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--- |
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license: mit |
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task_categories: |
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- text-generation |
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language: |
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- en |
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tags: |
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- code |
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- javascript |
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size_categories: |
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- 1M<n<10M |
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--- |
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**JavaScript-Code-Large** |
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JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around **5 million** JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem. |
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By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation, and downstream code understanding tasks. |
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JavaScript-Code-Large addresses the need for a dedicated JavaScript-only dataset at substantial scale, enabling focused research across frontend, backend, and full-stack JavaScript environments. |
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**1. Dataset Composition** |
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Programming Language: JavaScript |
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File Count: 5M+ JavaScript files |
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File Format: .jsonl |
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Content Types |
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The dataset includes a wide variety of JavaScript constructs and paradigms, such as: |
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- Functions (declarations, expressions, arrow functions) |
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- Classes and prototypes |
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- Modules (CommonJS and ES Modules) |
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- Asynchronous patterns (async/await, Promises, callbacks) |
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- Event-driven code |
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- Closures and higher-order functions |
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- Functional programming constructs |
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- DOM manipulation code |
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- Node.js backend logic |
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- Frontend framework components |
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- JSDoc comments |
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- Error handling patterns |
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- Modern ES6+ features |
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**2. Intended Research Applications** |
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2.1 Pretraining |
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- Training JavaScript code foundation models from scratch |
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- Continued pretraining of existing LLMs |
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- JavaScript-specialized language modeling |
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- Tokenizer training for JS ecosystems |
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2.2 Fine-Tuning and Adaptation |
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- Code completion systems |
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- Intelligent IDE assistants |
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- Automated refactoring tools |
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- Conversational programming agents |
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- JavaScript-specific copilots |
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2.3 Code Intelligence Tasks |
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- Code summarization |
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- Code-to-text generation |
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- Documentation 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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- Minified-to-readable code transformation |
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- Static and structural analysis |
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2.4 Software Engineering Research |
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- Empirical studies of JavaScript coding patterns |
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- Analysis of async and event-driven architectures |
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- Framework usage studies |
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- Dependency modeling |
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- AST-based experiments |
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- Cross-version JavaScript evolution analysis |
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**3. Relationship to [Java-Code-Large](https://huggingface.co/datasets/ajibawa-2023/Java-Code-Large)** |
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JavaScript-Code-Large complements **Java-Code-Large**, enabling comparative research between: |
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- Statically typed vs dynamically typed languages |
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- Class-based vs prototype-based paradigms |
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- Backend vs frontend dominant ecosystems |
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- JVM vs Node.js environments |
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Together, these datasets support cross-language transfer learning and controlled specialization studies. |
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Thanks to open source community for all the guidance & support!! |
