cngchis/Support-Ticket-Router-12K-Cleaned

---
license: apache-2.0
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- synthetic
- customer-support
- saas
- intent-classification
pretty_name: Support Ticket Router (12K Cleaned)
size_categories:
- 10K<n<100K
---

# 🔥 Support-Ticket-Router-12K-Cleaned

This dataset is a cleaned and structured version of real-world-like customer support messages designed for intent classification and routing tasks in SaaS / IT support systems.

It is intended for training and evaluating LLM-based or classical NLP intent classifiers for automated customer support ticket routing.

---

## 🧪 Data Source

This dataset is **synthetically generated using GPT-4-class models (GPT-4 / GPT-4o-style prompting)** with additional rule-based filtering and normalization.

The data was created to simulate realistic customer support scenarios in SaaS / IT service environments, including:

- API integration issues
- Billing and payment problems
- Subscription cancellation requests
- Technical bugs and system failures
- General customer complaints
- Plan upgrade/downgrade requests

---

### 🧠 Generation Process

- Prompts were designed to mimic real customer support tickets
- Multiple variations of each intent were generated for diversity
- Responses were normalized into 6 fixed intent classes
- Noise and ambiguous samples were filtered out
- Final dataset was manually and programmatically cleaned for consistency

---

### ⚠️ Notes

- This dataset is **synthetic and not collected from real users**
- It is intended for **research, benchmarking, and model training only**
- No real customer or personal data is included

---

## 🧠 Summary
- Total records: ~12,000+
- Task: Single-label Intent Classification
- Domain: Customer Support AI (SaaS / IT Services)
- Labels:
- api
- billing
- cancellation
- complaint
- technical
- upgrade

---

## 📌 Dataset Structure

Each example follows a unified format:

```json
{
"text": "user message",
"label": "intent label",
"meta": {
"source": "synthetic + real-world inspired",
"domain": "SaaS customer support"
}
}
```

Or instruction-style format (for LLM fine-tuning):

```json
{
"input": "Classify the customer support message into one of the following intents: api, billing, cancellation, complaint, technical, upgrade.\n\nMessage: I want to cancel my subscription.",
"output": "cancellation"
}
```

---

## 🧹 Cleaning Process

This dataset has been carefully processed to improve label quality and consistency:

- Removed ambiguous or noisy samples
- Normalized intent categories into 6 standard labels
- Filtered duplicate or near-duplicate entries
- Standardized user message formatting
- Balanced distribution across intent classes

---

## 📊 Dataset Statistics

| Label | Description |
|--------------|-------------|
| api | API usage, integration, endpoint issues |
| billing | Payment, invoice, pricing issues |
| cancellation | Stop subscription, churn intent |
| complaint | Dissatisfaction without clear category |
| technical | Bugs, errors, system issues |
| upgrade | Plan change, feature upgrade |

---

## ⚙️ Usage

```json
from datasets import load_dataset

dataset = load_dataset("cngchis/Support-Ticket-Router-12K-Cleaned")

train = dataset["train"]
test = dataset["test"]
```

---

## 🚀 Use Cases

This dataset can be used for:

- Intent classification models
- LLM fine-tuning (SFT / instruction tuning)
- Customer support automation systems
- Ticket routing systems in SaaS platforms
- Benchmarking lightweight LLMs (GGUF / 4-bit models)

---

## 📚 Model Compatibility

Works well with:

- BERT / RoBERTa / DeBERTa
- LLaMA / Mistral / Phi models
- GGUF (llama.cpp)
- Instruction-tuned LLMs

---

## 🧾 Citation

If you use this dataset, please cite:
```json
@misc{support_ticket_router_12k,
title={Support-Ticket-Router-12K-Cleaned},
author={cngchis},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/datasets/cngchis/Support-Ticket-Router-12K-Cleaned}
}
```