Datasets:

Kicaulah
/

script_daataset_ai

@@ -1,140 +0,0 @@
----
-language:
-- en
-tags:
-- code
-size_categories:
-- 100K<n<1M
----
-# 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: