README.md

---
license: agpl-3.0
task_categories:
- text-classification
- feature-extraction
- text-generation

sub_categories:
- text-classification
- code-understanding
- semantic-analysis

language:
- en
tags:
- code
- art
- biology
- synthetic
- rust
- ast
- emoji
- code-analysis
pretty_name: rust_ast_emoji
size_categories:
- 100K<n<1M
---

# Rust AST Emoji Dataset

## Dataset Description

- **Repository:** [GitHub Repository](https://github.com/meta-introspector/solfunmeme-dioxus)
- **Paper:** [If applicable]
- **Point of Contact:** [Your contact information]
- **Huggingface Hub:** [Dataset link when published]

### Dataset Summary

This dataset contains Rust codebase AST (Abstract Syntax Tree) analysis with emoji mapping for code understanding and visualization. The dataset provides a unique perspective on code structure by mapping AST node types and extracted words to emojis, enabling creative code analysis and visualization.

### Supported Tasks and Leaderboards

- **Code Understanding:** Analyze code structure through emoji patterns
- **Code Classification:** Identify code domains (Crypto, Web, i18n, etc.) through emoji signatures
- **Code Visualization:** Create emoji-based code summaries and visualizations
- **Pattern Recognition:** Discover common coding patterns through emoji frequency analysis

### Languages

The dataset contains Rust source code with English comments and identifiers.

## Dataset Structure

### Data Instances

Each instance contains:
- **file_path:** Path to the original Rust source file
- **timestamp:** Unix timestamp of analysis
- **ast:** Full AST representation in JSON format
- **summary:** Analysis summary including:
  - `top_level_nodes`: Number of top-level AST nodes
  - `total_nodes`: Total number of AST nodes
  - `type_counts`: Count of each AST node type
  - `string_literals`: Extracted string literals
  - `word_counts`: Word frequency analysis
  - `word_emoji_counts`: Emoji mapping for words
  - `emoji_counts_in_strings`: Emojis found in string literals

### Data Fields

- `file_path` (string): Path to the original Rust source file
- `timestamp` (int64): Unix timestamp of analysis
- `ast` (string): Full AST representation in JSON
- `summary` (map): Analysis summary with nested fields:
  - `top_level_nodes` (int64): Number of top-level AST nodes
  - `total_nodes` (int64): Total number of AST nodes
  - `type_counts` (map): Count of each AST node type
  - `string_literals` (sequence): Extracted string literals
  - `word_counts` (map): Word frequency analysis
  - `word_emoji_counts` (map): Emoji mapping for words
  - `emoji_counts_in_strings` (map): Emojis found in string literals

### Data Splits

- **train:** All analyzed Rust files

## Dataset Creation

### Source Data

#### Initial Data Collection and Normalization

The dataset was created by analyzing Rust source files from the solfunmeme-dioxus project, which includes:
- Core application code
- Vendor dependencies
- Generated code
- Test files

#### Who are the source language producers?

The source code was written by developers working on the solfunmeme-dioxus project, including contributions from the open-source community.

### Annotations

#### Annotation process

The annotation process involved:
1. **AST Parsing:** Using syn crate to parse Rust source files into ASTs
2. **Emoji Mapping:** Mapping AST node types and extracted words to emojis based on semantic categories
3. **Analysis:** Extracting string literals, word frequencies, and emoji patterns
4. **Chunking:** Splitting large datasets into manageable chunks (1MB each)

#### Who are the annotators?

The annotations were generated automatically using a custom Rust script that implements emoji mapping based on predefined categories.

### Personal and Sensitive Information

The dataset contains only code analysis data and does not include personal or sensitive information. All file paths are relative to the project structure.

## Additional Information

### Dataset Curators

The dataset was curated as part of the solfunmeme-dioxus project development process.

### Licensing Information

This dataset is licensed under AGPL-3.0, the same license as the source codebase.

### Citation Information

```bibtex
@dataset{rust_ast_emoji,
  title={Rust AST Emoji Dataset},
  author={solfunmeme-dioxus contributors},
  year={2024},
  url={https://github.com/meta-introspector/solfunmeme-dioxus}
}
```

### Contributions

Contributions to improve the dataset, emoji mappings, or analysis methods are welcome through the project's GitHub repository.

## Usage Examples

### Basic Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("h4/solfunmeme-dioxus-reports")

# Access a sample
sample = dataset["train"][0]
print(f"File: {sample['file_path']}")
print(f"Top-level nodes: {sample['summary']['top_level_nodes']}")
print(f"Total nodes: {sample['summary']['total_nodes']}")
```

### Emoji Analysis

```python
# Analyze emoji patterns
emoji_counts = sample['summary']['word_emoji_counts']
for emoji, count in emoji_counts.items():
    print(f"{emoji}: {count}")
```

### Code Domain Detection

The dataset enables detection of code domains through emoji patterns:
- 🌵 (Agave): Solana/blockchain code
- 🎨 (CSS): Frontend/styling code  
- 🔒 (Crypto): Security/cryptography code
- 🌐 (i18n): Internationalization code

## Technical Details

### Chunking Strategy

The dataset is split into chunks of maximum 1MB each to comply with Hugging Face and GitHub file size limits. Each chunk contains multiple code analysis examples.

### Emoji Mapping Categories

The emoji mapping covers several categories:
- **Rust Core:** Basic Rust language constructs (🦀⚙️, 🏛️🧱, etc.)
- **Web/CSS:** Frontend and styling concepts (📏, 🧭, etc.)
- **Crypto/Security:** Cryptography and security (🔒, 🔑, etc.)
- **Project-Specific:** Domain-specific terms (🌵, 🌞, etc.)
- **Internationalization:** i18n and localization (🌐, 🌍, etc.)
- **Testing/Benchmarking:** Testing and performance (⏱️, 🏋️, etc.)

### Performance Considerations

The dataset is optimized for:
- **Memory efficiency:** Compact JSON serialization
- **Accessibility:** Small chunk sizes for easy loading
- **Scalability:** Organized directory structure for large datasets