| ---
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| tags:
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| - code
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| - programming
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| - dataset
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| pretty_name: "Coding Dataset"
|
| ---
|
|
|
| # Coding Dataset
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|
|
| Production-grade dataset for training AI coding agents.
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|
|
| ## Dataset Summary
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|
|
| - **Total Examples**: 6 (demo)
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| - **Languages**: Python, JavaScript, Java
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| - **Task Types**: Code Generation
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| - **License**: CC0-1.0
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|
|
| ## Dataset Structure
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|
|
| ### Data Splits
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|
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| - train: 70% of data
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| - validation: 15% of data
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| - test: 15% of data
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|
|
| ### Features
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|
|
| - `id` (string): Unique identifier
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| - `code` (string): Source code snippet
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| - `code_description` (string): Natural language description
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| - `programming_language` (string): Language (python, javascript, java, etc.)
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| - `task_type` (string): Type of task
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| - `difficulty_level` (string): Difficulty (beginner, intermediate, advanced, expert)
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| - `quality_score` (float): Quality score 0.0-1.0
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| - `is_tested` (bool): Code is tested
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| - `has_bugs` (bool): Known bugs exist
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| - `lines_of_code` (int): Number of lines
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| - `collected_at` (string): Collection timestamp
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|
|
| ## Usage
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|
|
| ```python
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| from datasets import load_dataset
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|
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| # Load dataset
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| dataset = load_dataset("romcmu863/code-dataset")
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|
|
| # Access splits
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| train = dataset['train']
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| validation = dataset['validation']
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| test = dataset['test']
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|
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| # Get first example
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| example = train[0]
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| print(example['code_description'])
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| print(example['code'])
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| ```
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|
|
| ## License
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|
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| CC0-1.0
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|
|
| ## Created
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|
|
| 2025-10-25
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|
|
