license: mit
task_categories:
- text-generation
language:
- en
tags:
- code
- javascript
size_categories:
- 1M<n<10M
JavaScript-Code-Large
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.
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.
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. .
1. Dataset Composition
Programming Language: JavaScript
File Count: 5M+ JavaScript files
File Format: .jsonl
Content Types
The dataset includes a wide variety of JavaScript constructs and paradigms, such as:
Functions (declarations, expressions, arrow functions)
Classes and prototypes
Modules (CommonJS and ES Modules)
Asynchronous patterns (async/await, Promises, callbacks)
Event-driven code
Closures and higher-order functions
Functional programming constructs
DOM manipulation code
Node.js backend logic
Frontend framework components
JSDoc comments
Error handling patterns
Modern ES6+ features
2. Intended Research Applications
2.1 Pretraining
Training JavaScript code foundation models from scratch
Continued pretraining of existing LLMs
JavaScript-specialized language modeling
Tokenizer training for JS ecosystems
2.2 Fine-Tuning and Adaptation
Code completion systems
Intelligent IDE assistants
Automated refactoring tools
Conversational programming agents
JavaScript-specific copilots
2.3 Code Intelligence Tasks
Code summarization
Code-to-text generation
Documentation generation
Bug detection
Vulnerability detection
Clone detection
Code similarity modeling
Minified-to-readable code transformation
Static and structural analysis
2.4 Software Engineering Research
Empirical studies of JavaScript coding patterns
Analysis of async and event-driven architectures
Framework usage studies
Dependency modeling
AST-based experiments
Cross-version JavaScript evolution analysis
3. Relationship to Java-Code-Large
JavaScript-Code-Large complements Java-Code-Large, enabling comparative research between:
Statically typed vs dynamically typed languages
Class-based vs prototype-based paradigms
Backend vs frontend dominant ecosystems
JVM vs Node.js environments
Together, these datasets support cross-language transfer learning and controlled specialization studies.
Thanks to open source community for all the guidance & support!!