README.md

---
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
---

![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)              |

---

## 📜 License
MIT License — free to use, modify, and share.


## 🔤 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})")

```

---


## 🔤 Masked Language Model (MLM) Demo

Input: The robot can [MASK] the room in minutes.  
✨ → The robot can clean the room in minutes. (score: 0.3124)  
✨ → The robot can scan the room in minutes. (score: 0.1547)  
✨ → The robot can paint the room in minutes. (score: 0.0983)  

Input: He decided to [MASK] the project early.  
✨ → He decided to finish the project early. (score: 0.3876)  
✨ → He decided to start the project early. (score: 0.2109)  
✨ → He decided to abandon the project early. (score: 0.0765)  

Input: This device is [MASK] for small tasks.  
✨ → This device is perfect for small tasks. (score: 0.2458)  
✨ → This device is great for small tasks. (score: 0.1894)  
✨ → This device is useful for small tasks. (score: 0.1321)  

Input: The weather will [MASK] by tomorrow.  
✨ → The weather will improve by tomorrow. (score: 0.2987)  
✨ → The weather will change by tomorrow. (score: 0.1765)  
✨ → The weather will clear by tomorrow. (score: 0.1034)  

Input: She loves to [MASK] in the garden.  
✨ → She loves to work in the garden. (score: 0.3542)  
✨ → She loves to play in the garden. (score: 0.1986)  
✨ → She loves to relax in the garden. (score: 0.0879)  

Input: Please [MASK] the door before leaving.  
✨ → Please close the door before leaving. (score: 0.4673)  
✨ → Please lock the door before leaving. (score: 0.3215)  
✨ → Please open the door before leaving. (score: 0.0652)