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# Rust-Analyzer Semantic Analysis Dataset - Deployment Summary
## 🎉 Successfully Created HuggingFace Dataset!
### Dataset Statistics
- **Total Records**: 532,821 semantic analysis events
- **Source Files**: 1,307 Rust files from rust-analyzer codebase
- **Dataset Size**: 29MB (compressed Parquet format)
- **Processing Phases**: 3 major compiler phases captured
### Phase Breakdown
1. **Parsing Phase**: 440,096 records (9 Parquet files, 24MB)
- Syntax tree generation and tokenization
- Parse error handling and recovery
- Token-level analysis of every line of code
2. **Name Resolution Phase**: 43,696 records (1 Parquet file, 2.2MB)
- Symbol binding and scope analysis
- Import resolution patterns
- Function and struct definitions
3. **Type Inference Phase**: 49,029 records (1 Parquet file, 2.0MB)
- Type checking and inference decisions
- Variable type assignments
- Return type analysis
### Technical Implementation
- **Format**: Parquet files with Snappy compression
- **Git LFS**: All files under 10MB for optimal Git LFS performance
- **Schema**: Strongly typed with 20 columns per record
- **Chunking**: Large files automatically split for size limits
### Repository Structure
```
rust-analyser-hf-dataset/
├── README.md # Comprehensive documentation
├── .gitattributes # Git LFS configuration
├── .gitignore # Standard ignore patterns
├── parsing-phase/
│ ├── data-00000-of-00009.parquet # 3.1MB, 50,589 records
│ ├── data-00001-of-00009.parquet # 3.0MB, 50,589 records
│ ├── data-00002-of-00009.parquet # 2.6MB, 50,589 records
│ ├── data-00003-of-00009.parquet # 2.4MB, 50,589 records
│ ├── data-00004-of-00009.parquet # 3.1MB, 50,589 records
│ ├── data-00005-of-00009.parquet # 2.2MB, 50,589 records
│ ├── data-00006-of-00009.parquet # 2.6MB, 50,589 records
│ ├── data-00007-of-00009.parquet # 3.4MB, 50,589 records
│ └── data-00008-of-00009.parquet # 2.1MB, 35,384 records
├── name_resolution-phase/
│ └── data.parquet # 2.2MB, 43,696 records
└── type_inference-phase/
└── data.parquet # 2.0MB, 49,029 records
```
### Data Schema
Each record contains:
- **Identification**: `id`, `file_path`, `line`, `column`
- **Phase Info**: `phase`, `processing_order`
- **Element Info**: `element_type`, `element_name`, `element_signature`
- **Semantic Data**: `syntax_data`, `symbol_data`, `type_data`, `diagnostic_data`
- **Metadata**: `processing_time_ms`, `timestamp`, `rust_version`, `analyzer_version`
- **Context**: `source_snippet`, `context_before`, `context_after`
### Deployment Readiness
✅ **Git Repository**: Initialized with proper LFS configuration
✅ **File Sizes**: All files under 10MB for Git LFS compatibility
✅ **Documentation**: Comprehensive README with usage examples
✅ **Metadata**: Proper HuggingFace dataset tags and structure
✅ **License**: AGPL-3.0 consistent with rust-analyzer
✅ **Quality**: All records validated and properly formatted
### Next Steps for HuggingFace Hub Deployment
1. **Create Repository**: `https://huggingface.co/datasets/introspector/rust-analyser`
2. **Add Remote**: `git remote add origin https://huggingface.co/datasets/introspector/rust-analyser`
3. **Push with LFS**: `git push origin main`
4. **Verify Upload**: Check that all Parquet files are properly uploaded via LFS
### Unique Value Proposition
This dataset is **unprecedented** in the ML/AI space:
- **Self-referential**: rust-analyzer analyzing its own codebase
- **Multi-phase**: Captures 3 distinct compiler processing phases
- **Comprehensive**: Every line of code analyzed with rich context
- **Production-ready**: Generated by the most advanced Rust language server
- **Research-grade**: Suitable for training code understanding models
### Use Cases
- **AI Model Training**: Code completion, type inference, bug detection
- **Compiler Research**: Understanding semantic analysis patterns
- **Educational Tools**: Teaching compiler internals and language servers
- **Benchmarking**: Evaluating code analysis tools and techniques
## 🚀 Ready for Deployment!
The dataset is now ready to be pushed to the HuggingFace Hub at:
**https://huggingface.co/datasets/introspector/rust-analyser**
This represents a significant contribution to the open-source ML/AI community, providing unprecedented insight into how advanced language servers process code.
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