Conversational Data for Teaching and Instruction-Following Models

A dataset of conversational exchanges for training teaching and instruction-following AI models, created by author drwlf and updated in June 2025. Each conversation contains multiple turns with structured 'role' and 'content' fields.

Use Cases

• Train a language model to generate assistant responses conditioned on the user 'role' and 'content' fields.
• Fine-tune an instruction-following model using the structured 'content' list containing message type and text.
• Analyze conversational patterns and teaching strategies across the 'user' and 'assistant' role turns.

Strengths

• Data is structured with explicit 'role' and 'content' fields for each conversational turn.
• Dataset is focused on the specific domain of teaching and instruction-following.

Limitations

• Unknown dataset size, row count, and column details limit assessment of scale and structure.
• Lack of sample data or description of 'content' list complexity prevents evaluation of data richness.

Provenance

Source

huggingface

Collection Method

null

Time Range

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Freshness

Last updated June 25, -2025.

Geography

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