Update README.md

README.md

@@ -1,90 +1,77 @@
 ---
-dataset_info:
-  features:
-    - name: html_url
-      dtype: string
-    - name: labels
-      dtype: list
-    - name: text
-      dtype: string
-    - name: issue_number
-      dtype: int64
-  splits:
-    - name: train
-      num_examples: 12484
-  task_categories:
-    - text-classification
-  task_ids:
-    - multi-label-classification
-  languages:
-    - en
+language:
+- en
+task_categories:
+- text-classification
+task_ids:
+- multi-label-classification
+pretty_name: Scikit-learn GitHub Issues (Multilabel)
+license: apache-2.0
 ---
 
 # 🧩 Scikit-learn GitHub Issues – Multilabel Dataset
 
-This dataset contains **GitHub issues from the `scikit-learn` repository**, prepared specifically for **multilabel NLP tasks** such as issue tagging, automated triage, and semantic search.
+This dataset contains **GitHub issues from the `scikit-learn` repository**, prepared for **multilabel NLP tasks** such as issue tagging, automated triage, and semantic search.
 
-Each sample represents **one issue + one comment context**, making it suitable for fine-grained text understanding and real-world ML workflows.
+Each row corresponds to **one issue-comment context**, making the dataset suitable for real-world developer tooling.
 
 ---
 
 ## 📌 Motivation
 
-Open-source projects rely heavily on labeled GitHub issues:
-- bug tracking
-- feature requests
-- documentation updates
-- module-specific discussions
+GitHub issues are a critical signal in open-source projects:
+- Bug tracking
+- Feature requests
+- Documentation improvements
+- Module-specific discussions
 
-This dataset aims to support:
-- **multilabel classification**
-- **label recommendation systems**
-- **semantic search over issues**
-- downstream LLM and RAG pipelines
+This dataset enables:
+- Multilabel text classification
+- Label recommendation systems
+- Semantic search over issues
+- Downstream LLM & RAG pipelines
 
 ---
 
 ## 📦 Dataset Construction
 
-### Source
+**Source**
 - Repository: `scikit-learn/scikit-learn`
-- Data collected using the **GitHub REST API**
+- Collected using the GitHub REST API
 
-### Included
-✅ Open and closed **issues**  
-✅ Full issue **title + body**  
-✅ All **issue comments**  
-✅ Original **GitHub labels**
+**Included**
+- Open & closed **issues**
+- Issue **title + body**
+- All **comments**
+- Original **GitHub labels**
 
-### Excluded
-❌ Pull requests  
-❌ System / bot content (where possible)
+**Excluded**
+- Pull requests
 
 ---
 
-## 🔄 Preprocessing Steps
+## 🔄 Preprocessing Pipeline
 
-1. **Fetched issues** up to API safety limits
-2. **Filtered out pull requests**
-3. Retrieved **all comments** for each issue
-4. **Exploded comments**, creating multiple rows per issue
-5. Combined:
-   - issue title
-   - issue body
+1. Retrieved issues up to API safety limits  
+2. Removed pull requests  
+3. Downloaded all issue comments  
+4. Exploded issues by comments  
+5. Constructed a unified `text` field:
+   - title  
+   - body  
    - comments  
-   into a single `text` field
-6. Extracted label names into a clean multilabel format
+6. Extracted label names into a multilabel format
 
 ---
 
-## 📊 Dataset Statistics
+## 📊 Dataset Overview
 
-- **Total samples**: ~12,500
-- **Unique labels**: ~20+
+- **Samples**: ~12,500
 - **Labels per sample**: 1–6
+- **Unique labels**: ~20+
 - **Language**: English
 
-### Example labels
+Example labels:
 - `Bug`
 - `Documentation`
 - `New Feature`
@@ -94,50 +81,37 @@ This dataset aims to support:
 
 ---
 
-## 🧱 Dataset Schema
+## 🧱 Dataset Columns
 
-| Column        | Type   | Description |
-|--------------|--------|-------------|
-| `html_url`   | string | URL of the GitHub issue |
-| `labels`     | list   | List of issue labels (multilabel target) |
-| `text`       | string | Issue title + body + comments |
-| `issue_number` | int | Original GitHub issue number |
+| Column | Description |
+|------|------------|
+| `html_url` | GitHub issue URL |
+| `labels` | List of labels (multilabel target) |
+| `text` | Issue title + body + comments |
+| `issue_number` | Original GitHub issue number |
+
+> Column types are inferred automatically from the dataset files.
 
 ---
 
 ## 🚀 Intended Use
 
-This dataset is suitable for:
-- Multilabel text classification
-- Label recommendation models
-- Semantic search and retrieval
-- Training LLM-powered developer tools
-
-Example use cases:
-- Auto-tagging incoming GitHub issues
-- Finding similar historical issues
-- Prioritization and triage dashboards
+- Multilabel classification
+- Issue triage automation
+- Semantic search
+- Developer-facing ML tools
 
 ---
 
 ## ⚠️ Limitations
 
-- Strong **class imbalance** (natural for GitHub data)
+- Natural class imbalance
 - Domain-specific to scikit-learn
-- Labels evolve over time as project conventions change
-
----
-
-## 🔗 Related Resources
-
-- Trained model on this dataset:  
-  👉 `Talip7/scikit-learn-multilabel-classifier`
-- Hugging Face LLM Course – Chapter 5 (Datasets & Semantic Search)
+- Label taxonomy evolves over time
 
 ---
 
 ## 👤 Author
 
 **Talip7**  
-AI / NLP practitioner focusing on **real-world datasets**,  
-end-to-end ML pipelines, and applied transformer models.
+Focused on applied NLP, real-world datasets, and production ML pipelines.