---
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.