Duplicate from ajibawa-2023/JavaScript-Code-Large

Co-authored-by: Feynman Innovations <ajibawa-2023@users.noreply.huggingface.co>

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README.md

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+---
+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](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!!

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