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A benchmark dataset and open challenge for code intelligence.

CodeXGLUE is a benchmark dataset designed to evaluate and compare models for code intelligence tasks. It encompasses 14 datasets across 10 diversified code intelligence scenarios, including code-to-code translation, code summarization, and natural language code search. The platform supports the development of models leveraging pre-trained architectures like CodeBERT and CodeGPT. It aids developers in improving productivity through tasks such as code completion, defect detection, and code repair. Microsoft Research Asia, Developer Division, and Bing jointly created CodeXGLUE. The platform provides baseline models and pipelines, enabling researchers to participate in open challenges and contribute to the advancement of code intelligence. It includes tasks covering code-code, text-code, code-text, and text-text scenarios.
CodeXGLUE is a benchmark dataset designed to evaluate and compare models for code intelligence tasks.
Explore all tools that specialize in code understanding. This domain focus ensures CodeXGLUE delivers optimized results for this specific requirement.
Explore all tools that specialize in analyze code quality. This domain focus ensures CodeXGLUE delivers optimized results for this specific requirement.
Includes pre-trained models like CodeBERT and CodeGPT, allowing for fine-tuning on specific tasks.
Covers a wide range of code intelligence tasks, including code completion, translation, and search.
Offers pre-built pipelines for training and evaluating models, streamlining the development process.
Allows researchers to compare their models against others and track progress in the field.
Introduces newly created datasets for specific tasks like cloze testing and code translation.
Access the CodeXGLUE GitHub repository.
Download the datasets relevant to your task.
Set up the baseline models (CodeBERT, CodeGPT, Encoder-Decoder).
Fine-tune the models using the provided pipelines.
Evaluate your model on the benchmark tasks.
Submit your results to the leaderboard.
All Set
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"Highly regarded for its comprehensive benchmark coverage and high-quality datasets, facilitating advancements in code intelligence research."
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