Update README.md

README.md

@@ -47,18 +47,18 @@ metrics:
 library_name: transformers
 ---
 
-![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjs-oFsaBT02qc6fHOkisspeCbbWYewABhHI-_AcFaxR6DU298q4kiu_x3hga7P49fS8J1HMoxtSXBkXHqOFsqhvtqSakIteeEP7V7Thgjw2rhDBx_upegj16CZWkt2uvTs6DSEkDNB2BO0kswIDX7-DxnWyTRFiyCgT-3YFtSqbjyZaqygg9JTh7ZEI-k/s16000/bert-%20Emoji.png)
+![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhaQsGBpErn8tWyNzfwFkJa0TGL1MdGj94JoZYXP0nz_BBvoXuVC5JiUco2EbKb2CkefJ53uqo8gYTPf8OTp86wIRq7plhfAvRjMskOpspd5HH57J-llcLNNGdeVMDslACVCHnnzKiAA9eNoIVK2366IJGASCL4u5tSL2H1nIMkni00TSTYSeXOk14qZ2s/s16000/NeuroBERT-Mini.png)
 
-# 🧠 boltuix/bert-mini — Ultra Lightweight BERT for Real-Time NLP 🚀
+# 🧠 boltuix/NeuroBERT-Mini — Ultra Lightweight BERT for Real-Time NLP 🚀
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Model Size](https://img.shields.io/badge/Size-~40MB-blue)](#)
 [![Tasks](https://img.shields.io/badge/Tasks-NLI%20%7C%20Intent--Detection%20%7C%20Sentiment%20Analysis-orange)](#)
 [![Inference Speed](https://img.shields.io/badge/Optimized%20For-Edge%20Devices-green)](#)
 
-`bert-mini` is a compact, real-time Natural Language Processing (NLP) model derived from the original BERT architecture. Engineered for **low-latency** and **on-device inference**, it delivers impressive language understanding while keeping memory and compute requirements minimal — making it perfect for **IoT devices**, **mobile apps**, **wearables**, and **edge AI systems**.
+`NeuroBERT-Mini` is a compact, real-time Natural Language Processing (NLP) model derived from the original BERT architecture. Engineered for **low-latency** and **on-device inference**, it delivers impressive language understanding while keeping memory and compute requirements minimal — making it perfect for **IoT devices**, **mobile apps**, **wearables**, and **edge AI systems**.
 
-Unlike larger BERT variants, `bert-mini` retains deep **contextual understanding** even in resource-constrained environments, making it ideal for practical, production-ready applications in 2025 and beyond.
+Unlike larger BERT variants, `NeuroBERT-Mini` retains deep **contextual understanding** even in resource-constrained environments, making it ideal for practical, production-ready applications in 2025 and beyond.
 
 ---
 
@@ -110,7 +110,7 @@ pip install transformers torch
 from transformers import pipeline
 
 # Load the pipeline
-mlm_pipeline = pipeline("fill-mask", model="boltuix/bert-mini")
+mlm_pipeline = pipeline("fill-mask", model="boltuix/NeuroBERT-Mini")
 
 # Try a sentence
 result = mlm_pipeline("The robot can [MASK] the room in minutes.")
@@ -180,7 +180,7 @@ Input: Please [MASK] the door before leaving.
 
 ## 🏷️ Tags
 
-`#bert-mini` `#edge-nlp` `#lightweight-models` `#on-device-ai`  
+`#NeuroBERT-Mini` `#edge-nlp` `#lightweight-models` `#on-device-ai`  
 `#contextual-nlp` `#real-time-inference` `#offline-nlp` `#mobile-ai`  
 `#intent-recognition` `#named-entity-recognition` `#ner` `#text-classification`  
 `#transformers` `#tiny-transformers` `#embedded-nlp` `#smart-device-ai`