codesage-large-v2

Model overview

CodeSage is a family of open code embedding models with an encoder architecture that supports a wide range of source code understanding tasks. It was initially introduced in the paper: For this V2 model, we enhanced semantic search performance by improving the quality of the contrastive learning data through consistency filtering. Starting from the pretrained checkpoint (trained with both Masked Language Modeling (MLM) and deobfuscation Section 3.1) from our V1 model training (Zhang et al., 2023), we applied contrastive learning with the filtered data. Unlike the V1 model, we extracted the initial set of (text, code) pairs—specifically, summaries and function/class bodies—from The Stack V2 data instead of using the V1 data. We employed simple rule-based filtering as detailed in our previous work. We then applied consistency filtering to further refine the data. While using The Stack V2 resulted in minor performance boosts on downstream tasks, the majority of the performance improvements came from the consistency filtering. This pretrained checkpoint is the same as those used by our V1 model (codesage/codesage-small, which is trained on The Stack data. The constative learning data are extracted from The Stack V2. Same as our V1 model, we supported nine languages as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。