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Found 3 papers, 1 papers with code
Kotlin ML Pack: Technical Report
In this technical report, we present three novel datasets of Kotlin code: KStack, KStack-clean, and KExercises.
Judging Adam: Studying the Performance of Optimization Methods on ML4SE Tasks
In this work, we test the performance of various optimizers on deep learning models for source code and find that the choice of an optimizer can have a significant impact on the model quality, with up to two-fold score differences between some of the relatively well-performing optimizers.
Out of the BLEU: how should we assess quality of the Code Generation models?
Despite all that, minimal differences in the metric scores have been used in recent papers to claim superiority of some code generation models over the others.
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