Datasets:
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.
- Seed Phrase Generation: 100 unique root phrases were manually created for each category (e.g., "Call [person]" or "Start navigation to [place]").
- Variable Injection: BERT was used to generate and inject unique, domain-specific entities (e.g., thousands of unique names for the
making_callintent, and thousands of unique places for thenavigationintent). - 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.