ar5iv.labs.arxiv.orgauthorityabout the codebert family6,571 words
CodeBERT is a bimodal pre-trained model for programming and natural languages that uses a Transformer-based architecture. The model contains 125M parameters and is trained on data from six programming languages, including 2.1M bimodal datapoints and 6.4M unimodal codes. It employs a hybrid objective function combining masked language modeling and replaced token detection to support tasks like code search and documentation generation.written, reviewed, cited
- CodeBERT is trained without explicit markers to denote the input programming language across six languages.
- The training data filters out functions shorter than three lines and documentations shorter than three tokens.
- CodeBERT consistently outperforms RoBERTa in a zero-shot scenario where pre-trained model parameters are fixed.
github.comauthorityabout the codebert family750 words
Microsoft released the CodeBERT series, a collection of six pre-trained models for programming and natural languages. The series includes CodeBERT, GraphCodeBERT, UniXcoder, CodeReviewer, CodeExecutor, and LongCoder, all available in this repository as of June 2023. These models are multi-programming-lingual models pre-trained on NL-PL pairs in 6 programming languages including Python, Java, JavaScript, PHP, Ruby, and Go. LongCoder is specifically designed as a sparse and efficient pre-trained Transformer model for long code modeling.written, reviewed, cited
- The base CodeBERT model is explicitly stated as not suitable for mask prediction tasks.
- CodeExecutor learns to predict execution traces using a specific code execution pre-training task.
johal.in2026-05-02about the codebert family3,575 words
This benchmark evaluates four open-source code embedding models—CodeBERT-base, GraphCodeBERT-base, and two CodeT5+ variants—for RAG pipelines. CodeT5+ 220m achieves 0.89 recall@10, 22% higher than CodeBERT base, while GraphCodeBERT base reduces p99 embedding latency by 18% compared to CodeBERT base on NVIDIA T4 GPUs. This trade-off between accuracy and latency guides selection, with CodeT5+ costing $12.70 per million embeddings.written, reviewed, cited
- The benchmark script explicitly reduces batch size by half when an out-of-memory error occurs.
- CodeT5+ models utilize the pooler_output for embeddings while CodeBERT uses the last hidden state.
venturebeat.com2020-02-24about the codebert familyHN 2 pts · 0 comments
diversedaily.com2024-08-04about the codebert family
deepwiki.com2025-04-23about the codebert family