| | --- |
| | 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 |
| |
|
| | - 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 |
| |
|
| | - Analysis of async architectures in Python |
| |
|
| | - 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!! |
