Code search is a vital activity in software engineering, focused on identifying and retrieving the correct code snippets based on a query provided in natural language. Approaches based on deep ...

CL-GNN utilizes a contrastive learning strategy, a form of SSL, to learn from a large data set of 371 458 unique unlabeled protein–ligand complexes. By employing graph neural networks and molecular ...

Code search aims to retrieve the code snippet that highly matches the given query described in natural language. Recently, many code pre-training approaches have demonstrated impressive performance on ...

For instance, in contrastive learning using NCE-Loss, the loss value for each sample is computed by contrasting it with all negative samples. In other words, we need all samples in a batch to obtain ...

More performance gains are observed by the twin contrastive learning framework compared with the standard instance-level contrastive learning. The code supports multi-gpu training.