Datasets:

ronantakizawa
/

github-codereview

@@ -1,159 +1,140 @@
----
-license: mit
-task_categories:
-  - text-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, including negative examples of clean code that received no reviewer comments.
-## 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. The dataset also includes **negative examples** — code from the same PRs that passed review without comments — to help models learn when code is acceptable.
-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
-- **Know when not to comment** — recognizing clean code that needs no changes
-### Key Features
-- **167K+ positive triplets** from 725 top GitHub repositories
-- **51K+ negative examples** (~23% of dataset) of clean code labeled "No issues found."
-- **37 programming languages** (Python, TypeScript, Go, Rust, C++, JavaScript, C#, Java, Kotlin, Swift, and more)
-- **Human-only reviews**: AI/bot reviewers (Copilot, linter bots, etc.) are excluded
-- **Quality-filtered**: 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 |
-|--------|------|-------------|
-| `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 |
-| `author_username` | string | PR author's GitHub username |
-| `reviewer_username` | string | Reviewer's GitHub username |
-| `before_code` | string | ~50 lines of code around the comment, before the fix |
-| `reviewer_comment` | string | The inline review comment text (or "No issues found." for negatives) |
-| `after_code` | string | ~50 lines of code around the comment, after the fix |
-| `diff_context` | string | The PR diff hunk where the comment was placed |
-| `file_path` | string | File path within the repo |
-| `comment_line` | int | Line number within the code chunk (0 for negatives) |
-| `language` | string | Programming language |
-| `quality_score` | float | Comment quality score (0.0-1.0; 1.0 for negatives) |
-| `comment_type` | string | Category: suggestion, question, nitpick, bug, refactor, style, security, performance, none |
-| `comment_length` | int | Character count of reviewer comment |
-| `before_lines` | int | Line count of before code |
-| `after_lines` | int | Line count of after code |
-| `is_negative` | bool | True if this is a negative example (no reviewer comment) |
-## Usage
-```python
-from datasets import load_dataset
-ds = load_dataset("ronantakizawa/github-codereview")
-# 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_code'][:200]}")
-print(f"After:\n{example['after_code'][: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"] >= 0.5)
-```
-### Positive examples only
-```python
-positives = ds["train"].filter(lambda x: not x["is_negative"])
-```
-### Negative examples only
-```python
-negatives = ds["train"].filter(lambda x: x["is_negative"])
-```
-## Collection Methodology
-1. **Repo selection**: Top GitHub repos by stars with permissive licenses, sourced from [ronantakizawa/github-top-projects](https://huggingface.co/datasets/ronantakizawa/github-top-projects) and curated additions
-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
-6. **Negative extraction**: For each reviewed PR, identify source code files that were changed but received no review comments; extract a ~50-line chunk as a negative example labeled "No issues found."
-### Quality Filters Applied
-- Bot and AI reviewers excluded (dependabot, Copilot, golangcibot, houndci-bot, 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 examples 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
-- Negative examples assume that uncommented code was acceptable, though reviewers may have simply not reviewed certain files
-## 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/github-codereview}
-}
-```

+---
+license: mit
+task_categories:
+  - text-generation
+language:
+  - en
+  - code
+tags:
+  - code-review
+  - code-generation
+  - software-engineering
+  - pull-requests
+  - github
+size_categories:
+  - 100K<n<1M
+---
+# Code Review Dataset
+A large-scale dataset of the best code reviews from top GitHub repositories.
+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.
+The dataset also includes **negative examples** — code from the same PRs that passed review without comments — to help models learn when code is acceptable.
+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
+- **Know when not to comment** — recognizing clean code that needs no changes
+### Key Features
+- **167K+ positive triplets** from 725 top GitHub repositories
+- **51K+ negative examples** (~23% of dataset) of clean code labeled "No issues found."
+- **37 programming languages** (Python, TypeScript, Go, Rust, C++, JavaScript, C#, Java, Kotlin, Swift, and more)
+- **Human-only reviews**: AI/bot reviewers (Copilot, linter bots, etc.) are excluded
+- **Quality-filtered**: noise 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
+## Collection Methodology
+1. **Repo selection**: Top GitHub repos by stars with permissive licenses, sourced from [ronantakizawa/github-top-projects](https://huggingface.co/datasets/ronantakizawa/github-top-projects) and curated additions
+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
+6. **Negative extraction**: For each reviewed PR, identify source code files that were changed but received no review comments; extract a ~50-line chunk as a negative example labeled "No issues found."
+## Splits
+| Split | Percentage | Description |
+|-------|-----------|-------------|
+| train | 90% | Training data |
+| test | 5% | Test data |
+| validation | 5% | Validation data |
+Splits are deterministic by repository — all examples from the same repo appear in the same split.
+## Schema
+| Column | Type | Description |
+|--------|------|-------------|
+| `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 |
+| `author_username` | string | PR author's GitHub username |
+| `reviewer_username` | string | Reviewer's GitHub username |
+| `before_code` | string | ~50 lines of code around the comment, before the fix |
+| `reviewer_comment` | string | The inline review comment text (or "No issues found." for negatives) |
+| `after_code` | string | ~50 lines of code around the comment, after the fix |
+| `diff_context` | string | The PR diff hunk where the comment was placed |
+| `file_path` | string | File path within the repo |
+| `comment_line` | int | Line number within the code chunk (0 for negatives) |
+| `language` | string | Programming language |
+| `quality_score` | float | Comment quality score (0.0-1.0; 1.0 for negatives) |
+| `comment_type` | string | Category: suggestion, question, nitpick, bug, refactor, style, security, performance, none |
+| `comment_length` | int | Character count of reviewer comment |
+| `before_lines` | int | Line count of before code |
+| `after_lines` | int | Line count of after code |
+| `is_negative` | bool | True if this is a negative example (no reviewer comment) |
+## Usage
+```python
+from datasets import load_dataset
+ds = load_dataset("ronantakizawa/github-codereview")
+# 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_code'][:200]}")
+print(f"After:\n{example['after_code'][: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"] >= 0.5)
+```
+### Positive examples only
+```python
+positives = ds["train"].filter(lambda x: not x["is_negative"])
+```
+### Negative examples only
+```python
+negatives = ds["train"].filter(lambda x: x["is_negative"])
+```
+## 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/github-codereview}
+}
+```