Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- wikimedia/wikipedia
+- bookcorpus/bookcorpus
+- SetFit/mnli
+- sentence-transformers/all-nli
+language:
+- en
+new_version: v1.1
+base_model:
+- google-bert/bert-base-uncased
+pipeline_tag: text-classification
+tags:
+- BERT
+- MNLI
+- NLI
+- transformer
+- pre-training
+- nlp
+- tiny-bert
+- edge-ai
+- transformers
+- low-resource
+- micro-nlp
+- quantized
+- iot
+- wearable-ai
+- offline-assistant
+- intent-detection
+- real-time
+- smart-home
+- embedded-systems
+- command-classification
+- toy-robotics
+- voice-ai
+- eco-ai
+- english
+- lightweight
+- mobile-nlp
+metrics:
+- accuracy
+- f1
+- inference
+- recall
+library_name: transformers
+---
+
+![bert-mini-banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWsG0Nmwt7QDnCpZuNrWGRaDGURIV9QWifhhaDbBDaCb0wPEeGQidUl-jgE-GC21QDa-3WXgpM6y9OTWjvhnpho9nDmDNf3MiHqhs-sfhwn-Rphj3FtASbbQMxyPx9agHSib-GPj18nAxkYonB6hOqCDAj0zGis2qICirmYI8waqxTo7xNtZ6Ju3yLQM8/s1920/bert-%20lite.png)
+
+# 🧠 boltuix/bert-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-~44MB-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 NLP model derived from BERT but streamlined for blazing-fast inference on constrained hardware — think IoT, wearables, and mobile apps. 🛰️
+
+---
+
+## 🚀 Key Features
+
+| Feature                | Description                                           |
+|------------------------|-------------------------------------------------------|
+| 🔍 **Architecture**    | Lightweight BERT (≈4 layers, hidden size 256)         |
+| ⚙️ **Parameters**      | ~11M (vs. 110M in BERT-base)                          |
+| 💾 **Model Size**      | ~44MB (quantized)                                     |
+| ⚡ **Speed**            | Real-time inference on mobile and edge devices        |
+| 🌍 **Use Cases**       | NLI, intent detection, voice assistants, offline chat |
+| 📚 **Datasets**        | Wikipedia, BookCorpus, MNLI, All-NLI                  |
+| 🧪 **Training Tasks**  | Masked LM, NLI classification                         |
+| 📜 **License**         | MIT — free to use, modify, and distribute             |
+
+---
+
+## 📦 Installation
+
+Install dependencies:
+
+```bash
+pip install transformers torch
+```
+
+---
+
+## 🔤 Quickstart: Masked Language Prediction
+
+```python
+from transformers import pipeline
+
+# Load the pipeline
+mlm_pipeline = pipeline("fill-mask", model="boltuix/bert-mini")
+
+# Try a sentence
+result = mlm_pipeline("The robot can [MASK] the room in minutes.")
+print(result[0]["sequence"])
+```
+
+---
+
+## 💡 Sample Outputs
+
+```python
+Input: The device can [MASK] quickly.
+✨ → the device can operate quickly.
+✨ → the device can function quickly.
+
+Input: Please [MASK] the door before leaving.
+✨ → please open the door before leaving.
+✨ → please shut the door before leaving.
+```
+
+---
+
+## 🔬 Evaluation Metrics
+
+| Metric     | Value (Approx.)       |
+|------------|-----------------------|
+| ✅ Accuracy | ~90–97% of BERT-base  |
+| 🎯 F1 Score | Balanced performance  |
+| ⚡ Latency  | Fast on Raspberry Pi / Android |
+
+---
+
+## 🌐 Use Cases
+
+- 🔊 **Voice Assistants**: Smart speaker command disambiguation  
+- 🏠 **Smart Homes**: On-device NLP for offline automation  
+- 🤖 **Toy & Robotics**: Lightweight command understanding  
+- ⌚ **Wearables**: Real-time sentiment & intent detection  
+- 🧪 **AI on Budget**: NLP on minimal compute resources  
+
+---
+
+## 📚 Trained On
+
+- **Wikipedia**
+- **BookCorpus**
+- **MNLI** (MultiNLI)
+- **All-NLI** from `sentence-transformers`
+
+---
+
+## 🏷️ Tags
+
+`#tiny-bert` `#edge-ai` `#intent-detection` `#nlp` `#smart-home` `#wearable-ai` `#offline-assistant` `#transformers` `#real-time`
+
+---
+
+## 📜 License
+
+MIT License — free for commercial and personal use.
+
+---
+
+## 🙌 Credits
+
+Developed by [Hari Shankar S (boltuix)](https://huggingface.co/boltuix)  
+Base Model: [`google-bert/bert-base-uncased`](https://huggingface.co/google-bert/bert-base-uncased)  
+Optimized and Quantized for edge AI scenarios.
+
+---