---
language:
- tr
license: cc-by-4.0
size_categories:
- 100M<n<1B
task_categories:
- text-generation
- question-answering
tags:
- reasoning
- logic
- turkish
- pretraining
- synthetic
pretty_name: Turkish Reasoning Data
---

# Turkish Reasoning Data

A large-scale **synthetic Turkish logic-puzzle dataset** designed for pretraining and
fine-tuning language models on structured reasoning tasks.

## Dataset Summary

| Property | Value |
|---|---|
| Language | Turkish (`tr`) |
| Format | Parquet (HuggingFace Datasets compatible) |
| Approximate tokens | ~100 million |
| Number of examples | ~720,000 |
| Split | `train` only |
| License | CC BY 4.0 |

## Load with `datasets`

```python
from datasets import load_dataset

ds = load_dataset("DevHunterAI/turkish-reasoning-data", split="train")
print(ds[0])
# {'text': '<doc>\nAşağıdaki ipuçlarına...\n</doc>'}
```

## Reasoning Categories

Each example belongs to one of eight logic-puzzle categories:

| Category (TR) | Category (EN) | Weight |
|---|---|---|
| Sıralama bulmacaları | Ordering puzzles | 25 % |
| Doğru/Yanlış ifade | True/False statement evaluation | 15 % |
| Çelişki tespiti | Contradiction detection | 15 % |
| Kim ne yaptı | Person–action–object matching | 15 % |
| Ev/kat bulmacaları | Zebra-style house/floor puzzles | 10 % |
| Seçim argümanı | Argument strength evaluation | 10 % |
| Sayısal sıralama | Numerical magnitude reasoning | 10 % |
| Zaman çizelgesi | Timeline / before–after reasoning | 10 % |

## Format

Every example has a single `text` field containing a `<doc>...</doc>` block:

```
<doc>
Aşağıdaki ipuçlarına göre Ahmet, Zeynep, Mert kişilerinin
sıralamayı belirleyin:

- Zeynep, Mert'den önce gelir.
- Ahmet 1. sıradadır.

Doğru sıralama nedir?

Adım adım çözüm:
  1. Ahmet
  2. Zeynep
  3. Mert

Sonuç: Doğru sıralama Ahmet > Zeynep > Mert şeklindedir.
</doc>
```

## Intended Use

- **Pretraining** Turkish language models on structured reasoning
- **Fine-tuning** (split `text` on the solution separator to get instruction/response pairs)
- **Evaluation** of step-by-step reasoning in Turkish

## Generation

Generated programmatically with `random.seed(42)` using randomized templates
covering Turkish names, professions, cities, colors, objects, and events.

## Limitations

- Fully synthetic — does not cover real-world or open-ended reasoning
- Puzzle difficulty is limited; puzzles involve 3–5 entities
- Solutions are template-generated

## Citation

```bibtex
@dataset{devhunterai2025turkish_reasoning,
  title     = {Turkish Reasoning Data},
  author    = {DevHunterAI},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/DevHunterAI/turkish-reasoning-data}
}
```