Datasets:
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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
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