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SouravAnand/Assistant_NLP_Dataset

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… See the full description on the dataset page: https://huggingface.co/datasets/SouravAnand/Assistant_NLP_Dataset.

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Dataset Card

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.