| # 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. | |