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--- |
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license: mit |
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language: |
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- en |
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pretty_name: Synthetic On-Device Assistant Commands |
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size_categories: |
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- 10K<n<100K |
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tags: |
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- nlp |
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- text-classification |
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- synthetic-data |
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- on-device-ai |
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- intent-recognition |
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- mobilebert |
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--- |
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## Synthetic On-Device Assistant Commands (70K Records) |
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### Overview |
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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. |
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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. |
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### Supported Tasks and Intents |
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The dataset is structured for a multi-class **Intent Classification** task across seven common mobile device actions. |
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| Intent Category | Count (Records) | Example Commands | |
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| :--- | :--- | :--- | |
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| **making_call** | 10,000 | "Make a call to Anand," "Ring up my doctor." | |
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| **setting_alarm** | 10,000 | "Set alarm for 7 AM," "Wake me up at 06:45." | |
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| **setting_reminder** | 10,000 | "Remind me to pay rent tomorrow," "Set a reminder for the meeting." | |
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| **playing_song** | 10,000 | "Play 'Bohemian Rhapsody'," "Shuffle my workout playlist." | |
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| **navigation** | 10,000 | "Navigate to the nearest gas station," "Start driving home." | |
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| **weather_report** | 10,000 | "What is the weather in Croatia," "Will it rain today?" | |
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| **general_question** | 10,000 | "What's the best food in Croatia," "Tell me about the history of Rome." | |
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### Construction Methodology |
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The dataset was generated synthetically using **BERT (MobileBERT)** to create high-variability training samples. |
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1. **Seed Phrase Generation:** 100 unique root phrases were manually created for each category (e.g., "Call [person]" or "Start navigation to [place]"). |
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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). |
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3. **Combination:** This technique resulted in approximately 10,000 unique commands per category, yielding a final training set of **70,000 total records**. |
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### Data Format |
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The dataset is provided in a single CSV file. |
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### Usage Notes |
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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**. |
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