---
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:

```text
<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:

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