---
license: mit
language:
- en
pretty_name: Synthetic On-Device Assistant Commands
size_categories:
- 10K<n<100K
tags:
- nlp
- text-classification
- synthetic-data
- on-device-ai
- intent-recognition
- mobilebert
---

## Synthetic On-Device Assistant Commands (70K Records)

### Overview
This dataset, comprising **70000 unique synthetic command phrases**, was created to train a robust, low-latency text classifier for an **offline, private AI assistant** application on Android.

It addresses the lack of publicly available, high-variability command datasets tailored for edge computing and low-latency intent recognition. The resulting model, optimized with post-training quantization, operates entirely on-device, prioritizing user privacy and speed.

### Supported Tasks and Intents

The dataset is structured for a multi-class **Intent Classification** task across seven common mobile device actions.

| Intent Category | Count (Records) | Example Commands |
| :--- | :--- | :--- |
| **making_call** | 10,000 | "Make a call to Anand," "Ring up my doctor." |
| **setting_alarm** | 10,000 | "Set alarm for 7 AM," "Wake me up at 06:45." |
| **setting_reminder** | 10,000 | "Remind me to pay rent tomorrow," "Set a reminder for the meeting." |
| **playing_song** | 10,000 | "Play 'Bohemian Rhapsody'," "Shuffle my workout playlist." |
| **navigation** | 10,000 | "Navigate to the nearest gas station," "Start driving home." |
| **weather_report** | 10,000 | "What is the weather in Croatia," "Will it rain today?" |
| **general_question** | 10,000 | "What's the best food in Croatia," "Tell me about the history of Rome." |

### Construction Methodology

The dataset was generated synthetically using **BERT (MobileBERT)** to create high-variability training samples.
1. **Seed Phrase Generation:** 100 unique root phrases were manually created for each category (e.g., "Call [person]" or "Start navigation to [place]").
2. **Variable Injection:** BERT was used to generate and inject unique, domain-specific entities (e.g., thousands of unique names for the `making_call` intent, and thousands of unique places for the `navigation` intent).
3. **Combination:** This technique resulted in approximately 10,000 unique commands per category, yielding a final training set of **70,000 total records**.

### Data Format

The dataset is provided in a single CSV file.

### Usage Notes

This dataset is perfect for training lightweight transformer models (like MobileBERT) for applications in **Edge AI**, **on-device machine learning**, and **privacy-first application development**.

---