| --- |
| 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: |
| ```json |
| { |
| "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 |
| } |
| ``` |
