| --- |
| pretty_name: SadikAI Code Multiverse |
| license: mit |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - code |
| - python |
| - code-completion |
| - code-generation |
| - code-explanation |
| - sadik-ai |
| - multiverse |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.jsonl |
| - split: validation |
| path: data/validation.jsonl |
| --- |
| |
| # SadikAI Code Multiverse |
|
|
| **305 Python code examples** extracted from the SadikAI project, a small |
| from-scratch language-model and chat-app codebase. Three task types: |
|
|
| | task | rows | what it asks | |
| |---|---:|---| |
| | `code_completion` | 211 | finish a function from a prefix that ends on a statement boundary | |
| | `code_explanation` | 47 | explain a function in plain English, using its docstring | |
| | `code_generation` | 47 | write a function from its docstring and signature | |
|
|
| The code is the author's own MIT-licensed work, not scraped from GitHub. Every |
| function was parsed with Python's `ast` module before inclusion. |
|
|
| ## Files |
|
|
| ```text |
| data/ |
| train.jsonl 278 rows |
| validation.jsonl 27 rows |
| ``` |
|
|
| The split is **by source file**, not by row. Validation files are |
| `code_helper.py`, `demo_multilingual.py`, `fetch_datasets.py`, and |
| `memory_store.py`, so functions from the same file do not appear in both |
| splits. |
|
|
| Each row has: |
|
|
| | field | meaning | |
| |---|---| |
| | `task` | `code_completion`, `code_explanation`, or `code_generation` | |
| | `language` | always `python` | |
| | `instruction` | the prompt, with code in a fenced block where needed | |
| | `response` | the target continuation, explanation, or full function | |
| | `source_file` | path inside the SadikAI project | |
| | `function_name` | function or method name | |
| | `class_name` | class name for methods, otherwise `null` | |
| | `line_start`, `line_end` | source line range in the original file | |
| | `license` | `mit` | |
| | `source` | `SadikAI project` | |
|
|
| ## Load it |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset( |
| "json", |
| data_files={ |
| "train": "data/train.jsonl", |
| "validation": "data/validation.jsonl", |
| }, |
| ) |
| print(ds) |
| print(ds["train"][0]) |
| ``` |
|
|
| ## Example |
|
|
| ```json |
| { |
| "task": "code_explanation", |
| "instruction": "Explain what this Python function does.\n\n```python\ndef add(a, b):\n return a + b\n```", |
| "response": "Return the sum of a and b.", |
| "source_file": "example.py", |
| "function_name": "add", |
| "class_name": null, |
| "license": "mit" |
| } |
| ``` |
|
|
| ## How it was built |
|
|
| 1. Parse every `.py` file in the project, skipping virtualenvs, training runs, |
| datasets, and release archives. |
| 2. Keep top-level functions and class methods; skip nested functions to avoid |
| duplicate snippets. |
| 3. Drop very short, very long, and duplicate functions. |
| 4. For completion rows, split at a real statement boundary near the middle of |
| the function, never inside a string or expression. |
| 5. For explanation and generation rows, use the function's own docstring. |
| 6. Redact project/home paths and scan every snippet for token-like secrets. |
|
|
| ## Licensing |
|
|
| The source code is MIT-licensed: |
|
|
| > MIT License, Copyright (c) 2026 Sadik (SadikAI) |
|
|
| The dataset packaging and metadata are also MIT. See `LICENSE`. The code does |
| not include the SadikAI training corpus or any private chat data. |
|
|
| ## Limitations |
|
|
| - One small codebase, so the style and vocabulary are narrow. |
| - Auto-extracted examples: some completion prefixes are unusual, and not every |
| function has tests. |
| - The validation split is small (27 rows) and file-level, not a full benchmark. |
| - Docstring-based generation targets are only as clear as the original |
| docstrings. |
| - **Do not report this as a HumanEval/MBPP-style benchmark result.** It is a |
| small, project-specific training and evaluation set. |
|
|
| ## Credits |
|
|
| Created by **Sadik Nawaz** (SadikAI) from the SadikAI project source code. |
|
|
