| # CoCoSoDa: Effective Contrastive Learning for Code Search | |
| Our approach adopts the pre-trained model as the base code/query encoder and optimizes it using multimodal contrastive learning and soft data augmentation. | |
| CoCoSoDa is comprised of the following four components: | |
| * **Pre-trained code/query encoder** captures the semantic information of a code snippet or a natural language query and maps it into a high-dimensional embedding space. | |
| as the code/query encoder. | |
| * **Momentum code/query encoder** encodes the samples (code snippets or queries) of current and previous mini-batches to enrich the negative samples. | |
| * **Soft data augmentation** is to dynamically mask or replace some tokens in a sample (code/query) to generate a similar sample as a form of data augmentation. | |
| * **Multimodal contrastive learning loss function** is used as the optimization objective and consists of inter-modal and intra-modal contrastive learning loss. They are used to minimize the distance of the representations of similar samples and maximize the distance of different samples in the embedding space. | |
| ## Usage | |
| ``` | |
| import torch | |
| from transformers import RobertaTokenizer, RobertaConfig, RobertaModel | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| tokenizer = RobertaTokenizer.from_pretrained("DeepSoftwareAnalytics/CoCoSoDa") | |
| model = RobertaModel.from_pretrained("DeepSoftwareAnalytics/CoCoSoDa") | |
| ``` | |
| ## Reference | |
| Shi, E., Wang, Y., Gu, W., Du, L., Zhang, H., Han, S., ... & Sun, H. (2022). [CoCoSoDa: Effective Contrastive Learning for Code Search](https://arxiv.org/abs/2204.03293). ICSE2023. | |