Upload README.md with huggingface_hub

README.md

@@ -17,19 +17,23 @@ size_categories:
 
 # Code Review Diffs
 
-A large-scale dataset of **(before_code, reviewer_comment, after_code)** triplets extracted from merged pull requests on top GitHub repositories.
+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. This provides a natural signal for training models to:
+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
 
-- **118K+ triplets** from 562 top GitHub repositories
+- **118K+ positive triplets** from 562 top GitHub repositories
+- **Negative examples** (~30% ratio) of clean code labeled "No issues found."
 - **20+ programming languages** (Python, TypeScript, Go, C++, Rust, JavaScript, C#, Java, Kotlin, Swift, 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
@@ -41,17 +45,18 @@ Each row captures a moment where a human code reviewer left an inline comment on
 | Column | Type | Description |
 |--------|------|-------------|
 | `before_code` | string | ~50 lines of code around the comment, before the fix |
-| `reviewer_comment` | string | The inline review comment text |
+| `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 where the comment was placed |
+| `comment_line` | int | Line number where the comment was placed (0 for negatives) |
 | `language` | string | Programming language |
-| `quality_score` | float | Comment quality score (0.0-1.0) |
-| `comment_type` | string | Category: suggestion, question, nitpick, bug, refactor, style, security, performance |
+| `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) |
 | `pr_title` | string | Pull request title |
 | `pr_number` | int | PR number |
 | `repo_name` | string | Full repo name (owner/repo) |
@@ -87,6 +92,18 @@ python_reviews = ds["train"].filter(lambda x: x["language"] == "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 10,000 GitHub repos by stars with permissive licenses from [ronantakizawa/github-top-projects](https://huggingface.co/datasets/ronantakizawa/github-top-projects)
@@ -94,6 +111,7 @@ high_quality = ds["train"].filter(lambda x: x["quality_score"] >= 0.5)
 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
 
@@ -115,7 +133,7 @@ high_quality = ds["train"].filter(lambda x: x["quality_score"] >= 0.5)
 | test | 5% | Test data |
 | validation | 5% | Validation data |
 
-Splits are deterministic by repository — all triplets from the same repo appear in the same split.
+Splits are deterministic by repository — all examples from the same repo appear in the same split.
 
 ## Limitations
 
@@ -123,6 +141,7 @@ Splits are deterministic by repository — all triplets from the same repo appea
 - 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