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README.md

@@ -32,9 +32,9 @@ This provides a natural signal for training models to:
 
 ### Key Features
 
-- **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)
+- **142K+ positive triplets** from 617 top GitHub repositories
+- **40K+ negative examples** (~22% of dataset) of clean code labeled "No issues found."
+- **36 programming languages** (Python, TypeScript, Go, Rust, C++, 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
 - **Permissive licenses only**: all source repos use MIT, Apache-2.0, BSD, or similar licenses
@@ -106,7 +106,7 @@ 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)
+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