Upload README.md

README.md

@@ -1,3 +1,154 @@
 ---
-license: cc-by-nc-nd-4.0
+language:
+- en
+license: other
+task_categories:
+- text-classification
+- conversational
+task_ids:
+- intent-classification
+- sentiment-classification
+tags:
+- ecommerce
+- customer-service
+- synthetic
+- intent
+- sentiment
+- chatbot
+- dialogue
+- jsonl
+size_categories:
+- 1K<n<10K
 ---
+
+# EcommerceAI Dataset — English E-commerce Customer Service
+
+## Dataset Description
+
+A high-quality **synthetic** English-language dataset of e-commerce 
+customer service conversations with full intent and sentiment annotations.
+
+> ⚠️ **Transparency Notice:** This is a synthetic dataset. All conversations 
+> are programmatically generated. No real customer data is included. 
+> Every record is labeled `"data_type": "SYNTHETIC"` in metadata.
+
+---
+
+## Dataset Stats
+
+| Metric | Value |
+|--------|-------|
+| Conversations | 5,000 |
+| Dialogue Turns | 47,028 |
+| Avg Turns/Conversation | 9.4 |
+| Language | English |
+| Issue Categories | 5 |
+| Product Categories | 10 |
+| Intent Classes | 12 |
+
+---
+
+## Issue Categories
+
+| Category | Count |
+|----------|-------|
+| Late Delivery | 1,000 |
+| Wrong Item Received | 1,000 |
+| Refund Request | 1,000 |
+| Damaged Item | 1,000 |
+| Product Inquiry | 1,000 |
+
+---
+
+## Data Schema
+
+```json
+{
+  "id": "uuid-v4",
+  "metadata": {
+    "domain": "ecommerce_customer_service",
+    "issue_type": "late_delivery",
+    "product_category": "electronics",
+    "language": "en",
+    "data_type": "SYNTHETIC",
+    "quality_tier": "enterprise",
+    "turns_count": 10
+  },
+  "conversation": [
+    {
+      "role": "user",
+      "content": "My order hasn't arrived in 7 days.",
+      "intent": "late_delivery",
+      "sentiment": "negative"
+    },
+    {
+      "role": "agent",
+      "content": "I'm sorry to hear that. Let me check your order.",
+      "intent": "acknowledge",
+      "sentiment": "positive"
+    }
+  ]
+}
+```
+
+---
+
+## Quick Load
+
+```python
+# Option 1 — HuggingFace Datasets
+from datasets import load_dataset
+ds = load_dataset("YOUR_USERNAME/ecommerce-cs-dataset")
+
+# Option 2 — Pure Python
+import json
+conversations = []
+with open('ecommerce_cs_en_synthetic.jsonl') as f:
+    for line in f:
+        conversations.append(json.loads(line))
+
+# Filter by issue type
+refunds = [c for c in conversations 
+           if c['metadata']['issue_type'] == 'refund_request']
+```
+
+---
+
+## Use Cases
+
+- ✅ Fine-tuning LLMs for customer service chatbots
+- ✅ Training intent classifiers (12 classes)
+- ✅ Training sentiment analysis models
+- ✅ Dialogue state tracking research
+- ✅ Augmenting real-world datasets
+- ✅ Testing chatbot pipelines
+
+---
+
+## 🔒 Full Dataset (Commercial License)
+
+This repository contains a **free sample of 500 conversations.**
+
+The full dataset **(5,000 conversations, 47K+ turns)** with commercial 
+license is available here:
+
+👉 **[Get Full Dataset on Sellix → YOUR_SELLIX_LINK]**
+
+Includes:
+- Full 5,000 conversation JSONL
+- Complete JSON array format
+- Data card & documentation
+- Commercial use license
+
+---
+
+## License
+
+Sample (this repo): CC BY-NC 4.0 — free for research & personal use.  
+Full dataset: Commercial license — see Sellix listing for terms.
+
+---
+
+## Citation
+
+If you use this dataset in research, please cite: