tokenized, the performance should be better.

Here is how to use this model to generate javascript function documentation using Transformers SummarizationPipeline:

Run this example in colab notebook.

The supervised training tasks datasets can be downloaded on Link

The model was trained on a single TPU Pod V3-8 for half million steps in total, 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.

This model was then fine-tuned on a single TPU Pod V2-8 for 32,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing javascript code.

For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score):

Test results :