---
license: apache-2.0
task_categories:
- text-generation
language:
- tr
tags:
- e-commerce
- customer-support
- chain-of-thought
- instruction-tuning
pretty_name: TR E-Commerce Customer Support Instructions
size_categories:
- n<1K
---

# TR E-Commerce Customer Support Instructions 🇹🇷

A high-quality **Turkish E-Commerce Customer Support** dataset designed for fine-tuning large language models (LLMs) on instruction-following customer support tasks.

## Dataset Summary

| Feature | Value |
|---------|-------|
| **Language** | Turkish (tr) |
| **Domain** | E-Commerce Customer Support |
| **Format** | Conversation (Conversational/Chat) |
| **Chain-of-Thought (CoT)** | ✅ Natural paragraph reasoning (`thinking` field) |
| **Total Categories** | 20 |
| **Total Rows** | 186 conversations |

---

## Categories & Distribution

| # | Category | Description | Count |
|---|----------|-------------|-------|
| 1 | **Sipariş Oluşturma** | Cart errors, page load issues during checkout, guest checkout | 10 |
| 2 | **Sipariş İptali** | Order cancellation before/after shipping, partial cancellation | 10 |
| 3 | **Sipariş Takibi** | Status tracking, order processing delays, missing confirmation | 10 |
| 4 | **Kargo Gecikmesi** | Delivery delay, package stuck in transit, express delivery issues | 8 |
| 5 | **Teslimat Sorunlari** | Package marked delivered but not received, left with neighbor | 10 |
| 6 | **Eksik Ürün** | Missing items in order, missing accessories inside the box | 8 |
| 7 | **Yanlis Ürün** | Wrong color, size, or completely incorrect model delivered | 8 |
| 8 | **Hasarli Ürün** | Broken/cracked items, damaged packaging, used product received | 8 |
| 9 | **İade** | Return request, right of withdrawal, refund status, return shipping | 12 |
| 10 | **Değişim** | Size exchange, color exchange, product exchange process | 10 |
| 11 | **Ödeme** | 3D Secure failures, double charges, installments, bank transfer | 12 |
| 12 | **Kupon** | Coupon code errors, expired codes, minimum cart value rules | 8 |
| 13 | **Kampanya** | Discounts not applied, campaign rules, first-order discounts | 10 |
| 14 | **Hesap** | Login issues, account deletion (GDPR/KVKK), account hijacking | 10 |
| 15 | **Şifre** | Password reset steps, verification email not arriving | 8 |
| 16 | **Adres Değişikliği** | Changing address after shipment, updating registration address | 8 |
| 17 | **Fatura** | Invoice details correction, corporate invoice, PDF download | 8 |
| 18 | **Satıcı İletişimi** | Seller not responding, counterfeit suspicion, seller disputes | 10 |
| 19 | **Ürün Değerlendirmesi** | Product authenticity, stock query, size charts, descriptions | 10 |
| 20 | **Garanti** | Warranty period, warranty coverage, authorized service centers | 8 |

---

## Data Format

Each conversation is formatted as a list of message dictionaries featuring `role`, `content`, `images`, `thinking` (for the assistant), and `tool_calls` fields:

```json
[
  {
    "role": "user",
    "content": "merhaba siparişim nerede kaldı acaba",
    "images": null,
    "thinking": null,
    "tool_calls": null
  },
  {
    "role": "assistant",
    "content": "Merhaba, hemen kontrol edelim. Siparişinizin durumunu sorgulayabilmem için e-posta adresinizi veya takip numaranızı paylaşabilir misiniz?",
    "images": null,
    "thinking": "Kullanıcı siparişinin nerede olduğunu merak ediyor. Muhtemelen kargoya verilmiş ama takip bilgisi güncellenmemiş olabilir. Kargo takip numarasıyla kontrol etmesini önermeli ya da sipariş bilgisini alıp ben kontrol etmeliyim.",
    "tool_calls": null
  }
]
```

### Dataset Key Design Features

1. **Natural User Messages**: Users write in realistic, natural Turkish. Some messages contain missing punctuation or all-lowercase letters (mimicking actual human messaging habits), without using excessive chat slangs or abbreviations.
2. **Diverse Assistant Openings**: Assistant responses use more than 10 different opening variations to avoid repetitive patterns in LLM fine-tuning.
3. **Fluid Chain-of-Thought (CoT)**: Assistant entries include a single-paragraph `thinking` block containing the logical reasoning flow before producing the final response.
4. **Varied Information Requests**: Unlike rigid datasets asking only for order numbers, the assistant requests different details depending on the scenario (e.g., e-mail, phone number, tracking ID, screenshot) or resolves the query directly without asking for any information.
5. **Length Constraints**: Assistant responses are kept concise (ranging from 100 to 170 words) to mimic realistic customer support agent behaviors.

---

## Usage

You can easily load this dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("Mer1Alii/TR-ECommerce-CustomerSupport-Instructions")
print(dataset["train"][0])
```

---

## Fine-Tuning Tools

This dataset is compatible with popular training frameworks such as:
- [Unsloth](https://github.com/unslothai/unsloth)
- [Hugging Face TRL](https://github.com/huggingface/trl)
- [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory)
- [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)

## License

This dataset is licensed under the Apache 2.0 License. It is intended for educational, research, and commercial applications.

## Developer

**[Mert Ali Alkan](https://github.com/MertAlii)**