Create README.md

README.md

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+---
+pretty_name: Repository learning training dataset
+tags:
+- code-review
+- github-data
+- contrastive-learning
+- fine-tuning
+- semantic-indexing
+- multi-modal
+- jsonl
+- faiss-index
+- tree-sitter
+license: mit
+language:
+- en
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-generation
+- text-classification
+- text-retrieval
+- feature-extraction
+source_datasets:
+- github-repositories
+annotations_creators:
+- machine-generated
+- expert-reviewed
+---
+
+# Repository Learning Training Dataset
+
+This dataset contains training data extracted from GitHub repositories for training context-aware code review models. The dataset supports three primary machine learning tasks: contrastive learning, fine-tuning, and semantic indexing.
+
+## Dataset Overview
+
+**Purpose**: Enable training of AI models that understand repository-specific code review patterns and provide contextual feedback.
+
+**Source**: GitHub repositories with rich pull request history and review comments.
+
+## Dataset Structure
+
+```
+{repository-name}/
+├── contrastive/
+│   ├── changed_files_001.json      # Files changed together (positive pairs)
+│   ├── changed_files_002.json   
+│   └── ...
+├── fine_tune/
+│   ├── pr_reviews_001.jsonl        # Instruction-following format
+│   ├── pr_reviews_002.jsonl
+│   └── ...
+├── index/
+│   ├── functions.json              # AST-extracted function metadata
+└── manifest.json                   # Processing metadata
+```
+
+## Data Components
+
+### 1. Contrastive Learning Data (`/contrastive/`)
+
+**Format**: JSON files containing file groupings for contrastive learning.
+
+**Purpose**: Learn semantic relationships between code files based on change patterns.
+
+**Structure**:
+```json
+{
+  "pr_12345": [
+    "src/components/Button.tsx",
+    "src/styles/button.css", 
+    "tests/Button.test.tsx"
+  ],
+  "pr_12346": [
+    "src/api/user.py",
+    "src/models/user.py",
+    "tests/test_user.py"
+  ]
+}
+```
+
+**Usage**: Files changed together form positive pairs; files from different PRs form negative pairs for contrastive learning.
+
+### 2. Fine-Tuning Data (`/fine_tune/`)
+
+**Format**: JSONL files with instruction-following examples.
+
+**Purpose**: Adapt language models to repository-specific review patterns and conventions.
+
+**Structure**:
+```json
+{
+  "prompt": "Code diff:\n```diff\n+def calculate_score(user_data):\n+    return sum(user_data.values())\n```\nPrevious comments:\n- alice: Consider input validation\n\nPlease write a code review comment:",
+  "completion": "Good addition! I'd suggest adding type hints and handling edge cases where user_data might be empty or contain non-numeric values."
+}
+```
+
+**Features**:
+- Chronological conversation context
+- Multi-turn review discussions
+- Repository-specific terminology and patterns
+- Code diff context with surrounding discussion
+
+### 3. Semantic Index Data (`/index/`)
+
+**Format**: JSON metadata with function definitions and embeddings.
+
+**Purpose**: Enable fast semantic search across repository functions and documentation.
+
+**Structure** (`functions.json`):
+```json
+[
+  {
+    "file": "src/utils/parser.py",
+    "name": "parse_diff_hunk", 
+    "start_line": 45,
+    "end_line": 67,
+    "code": "def parse_diff_hunk(hunk_text: str) -> DiffHunk:\n    # Function implementation...",
+  }
+]
+```
+
+**Components**:
+- **AST Extraction**: Tree-sitter parsers for different programming languages
+
+## Data Generation Pipeline
+
+### Data Statistics
+
+| Repository | PRs | Review Comments | Functions | Languages |
+|------------|-----|-----------------|-----------|-----------|
+| dotnet/xharness | 100 | 50 | 1500 | C# |
+| dotnet/runtime | N/A | N/A | N/A | C#, c, c++ |
+
+## Usage Examples
+
+If you use this dataset, please refer to https://github.com/kotlarmilos/repository-learning