Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- Python
+- code
+size_categories:
+- 1M<n<10M
+---
+
+
+**Python-Code-Large**
+
+Python-Code-Large is a large-scale corpus of Python source code comprising more than **2 million** rows of Python code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the Python ecosystem.
+
+By providing a high-volume, language-specific corpus, Python-Code-Large enables systematic experimentation in Python-focused model training, domain adaptation, and downstream code understanding tasks.
+
+Python-Code-Large addresses the need for a dedicated Python-only dataset at substantial scale, enabling focused research across data science, backend systems, automation, scientific computing, and AI-driven Python environments.
+
+
+**1. Dataset Composition**
+
+Programming Language: Python
+
+Size: 2M+ rows of Python code
+
+File Format: .jsonl
+
+Each record is stored as structured JSON Lines format for efficient streaming, large-scale training, and distributed processing.
+
+
+Content Types
+
+The dataset includes a wide variety of Python constructs and paradigms, such as:
+
+- Function definitions and decorators
+
+- Class-based and object-oriented programming
+
+- Inheritance and multiple inheritance patterns
+
+- Async programming (async / await)
+
+- Generators and iterators
+
+- Context managers
+
+- Exception handling patterns
+
+- Type hints and annotations
+
+- Functional programming constructs (map, filter, lambda)
+
+- List, dictionary, and set comprehensions
+
+- Metaprogramming patterns
+
+- Data processing pipelines
+
+- Web framework logic
+
+- REST API implementations
+
+- Machine learning scripts
+
+- Data science notebooks (converted to .py where applicable)
+
+- CLI utilities
+
+- Testing frameworks (unit tests, integration tests)
+
+- Configuration and environment management code
+
+- Docstrings and inline documentation
+
+- Modern Python 3.x features
+
+
+
+**2. Intended Research Applications**
+
+2.1 Pretraining
+
+- Training Python code foundation models from scratch
+
+- Continued pretraining of existing LLMs
+
+- Python-specialized language modeling
+
+- Tokenizer training optimized for Python syntax
+
+- AST-aware pretraining experiments
+
+
+2.2 Fine-Tuning and Adaptation
+
+- Code completion systems
+
+- Intelligent IDE assistants
+
+- Automated refactoring tools
+
+- Conversational programming agents
+
+- Python-specific copilots
+
+- Docstring generation systems
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+- Type inference assistants
+
+  
+
+2.3 Code Intelligence Tasks
+
+- Code summarization
+
+- Code-to-text generation
+
+- Documentation generation
+
+- Bug detection
+
+- Vulnerability detection
+
+- Clone detection
+
+- Code similarity modeling
+
+- Readability enhancement
+
+_ Static code analysis
+
+- Structural and dependency modeling
+
+
+
+
+2.4 Software Engineering Research
+
+- Empirical studies of Python coding patterns
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+- Analysis of async architectures in Python
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+- Framework usage studies
+
+- Dependency and import graph modeling
+
+- AST-based experiments
+
+- Cross-version Python evolution analysis
+
+- Type adoption analysis (PEP-based transitions)
+
+- Large-scale study of testing patterns
+
+
+**3. Research Opportunities Enabled**
+
+Python-Code-Large enables exploration of:
+
+- Python-specific tokenizer efficiency
+
+- Function-level representation learning
+
+- Retrieval-augmented generation for code
+
+- Secure code modeling
+
+- Long-context modeling of large Python files
+
+- Docstring-conditioned generation
+
+- Python-specific benchmark creation
+
+
+
+Thanks to open source community for all the guidance & support!!