README.md

---
language:
- en
license: apache-2.0
task_categories:
- text-generation
- question-answering
pretty_name: Rust-Coder
size_categories:
- 10K<n<100K
tags:
- rust
- programming
- education
- code-generation
dataset_info:
  features:
  - name: id
    dtype: string
  - name: instruction
    dtype: string
  - name: code
    dtype: string
  - name: explanation
    dtype: string
  - name: category
    dtype: string
  - name: topic
    dtype: string
  - name: metadata
    struct:
      - name: adjective
        dtype: string
      - name: verb
        dtype: string
      - name: context
        dtype: string
      - name: length
        dtype: int64
  splits:
  - name: train
    num_examples: 10800
  - name: validation
    num_examples: 1200
---

# Rust-Coder

**Rust-Coder** is a comprehensive text dataset designed for Rust programming language learning. It contains **12,000** unique samples focusing on distinct Rust concepts, code snippets, and explanations.

## Dataset Structure

Each sample consists of:
- `id`: A unique UUID.
- `instruction`: A prompt or question about a Rust concept.
- `code`: An idiomatic Rust code snippet.
- `explanation`: A detailed explanation of the concept and code.
- `category`: The high-level Rust category (e.g., Ownership & Borrowing).
- `topic`: The specific topic within the category.
- `metadata`: Additional details like used adjectives, verbs, and context.

## Covered Topics

- Ownership & Borrowing
- Types & Data Structures
- Control Flow & Logic
- Functions & Methods
- Error Handling
- Standard Library & Collections
- Concurrency & Parallelism
- Macros & Metaprogramming
- Unsafe & FFI
- Cargo & Tooling

## Duplicate Detection

Strict duplicate detection was implemented using SHA-256 hashing of instructions and code snippets to ensure 100% uniqueness across all 12,000 samples.

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("Convence/Rust-Coder")
print(dataset['train'][0])
```

## License

Apache 2.0