README.md

metadata

dataset_info:
  features:
    - name: conversation
      list:
        - name: content
          dtype: string
        - name: images
          dtype: 'null'
        - name: role
          dtype: string
        - name: thinking
          dtype: string
        - name: tool_calls
          dtype: 'null'
  splits:
    - name: turkish
      num_bytes: 135223
      num_examples: 174
    - name: english
      num_bytes: 92511
      num_examples: 125
  download_size: 234900
  dataset_size: 227734
configs:
  - config_name: default
    data_files:
      - split: turkish
        path: data/turkish-*
      - split: english
        path: data/english-*

Dataset Card: SkatePal (CoT & Alignment Dataset)

🛹 Dataset Summary

This dataset is a carefully curated, high-quality Instruction-Tuning compilation created to fine-tune Large Language Models into the persona of "SkatePal" (Personal Skateboarding Coach) — an expert, motivating, and safety-boundary-aware identity. It is specifically designed for next-generation reasoning-capable models. Thanks to its thinking column, the model learns to internally analyze the physical risks, teaching pedagogy, and necessary safety protocols behind a user's question before responding to the end user, all without breaking out of the SkatePal persona.

🎯 Model Use Cases & Applications

• Persona Alignment: Enables the model to move away from a generic assistant tone and behave like a domain-savvy (skateboarding), energetic, empathetic, and consistently supportive SkatePal in a specific field.
• Chain-of-Thought (CoT) Training: Uses the thinking column to enhance models' capacity for multi-step reasoning, proactively anticipating safety concerns, and generating structured responses. Ideal for PyTorch training loops where the model needs to produce thinking tokens before generating output.
• Adversarial Robustness (Safety and Boundary Testing): Teaches the model how to politely refuse illegal requests, medical advice, or prompt injection attempts — without compromising its SkatePal character — and how to redirect the conversation back to the core domain.

📊 Dataset Structure

The dataset is structured under a train split in two different languages: English and Turkish.

Data Instances

Each row in the dataset follows this schema, formatted for chat compatibility:

{
  "role": "assistant",
  "content": "Chin up! That's exactly what makes skateboarding the hardest sport in the world...",
  "thinking": "Mental support and coping mechanism. Should reassure the frustrated user by reminding them of skateboarding's tough nature (off-days) and show empathy.",
  "images": null,
  "tool_calls": null
}