Bert Lite

🚀 bert-lite: A Lightweight BERT for Efficient NLP

bert-lite is a lightweight NLP model designed for efficiency. It is tailored for tasks like MNLI and NLI, and performs well in low-resource environments. With its compact architecture, it is suitable for edge devices, IoT applications, and real - time NLP needs.

🚀 Quick Start

Basic Usage

from transformers import pipeline
mlm = pipeline("fill-mask", model="boltuix/bert-lite")
result = mlm("The cat [MASK] on the mat.")
print(result[0]['sequence'])  # ✨ "The cat sat on the mat."

Advanced Usage

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})")

✨ Features

• 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: Suitable for IoT, wearables, smart homes, and more.
• Strong Contextual Understanding: Captures language nuances with bidirectional attention and resolves ambiguities.
• Lightweight Learning: Can learn from minimal data and adapt to niche domains.
• Competitive Metrics: Achieves competitive accuracy, F1 score, and inference time compared to larger models.

📦 Installation

The installation process is related to the transformers library. You can install it using the following command:

pip install transformers

📚 Documentation

Model Details

Metrics

• Accuracy: Competitive with larger models.
• F1 Score: Balanced precision and recall.
• Inference Time: Optimized for real - time use.

Use Cases

• Developers: Build 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.

🔧 Technical Details

bert-lite is based on the BERT framework. It has a custom lightweight design with a reduced number of layers, hidden size, and attention heads. This reduction in parameters and size allows it to run efficiently on edge devices. The model is fine - tuned on datasets like MNLI and all - nli, enabling it to adapt to different NLP tasks.

📄 License

This project is under the MIT License, which means it is free to use, modify, and share.

Tags

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