boltuix / bert-lite

🌟 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

📜 License

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

🔤 Usage Example – Masked Language Modeling (MLM)

from transformers import pipeline

# 📢 Start demo
print("\n🔤 Masked Language Model (MLM) Demo")

# 🧠 Load masked language model : eg boltuix/bert-lite
mlm_pipeline = pipeline("fill-mask", model="boltuix/bert-lite")

# ✍️ 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)