Datasets:
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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:
|