README.md

metadata

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-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. 🌍

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🌟 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!

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🧠 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)

from transformers import pipeline

# 📢 Start demo
mlm_pipeline = pipeline("fill-mask", model="boltuix/bert-lite")

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.",
]

for sentence in masked_sentences:
    print(f"Input: {sentence}")
    predictions = mlm_pipeline(sentence)
    for pred in predictions[:3]:
        print(f"✨ → {pred['sequence']} (score: {pred['score']:.4f})")

---

🔤 Masked Language Model (MLM)'s Output

Input: The robot can [MASK] the room in minutes.
✨ → the robot can leave the room in minutes. (score: 0.1608)
✨ → the robot can enter the room in minutes. (score: 0.1067)
✨ → the robot can open the room in minutes. (score: 0.0498)
Input: He decided to [MASK] the project early.
✨ → he decided to start the project early. (score: 0.1503)
✨ → he decided to continue the project early. (score: 0.0812)
✨ → he decided to leave the project early. (score: 0.0412)
Input: This device is [MASK] for small tasks.
✨ → this device is used for small tasks. (score: 0.4118)
✨ → this device is useful for small tasks. (score: 0.0615)
✨ → this device is required for small tasks. (score: 0.0427)
Input: The weather will [MASK] by tomorrow.
✨ → the weather will be by tomorrow. (score: 0.0980)
✨ → the weather will begin by tomorrow. (score: 0.0868)
✨ → the weather will come by tomorrow. (score: 0.0657)
Input: She loves to [MASK] in the garden.
✨ → she loves to live in the garden. (score: 0.3112)
✨ → she loves to stay in the garden. (score: 0.0823)
✨ → she loves to be in the garden. (score: 0.0796)
Input: Please [MASK] the door before leaving.
✨ → please open the door before leaving. (score: 0.3421)
✨ → please shut the door before leaving. (score: 0.3208)
✨ → please closed the door before leaving. (score: 0.0599)

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💡 Who's It For?

👨‍💻 Developers: Lightweight NLP apps for mobile or IoT

🤖 Innovators: Power wearables, smart homes, or robots

🧪 Enthusiasts: Experiment on a budget

🌿 Eco-Warriors: Reduce AI’s carbon footprint

📈 Metrics That Matter

✅ Accuracy: Competitive with larger models

🎯 F1 Score: Balanced precision and recall

⚡ Inference Time: Optimized for real-time use

🧪 Trained On

📘 Wikipedia 📚 BookCorpus 🧾 MNLI (Multi-Genre NLI) 🔗 sentence-transformers/all-nli

🔖 Tags

#tiny-bert #iot #wearable-ai #intent-detection #smart-home #offline-assistant #nlp #transformers