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README.md

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+---
+license: mit
+task_categories:
+  - text-generation
+  - text2text-generation
+language:
+  - en
+  - code
+tags:
+  - code-review
+  - code-generation
+  - software-engineering
+  - pull-requests
+  - github
+size_categories:
+  - 100K<n<1M
+---
+
+# Code Review Diffs
+
+A large-scale dataset of **(before_code, reviewer_comment, after_code)** triplets extracted from merged pull requests on top GitHub repositories.
+
+## Dataset Description
+
+Each row captures a moment where a human code reviewer left an inline comment on a pull request, and the author subsequently modified the code in response. This provides a natural signal for training models to:
+
+- **Generate code review comments** given a code diff
+- **Apply review feedback** by modifying code based on reviewer suggestions
+- **Understand code quality patterns** across languages and projects
+
+### Key Features
+
+- **500K+ triplets** from 10,000+ top GitHub repositories
+- **10+ programming languages** (Python, JavaScript, TypeScript, Java, Go, Rust, C++, C#, Ruby, PHP, and more)
+- **Quality-filtered**: bot comments, noise ("LGTM", "+1"), and auto-generated content removed
+- **Chunk-focused**: ~50 lines of context around the reviewed code, not entire files
+- **Permissive licenses only**: all source repos use MIT, Apache-2.0, BSD, or similar licenses
+- **Verified changes**: only includes triplets where the code chunk actually changed after the review
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `before_chunk` | string | ~50 lines of code around the comment, before the fix |
+| `reviewer_comment` | string | The inline review comment text |
+| `after_chunk` | string | ~50 lines of code around the comment, after the fix |
+| `diff_hunk` | string | The PR diff hunk where the comment was placed |
+| `file_path` | string | File path within the repo |
+| `comment_line` | int | Line number where the comment was placed |
+| `language` | string | Programming language |
+| `quality_score` | float | Comment quality score (0.0-1.0) |
+| `comment_length` | int | Character count of reviewer comment |
+| `before_line_count` | int | Lines in before chunk |
+| `after_line_count` | int | Lines in after chunk |
+| `pr_title` | string | Pull request title |
+| `pr_number` | int | PR number |
+| `repo_name` | string | Full repo name (owner/repo) |
+| `repo_stars` | int | GitHub stars |
+| `repo_language` | string | Primary repo language |
+| `reviewer_username` | string | Reviewer's GitHub username |
+| `author_username` | string | PR author's GitHub username |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("ronantakizawa/code-review-diffs")
+
+# Get a training example
+example = ds["train"][0]
+print(f"Review comment: {example['reviewer_comment']}")
+print(f"Language: {example['language']}")
+print(f"Before:\n{example['before_chunk'][:200]}")
+print(f"After:\n{example['after_chunk'][:200]}")
+```
+
+### Filter by language
+
+```python
+python_reviews = ds["train"].filter(lambda x: x["language"] == "Python")
+```
+
+### Filter by quality
+
+```python
+high_quality = ds["train"].filter(lambda x: x["quality_score"] >= 0.3)
+```
+
+## Collection Methodology
+
+1. **Repo selection**: Top 10,000 GitHub repos by stars with permissive licenses from [ronantakizawa/github-top-projects](https://huggingface.co/datasets/ronantakizawa/github-top-projects)
+2. **PR discovery**: Paginate merged PRs, filter bot authors, fetch inline review comments
+3. **Comment filtering**: Remove bots, noise patterns, auto-generated comments, non-English text, non-code files, reply comments
+4. **Triplet extraction**: Fetch file contents at the review commit (before) and PR head (after), extract focused chunks around the comment line
+5. **Change verification**: Only keep triplets where the code chunk around the comment actually changed
+
+### Quality Filters Applied
+
+- Bot authors excluded (dependabot, renovate, codecov, etc.)
+- Comments < 30 characters excluded
+- Noise patterns excluded (LGTM, +1, emoji-only, etc.)
+- Auto-generated comments excluded (coverage reports, CI output)
+- Non-English comments excluded (ASCII ratio < 70%)
+- Non-source-code files excluded
+- Reply comments excluded (only top-level review comments)
+- Files > 512 KB excluded
+- PRs with < 10 or > 500 changed lines excluded
+
+## Splits
+
+| Split | Percentage | Description |
+|-------|-----------|-------------|
+| train | 90% | Training data |
+| test | 5% | Test data |
+| validation | 5% | Validation data |
+
+Splits are deterministic by repository — all triplets from the same repo appear in the same split.
+
+## Limitations
+
+- Review comments may address style/formatting rather than substantive logic changes
+- The "after" code may include changes unrelated to the specific review comment
+- Line number alignment may be imprecise when multiple commits occur between review and merge
+- Some `original_commit_id` SHAs may be unavailable due to force-pushes; in these cases, the PR merge base is used as the "before" state
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@dataset{takizawa2026codereviewdiffs,
+  title={Code Review Diffs: A Large-Scale Dataset of Review-Driven Code Changes},
+  author={Takizawa, Ronan},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/ronantakizawa/code-review-diffs}
+}
+```