- Create useful instruction examples for SFT.
- Split train, validation, and test without contaminating results.
- Remove sensitive data, duplicates, and mediocre responses.
Simple format
{"instruction":"Clasifica el email","input":"Hola, quiero cambiar mi factura...","output":"categoria: facturacion\nprioridad: media\naccion: pedir numero de factura"}
{"instruction":"Redacta respuesta breve","input":"Cliente pide plazo de entrega","output":"Hola, gracias por escribir. El plazo estimado es..."}
{"instruction":"Extrae campos","input":"Presupuesto para 3 licencias anuales","output":"{"producto":"licencia anual","cantidad":3}"}Cleaning checklist
- No emails, phone numbers, national IDs, keys, or real names unless you have a legal basis.
- No duplicate examples between train and test.
- No contradictory responses for the same instruction.
- Include real-world errors from the domain, not only perfect cases.
- Include examples where the model should reject or ask for clarification.
dataset/ train.jsonl validation.jsonl test.jsonl README.md data_card.md redactions.log