Update README.md

README.md

@@ -1,3 +1,105 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- wikimedia/wikipedia
+- bookcorpus/bookcorpus
+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
+---
+
+![Banner](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiWsG0Nmwt7QDnCpZuNrWGRaDGURIV9QWifhhaDbBDaCb0wPEeGQidUl-jgE-GC21QDa-3WXgpM6y9OTWjvhnpho9nDmDNf3MiHqhs-sfhwn-Rphj3FtASbbQMxyPx9agHSib-GPj18nAxkYonB6hOqCDAj0zGis2qICirmYI8waqxTo7xNtZ6Ju3yLQM8/s1920/bert-%20lite.png)
+
+# 🌟 bert-lite: A Lightweight BERT for Efficient NLP 🌟
+
+## 🚀 Overview
+Meet **bert-lite**—a streamlined marvel of NLP! 🎉 Designed with efficiency in mind, this model features a compact architecture tailored for tasks like **MNLI** and **NLI**, while excelling in low-resource environments. With a lightweight footprint, `bert-lite` is perfect for edge devices, IoT applications, and real-time NLP needs. 🌍
+
+---
+
+## 🌟 Why bert-lite? The Lightweight Edge
+- 🔍 **Compact Power**: Optimized for speed and size  
+- ⚡ **Fast Inference**: Blazing quick on constrained hardware  
+- 💾 **Small Footprint**: Minimal storage demands  
+- 🌱 **Eco-Friendly**: Low energy consumption  
+- 🎯 **Versatile**: IoT, wearables, smart homes, and more!
+
+---
+
+## 🧠 Model Details
+
+| Property           | Value                             |
+|-------------------|------------------------------------|
+| 🧱 Layers          | Custom lightweight design          |
+| 🧠 Hidden Size     | Optimized for efficiency           |
+| 👁️ Attention Heads | Minimal yet effective              |
+| ⚙️ Parameters      | Ultra-low parameter count          |
+| 💽 Size            | Quantized for minimal storage      |
+| 🌐 Base Model      | google-bert/bert-base-uncased      |
+| 🆙 Version         | v1.1 (April 04, 2025)              |
+
+---
+
+## 🔤 Usage Example – Masked Language Modeling (MLM)
+
+```python
+from transformers import pipeline
+
+# 📢 Start demo
+print("\n🔤 Masked Language Model (MLM) Demo")
+
+# 🧠 Load masked language model
+mlm_pipeline = pipeline("fill-mask", model="bert-base-uncased")
+
+# ✍️ Masked sentences
+masked_sentences = [
+    "The robot can [MASK] the room in minutes.",
+    "He decided to [MASK] the project early.",
+    "This device is [MASK] for small tasks.",
+    "The weather will [MASK] by tomorrow.",
+    "She loves to [MASK] in the garden.",
+    "Please [MASK] the door before leaving.",
+]
+
+# 🤖 Predict missing words
+for sentence in masked_sentences:
+    print(f"\nInput: {sentence}")
+    predictions = mlm_pipeline(sentence)
+    for pred in predictions[:3]:
+        print(f"✨ → {pred['sequence']} (score: {pred['score']:.4f})")