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


**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](https://huggingface.co/datasets/ajibawa-2023/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!!