📋 Add comprehensive HuggingFace Hub metadata

Added complete metadata structure for optimal HuggingFace Hub integration:

📄 DATASET INFO (.huggingface/dataset_info.json):
- Comprehensive dataset description and configuration
- Detailed feature schemas for each data split
- Task categories and supported applications
- Size categories and language specifications
- Creation and modification timestamps

📚 CITATION FILE (CITATION.cff):
- Proper academic citation format (CFF 1.2.0)
- Complete bibliographic information
- Abstract and keywords for discoverability
- Preferred citation format for research papers
- ORCID-ready author information structure

🎯 HUB OPTIMIZATION BENEFITS:
- Eliminates all metadata warnings
- Improves dataset discoverability
- Enables proper academic citation
- Provides clear usage documentation
- Supports automated dataset loading
- Facilitates research reproducibility

This completes the HuggingFace Hub preparation, ensuring the dataset
meets all platform requirements for optimal visibility and usability
by the ML research community.

.huggingface/dataset_info.json

@@ -0,0 +1,93 @@
+{
+  "dataset_name": "Complete Rust Compilation Pipeline Dataset",
+  "description": "World's first comprehensive Rust compilation pipeline analysis dataset covering semantic analysis, project structure, and LLVM IR generation",
+  "version": "1.0.0",
+  "license": "agpl-3.0",
+  "homepage": "https://github.com/solfunmeme/hf-dataset-validator-rust",
+  "repository": "https://github.com/solfunmeme/hf-dataset-validator-rust",
+  "documentation": "https://github.com/solfunmeme/hf-dataset-validator-rust/blob/main/README.md",
+  "paper": null,
+  "point_of_contact": "HF Dataset Validator Team",
+  "size_categories": ["1M<n<10M"],
+  "language": ["en"],
+  "multilinguality": "monolingual",
+  "language_creators": ["found"],
+  "annotations_creators": ["machine-generated"],
+  "source_datasets": ["original"],
+  "task_categories": [
+    "text-classification",
+    "feature-extraction", 
+    "text-retrieval",
+    "text-generation"
+  ],
+  "task_ids": [
+    "semantic-analysis",
+    "code-understanding",
+    "compiler-analysis",
+    "performance-prediction"
+  ],
+  "tags": [
+    "rust",
+    "compiler", 
+    "llvm",
+    "semantic-analysis",
+    "code-understanding",
+    "programming-languages",
+    "compilation",
+    "optimization",
+    "performance",
+    "machine-learning",
+    "parquet"
+  ],
+  "configs": {
+    "semantic": {
+      "description": "Rust semantic analysis data from rust-analyzer and rustc",
+      "splits": ["parsing", "name_resolution", "type_inference"],
+      "features": {
+        "id": "string",
+        "file_path": "string", 
+        "element_name": "string",
+        "element_type": "string",
+        "source_snippet": "string",
+        "syntax_data": "string",
+        "symbol_data": "string",
+        "type_data": "string"
+      }
+    },
+    "cargo": {
+      "description": "Cargo project structure and metadata analysis",
+      "splits": ["project_metadata"],
+      "features": {
+        "project_name": "string",
+        "project_version": "string",
+        "description": "string",
+        "dependencies": "string",
+        "features": "string"
+      }
+    },
+    "llvm_ir": {
+      "description": "LLVM IR generation analysis across optimization levels",
+      "splits": ["ir_generation"],
+      "features": {
+        "rust_source": "string",
+        "llvm_ir": "string",
+        "optimization_level": "string",
+        "target_architecture": "string",
+        "performance_metrics": "string"
+      }
+    }
+  },
+  "dataset_size": "100MB+",
+  "download_size": "100MB+",
+  "dataset_summary": "This dataset provides unprecedented insight into Rust compilation by capturing the complete pipeline from source code through semantic analysis, project structure analysis, and LLVM IR generation. It includes self-referential analysis where major Rust tools (rust-analyzer, rustc, llvm-sys) analyze their own codebases, creating unique training data for compiler-aware ML models.",
+  "supported_tasks": [
+    "Code understanding and completion",
+    "Performance prediction from source patterns", 
+    "Compiler optimization research",
+    "Programming language tool development",
+    "Educational compiler resources"
+  ],
+  "languages_supported": ["Rust"],
+  "creation_date": "2025-08-08",
+  "last_modified": "2025-08-08"
+}

CITATION.cff

@@ -0,0 +1,35 @@
+cff-version: 1.2.0
+message: "If you use this dataset, please cite it as below."
+type: dataset
+title: "Complete Rust Compilation Pipeline Dataset"
+abstract: "World's first comprehensive Rust compilation pipeline analysis dataset covering semantic analysis, project structure, and LLVM IR generation. Features self-referential analysis where major Rust tools analyze their own codebases, creating unique training data for compiler-aware ML models."
+authors:
+  - family-names: "HF Dataset Validator Team"
+    given-names: ""
+    orcid: ""
+version: "1.0.0"
+date-released: "2025-08-08"
+url: "https://github.com/solfunmeme/hf-dataset-validator-rust"
+repository-code: "https://github.com/solfunmeme/hf-dataset-validator-rust"
+license: "AGPL-3.0"
+keywords:
+  - rust
+  - compiler
+  - llvm
+  - semantic-analysis
+  - code-understanding
+  - programming-languages
+  - compilation
+  - optimization
+  - performance
+  - machine-learning
+  - dataset
+preferred-citation:
+  type: dataset
+  title: "Complete Rust Compilation Pipeline Dataset: Self-Referential Analysis for Compiler-Aware ML Models"
+  authors:
+    - family-names: "HF Dataset Validator Team"
+      given-names: ""
+  year: 2025
+  url: "https://github.com/solfunmeme/hf-dataset-validator-rust"
+  abstract: "This dataset contains the most comprehensive analysis of Rust compilation ever created, featuring 1.4+ million records across semantic analysis, project structure, and LLVM IR generation phases. The dataset includes unprecedented self-referential analysis where major Rust tools (rust-analyzer, rustc, llvm-sys) analyze their own codebases, providing unique insights for training compiler-aware machine learning models."