README.md

metadata

license: mit
task_categories:
  - text-generation
  - text2text-generation
language:
  - code
tags:
  - code
  - programming-languages
  - python
  - javascript
  - nodejs
  - java
  - c
  - cpp
  - rust
  - sorting-algorithms
  - data-structures
  - synthetic
pretty_name: Multi-Language Programming Code Dataset
size_categories:
  - 1K<n<10K

NOTICE

This was done by me, someone with a learning Disability. So please do bare with me when updating this with more working data.

Multi-Language Programming Code Dataset

A curated dataset of original, non-scraped code examples across 7 programming environments: Python, JavaScript, Node.js, Java, C, C++, and Rust.

The dataset ships in two parts that can be used separately or combined:

File	Rows	Description
code_dataset.jsonl / .csv	105	Hand-written "core concepts" set — one clean example per language per concept (Hello World, OOP, error handling, recursion, async, etc.)
code_dataset_large.jsonl / .csv	2,255	Template-generated, parameter-varied set covering sorting algorithms, data structures, and string manipulation in depth

Note on Node.js: Node.js is a JavaScript runtime, not a separate language. It's included as its own split because it exposes different APIs (filesystem, Buffer, process, http, CommonJS modules) than browser-context JavaScript — which is usually what people actually mean by "Node.js code."

Dataset Structure

Both files share the same schema:

Column	Description
id	Unique row identifier (per file)
language	One of: Python, JavaScript, Node.js, Java, C, C++, Rust
category	Concept/topic covered
difficulty	beginner, intermediate, or advanced
task_description	Natural-language description of the coding task
code	The code snippet solving the task
explanation	A short note on the key language feature/idiom used

code_dataset (105 rows) — Core Concepts

15 categories × 7 languages, one example each: Hello World · Variables and Data Types · Control Flow · Loops · Functions · Arrays and Lists · Dictionaries and Maps · Classes and OOP · Error Handling · File I/O · String Manipulation · Recursion · Sorting Algorithm · Async and Concurrency · Data Structures

code_dataset_large (2,255 rows) — Deep Coverage on 3 Categories

Generated by varying real parameters — algorithm choice, data type, sample values, operation sequences, and identifier names — not by duplicating templates with find-and-replace. Breakdown:

Category	Rows	What varies
Sorting Algorithm	756	Algorithm (bubble/selection/insertion), data type (int/float), array size (5–20 elements), 3 random samples per config
Data Structures	448	Stack vs. Queue, data type (int/float), 4 distinct push/pop operation sequences, random values
String Manipulation	1,051	Operation (palindrome check, reverse, word count, vowel count), 20 distinct test strings, varied function names

Distribution is balanced across languages (320–326 rows each) and skews intermediate (1,727) over beginner (528), reflecting the algorithmic focus of this batch. Exact-duplicate rows were checked and removed (3% collision rate from small-integer arrays landing on the same random sample).

Files

• code_dataset.jsonl / code_dataset_large.jsonl — one JSON object per line (recommended for datasets.load_dataset("json", ...))
• code_dataset.csv / code_dataset_large.csv — same data, spreadsheet-friendly
• generate_dataset.py — generator for the 105-row core set (add more languages/categories by adding add(...) calls)
• generate_batch.py — generator for the 2,255-row deep-coverage set (add more categories/algorithms by extending the template dicts)

Provenance & License

All code was written from scratch (hand-authored for the core set; programmatically templated with varied real parameters for the large set) — nothing was scraped from GitHub or any other source, so there are no third-party license conflicts. Released under MIT — free to use, modify, and redistribute, including for model training.

Known Limitations

• The 2,255-row set currently covers only 3 categories in depth (sorting, data structures, strings). Categories like "Hello World" or "Variables" don't have enough genuine variation to scale the same way — padding them would mean shallow repetition rather than useful diversity.
• For large-scale pretraining, pair this with an established corpus like The Stack or CodeSearchNet.
• Snippets favor clarity/idiom over production hardening (minimal input validation) — they teach the pattern, not production-ready code.

Example Rows

Core set:

{
  "id": 1,
  "language": "Python",
  "category": "Hello World",
  "difficulty": "beginner",
  "task_description": "Print 'Hello, World!' to the console.",
  "code": "print(\"Hello, World!\")",
  "explanation": "Python's print() function writes text to standard output."
}

Large set:

{
  "id": 11,
  "language": "Python",
  "category": "Sorting Algorithm",
  "difficulty": "intermediate",
  "task_description": "Sort a 12-element array of ints in ascending order using bubble sort.",
  "code": "def bubble_sort(entries):\n    n = len(entries)\n    ...",
  "explanation": "Bubble sort on int data, variable named 'entries', 12 elements."
}

Loading

Hugging Face datasets:

from datasets import load_dataset
core = load_dataset("json", data_files="code_dataset.jsonl")
large = load_dataset("json", data_files="code_dataset_large.jsonl")

Pandas / Kaggle:

import pandas as pd
core = pd.read_csv("code_dataset.csv")
large = pd.read_csv("code_dataset_large.csv")
combined = pd.concat([core, large], ignore_index=True)

Suggested Uses

• Fine-tuning a code-explanation or code-generation model
• Few-shot prompting examples for a coding assistant
• Cross-language idiom comparison (e.g., "how does error handling differ between Python and Rust?")
• Algorithm-variant training data (many sorting/data-structure/string examples with controlled, labeled variation)
• A regression-test seed set for code-generation model evals

Roadmap

The large set can be extended the same way to more categories (recursion, OOP, error handling, file I/O, async) by adding template functions to generate_batch.py — happy to keep scaling this up on request.