Datasets:

tsilva
/

notebookops-synthetic

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - synthetic
  - instruction-tuning
  - alpaca-format
  - lora
  - notebookops
pretty_name: NotebookOps Synthetic Instruction Dataset
size_categories:
  - n<1K

NotebookOps Synthetic Instruction Dataset

NotebookOps is a small synthetic instruction-tuning dataset for beginner fine-tuning experiments. It teaches a model to convert natural-language productivity requests into a strict fake command format.

The format is intentionally artificial so that the before/after fine-tuning effect is easy to see:

<NBOOK>
intent: reminder.create
recipient: Sofia
date: tomorrow
time: 09:00
task: review invoice follow-up
</NBOOK>

Files

train.jsonl: 500 generated training examples
eval.jsonl: 80 held-out generated evaluation examples

Each row contains:

instruction: the instruction prompt
input: the natural-language request
output: the target NotebookOps record

Intents

The dataset covers five synthetic intents:

reminder.create
email.send
task.create
note.create
calendar.create

Intended Use

This dataset is meant for small LoRA/SFT demonstrations where the goal is to make fine-tuning behavior visually obvious. It is not intended as a production parser or a benchmark.

Generation

The dataset was generated deterministically with:

python3 generate_notebookops_dataset.py --train-size 500 --eval-size 80 --seed 3407