README.md

metadata

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

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

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

{
  "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.