Update README.md

README.md

@@ -5,4 +5,49 @@ language:
 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**.
+
 ---