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  **Java-Code-Large**
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- I am releasing a large dataset containing only Java codes. You can use this data for finetuning a specific model, for pretraining, or any downstream application as well.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- This dataset has around more than **15 million** codes which can be used for various purpose by Data Scientist, LLM Developers, etc.
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  Thanks to open source community for all the guidance & support!!
 
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  **Java-Code-Large**
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+ Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than **15 million** java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
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+ By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.
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+ **1. Introduction**
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+ Large-scale code corpora have become fundamental resources for training and evaluating machine learning models for code-related tasks. While multilingual code datasets exist, there is increasing interest in language-specialized corpora to:
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+ - Improve domain-specific performance
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+ - Reduce cross-language noise
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+ - Enable controlled experimental settings
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+ - Support Java-specific tooling and research
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+ Java-Code-Large addresses this need by providing a dedicated Java-only dataset at substantial scale.
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+ **2. Dataset Composition**
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+ Programming Language: Java
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+ File Count: 15M+ Java files
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+ File Format: .jsonl
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+ Content Types:
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+ - Classes
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+ - Interfaces
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+ - Enums
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+ - Methods
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+ - Annotations
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+ - JavaDoc comments
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+ - Exception handling structures
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+ - Generics and concurrency constructs
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+ The dataset consists of source code extracted from publicly accessible open-source repositories.
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+ **3. Intended Research Applications**
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+ 3.1 Pretraining
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+ - Training code foundation models from scratch
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+ - Continued pretraining of existing LLMs
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+ - Java-specialized language modeling
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+ 3.2 Fine-Tuning and Adaptation
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+ - Code completion systems
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+ - Automated refactoring tools
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+ - IDE copilots
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+ - Java-specific conversational assistants
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+ 3.3 Code Intelligence Tasks
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+ - Code summarization
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+ - Code-to-text generation
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+ - Bug detection
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+ - Vulnerability detection
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+ - Clone detection
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+ - Code similarity modeling
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+ - Static and structural analysis
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+ 3.4 Software Engineering Research
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+ - Empirical studies of Java programming patterns
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+ - Tokenization and AST modeling experiments
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  Thanks to open source community for all the guidance & support!!