Update README.md

README.md

@@ -46,7 +46,7 @@ library_name: transformers
 # 🧠 NeuroBERT — The Brain of Lightweight NLP for Real-World Intelligence 🌍
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Model Size](https://img.shields.io/badge/Size-~55MB-blue)](#)
+[![Model Size](https://img.shields.io/badge/Size-~57MB-blue)](#)
 [![Tasks](https://img.shields.io/badge/Tasks-MLM%20%7C%20Intent%20Detection%20%7C%20Text%20Classification%20%7C%20NER-orange)](#)
 [![Inference Speed](https://img.shields.io/badge/Blazing%20Fast-Edge%20Devices-green)](#)
 
@@ -72,10 +72,10 @@ library_name: transformers
 
 ## Overview
 
-`NeuroBERT` is an **advanced lightweight** NLP model derived from **google/bert-base-uncased**, optimized for **real-time inference** on **resource-constrained devices**. With a quantized size of **~55MB** and **~30M parameters**, it delivers powerful contextual language understanding for real-world applications in environments like mobile apps, wearables, microcontrollers, and smart home devices. Designed for **low-latency**, **offline operation**, and **real-world intelligence**, it’s ideal for privacy-first applications requiring robust intent detection, classification, and semantic understanding with limited connectivity.
+`NeuroBERT` is an **advanced lightweight** NLP model derived from **google/bert-base-uncased**, optimized for **real-time inference** on **resource-constrained devices**. With a quantized size of **~57MB** and **~30M parameters**, it delivers powerful contextual language understanding for real-world applications in environments like mobile apps, wearables, microcontrollers, and smart home devices. Designed for **low-latency**, **offline operation**, and **real-world intelligence**, it’s ideal for privacy-first applications requiring robust intent detection, classification, and semantic understanding with limited connectivity.
 
 - **Model Name**: NeuroBERT
-- **Size**: ~55MB (quantized)
+- **Size**: ~57MB (quantized)
 - **Parameters**: ~30M
 - **Architecture**: Advanced BERT (8 layers, hidden size 256, 4 attention heads)
 - **Description**: Advanced 8-layer, 256-hidden
@@ -83,7 +83,7 @@ library_name: transformers
 
 ## Key Features
 
-- ⚡ **Lightweight Powerhouse**: ~55MB footprint fits devices with constrained storage while offering advanced NLP capabilities.
+- ⚡ **Lightweight Powerhouse**: ~57MB footprint fits devices with constrained storage while offering advanced NLP capabilities.
 - 🧠 **Deep Contextual Understanding**: Captures complex semantic relationships with an 8-layer architecture.
 - 📶 **Offline Capability**: Fully functional without internet access.
 - ⚙️ **Real-Time Inference**: Optimized for CPUs, mobile NPUs, and microcontrollers.
@@ -97,13 +97,13 @@ Install the required dependencies:
 pip install transformers torch
 ```
 
-Ensure your environment supports Python 3.6+ and has ~55MB of storage for model weights.
+Ensure your environment supports Python 3.6+ and has ~57MB of storage for model weights.
 
 ## Download Instructions
 
 1. **Via Hugging Face**:
    - Access the model at [boltuix/NeuroBERT](https://huggingface.co/boltuix/NeuroBERT).
-   - Download the model files (~55MB) or clone the repository:
+   - Download the model files (~57MB) or clone the repository:
      ```bash
      git clone https://huggingface.co/boltuix/NeuroBERT
      ```
@@ -293,7 +293,7 @@ NeuroBERT is designed for **real-world intelligence** in **edge and IoT scenario
 ## Hardware Requirements
 
 - **Processors**: CPUs, mobile NPUs, or microcontrollers (e.g., Raspberry Pi, ESP32-S3)
-- **Storage**: ~55MB for model weights (quantized for reduced footprint)
+- **Storage**: ~57MB for model weights (quantized for reduced footprint)
 - **Memory**: ~120MB RAM for inference
 - **Environment**: Offline or low-connectivity settings
 
@@ -391,7 +391,7 @@ To adapt NeuroBERT for custom IoT tasks (e.g., specific smart home commands):
 
 | Model           | Parameters | Size   | Edge/IoT Focus | Tasks Supported         |
 |-----------------|------------|--------|----------------|-------------------------|
-| NeuroBERT       | ~30M       | ~55MB  | High           | MLM, NER, Classification |
+| NeuroBERT       | ~30M       | ~57MB  | High           | MLM, NER, Classification |
 | NeuroBERT-Small | ~20M       | ~50MB  | High           | MLM, NER, Classification |
 | NeuroBERT-Mini  | ~7M        | ~35MB  | High           | MLM, NER, Classification |
 | NeuroBERT-Tiny  | ~4M        | ~15MB  | High           | MLM, NER, Classification |