Update README.md

README.md

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 ## Overview
 
-`NeuroBERT-Mini` is a **lightweight** NLP model derived from **google/bert-base-uncased**, optimized for **real-time inference** on **edge and IoT devices**. With a quantized size of **~35MB** and **~10M parameters**, it delivers efficient contextual language understanding for resource-constrained environments like mobile apps, wearables, microcontrollers, and smart home devices. Designed for **low-latency** and **offline operation**, it’s ideal for privacy-first applications with limited connectivity.
+`NeuroBERT-Mini` is a **lightweight** NLP model derived from **google/bert-base-uncased**, optimized for **real-time inference** on **edge and IoT devices**. With a quantized size of **~35MB** and approximately **10 million parameters**, it enables efficient contextual language understanding in **resource-constrained environments** such as **mobile apps**, **wearables**, **microcontrollers**, and **smart home devices**.
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+In addition to its edge-ready design, `NeuroBERT-Mini` is suitable for a wide range of **general-purpose NLP tasks**, including **text classification**, **intent detection**, **semantic similarity**, and **information extraction**. Its compact architecture makes it ideal for **offline**, **privacy-first** applications that demand fast, on-device language processing without relying on constant cloud connectivity.
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+Whether you're building a **chatbot**, a **smart assistant**, or an **embedded NLP module**, `NeuroBERT-Mini` offers a strong balance of performance and portability for both specialized and mainstream NLP applications.
 
 - **Model Name**: NeuroBERT-Mini
 - **Size**: ~35MB (quantized)