| | --- |
| | license: mit |
| | task_categories: |
| | - text-generation |
| | language: |
| | - en |
| | tags: |
| | - code |
| | - javascript |
| | --- |
| | |
| |
|
| | **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!! |
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