Neurobert Pro

🚀 NeuroBERT-Pro — The Pinnacle of Lightweight NLP for Cutting-Edge Intelligence

NeuroBERT-Pro is a state-of-the-art lightweight NLP model. It is derived from Google's BERT and is optimized for high accuracy and real - time inference on resource - constrained devices. With a small footprint and excellent performance, it's ideal for various privacy - first applications.

🚀 Quick Start

Installation

Install the required dependencies:

pip install transformers torch

Ensure your environment supports Python 3.6+ and has ~150MB of storage for model weights.

Download Instructions

1. Via Hugging Face:
   • Access the model at boltuix/NeuroBERT-Pro.
   • Download the model files (~150MB) or clone the repository:

     git clone https://huggingface.co/boltuix/NeuroBERT-Pro
     

2. Via Transformers Library:
   • Load the model directly in Python:

     from transformers import AutoModelForMaskedLM, AutoTokenizer
     model = AutoModelForMaskedLM.from_pretrained("boltuix/NeuroBERT-Pro")
     tokenizer = AutoTokenizer.from_pretrained("boltuix/NeuroBERT-Pro")
     

3. Manual Download:
   • Download quantized model weights from the Hugging Face model hub.
   • Extract and integrate into your edge/IoT application.

Quickstart: Masked Language Modeling

Predict missing words in IoT - related sentences with masked language modeling:

from transformers import pipeline

# Unleash the power
mlm_pipeline = pipeline("fill-mask", model="boltuix/NeuroBERT-Pro")

# Test the magic
result = mlm_pipeline("Please [MASK] the door before leaving.")
print(result[0]["sequence"])  # Output: "Please open the door before leaving."

Quickstart: Text Classification

Perform intent detection or text classification for IoT commands:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load tokenizer and classification model
model_name = "boltuix/NeuroBERT-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

# Example input
text = "Turn off the fan"

# Tokenize the input
inputs = tokenizer(text, return_tensors="pt")

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    pred = torch.argmax(probs, dim=1).item()

# Define labels
labels = ["OFF", "ON"]

# Print result
print(f"Text: {text}")
print(f"Predicted intent: {labels[pred]} (Confidence: {probs[0][pred]:.4f})")

✨ Features

• Flagship Performance: ~150MB footprint delivers near - BERT - base accuracy on constrained devices.
• Superior Contextual Understanding: Captures intricate semantic relationships with an 8 - layer, 512 - hidden architecture.
• Offline Capability: Fully functional without internet access.
• Real - Time Inference: Optimized for CPUs, mobile NPUs, and edge servers.
• Versatile Applications: Excels in masked language modeling (MLM), intent detection, text classification, and named entity recognition (NER).

📚 Documentation

Overview

• Model Name: NeuroBERT - Pro
• Size: ~150MB (quantized)
• Parameters: ~50M
• Architecture: Flagship BERT (8 layers, hidden size 512, 8 attention heads)
• Description: Flagship 8 - layer, 512 - hidden model
• License: MIT — free for commercial and personal use

Use Cases

NeuroBERT - Pro is designed for cutting - edge intelligence in edge and IoT scenarios, delivering unparalleled NLP accuracy on resource - constrained devices. Key applications include:

• Smart Home Devices: Parse highly nuanced commands like “Turn [MASK] the coffee machine” (predicts “on”) or “The fan will turn [MASK]” (predicts “off”).
• IoT Sensors: Interpret intricate sensor contexts, e.g., “The drone collects data using onboard [MASK]” (predicts “sensors”).
• Wearables: Real - time intent detection with high precision, e.g., “The music pauses when someone [MASK] the room” (predicts “enters”).
• Mobile Apps: Offline chatbots or semantic search with near - BERT - base accuracy, e.g., “She is a [MASK] at the hospital” (predicts “nurse”).
• Voice Assistants: Local command parsing with exceptional accuracy, e.g., “Please [MASK] the door” (predicts “shut”).
• Toy Robotics: Sophisticated command understanding for next - generation interactive toys.
• Fitness Trackers: Local text feedback processing, e.g., advanced sentiment analysis or personalized workout command recognition.
• Car Assistants: Offline command disambiguation for in - vehicle systems, enhancing safety and reliability without cloud reliance.

Hardware Requirements

• Processors: CPUs, mobile NPUs, or edge servers (e.g., Raspberry Pi 4, NVIDIA Jetson Nano)
• Storage: ~150MB for model weights (quantized for reduced footprint)
• Memory: ~200MB RAM for inference
• Environment: Offline or low - connectivity settings

Trained On

• Custom IoT Dataset: Curated data focused on IoT terminology, smart home commands, and sensor - related contexts (sourced from chatgpt - datasets). This enhances performance on tasks like intent detection, command parsing, and device control.

Fine - Tuning Guide

To adapt NeuroBERT - Pro for custom IoT tasks (e.g., specific smart home commands):

1. Prepare Dataset: Collect labeled data (e.g., commands with intents or masked sentences).
2. Fine - Tune with Hugging Face:

   #!pip uninstall -y transformers torch datasets
   #!pip install transformers==4.44.2 torch==2.4.1 datasets==3
   

🔧 Technical Details

Evaluation

NeuroBERT - Pro was evaluated on a masked language modeling task using 10 IoT - related sentences. The model predicts the top - 5 tokens for each masked word, and a test passes if the expected word is in the top - 5 predictions. With its flagship architecture, NeuroBERT - Pro achieves near - perfect performance.

Test Sentences

Evaluation Code

from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch

# Load model and tokenizer
model_name = "boltuix/NeuroBERT-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)
model.eval()

# Test data
tests = [
    ("She is a [MASK] at the local hospital.", "nurse"),
    ("Please [MASK] the door before leaving.", "shut"),
    ("The drone collects data using onboard [MASK].", "sensors"),
    ("The fan will turn [MASK] when the room is empty.", "off"),
    ("Turn [MASK] the coffee machine at 7 AM.", "on"),
    ("The hallway light switches on during the [MASK].", "night"),
    ("The air purifier turns on due to poor [MASK] quality.", "air"),
    ("The AC will not run if the door is [MASK].", "open"),
    ("Turn off the lights after [MASK] minutes.", "five"),
    ("The music pauses when someone [MASK] the room.", "enters")
]

results = []

# Run tests
for text, answer in tests:
    inputs = tokenizer(text, return_tensors="pt")
    mask_pos = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits[0, mask_pos, :]
    topk = logits.topk(5, dim=1)
    top_ids = topk.indices[0]
    top_scores = torch.softmax(topk.values, dim=1)[0]
    guesses = [(tokenizer.decode([i]).strip().lower(), float(score)) for i, score in zip(top_ids, top_scores)]
    results.append({
        "sentence": text,
        "expected": answer,
        "predictions": guesses,
        "pass": answer.lower() in [g[0] for g in guesses]
    })

# Print results
for r in results:
    status = "✓ PASS" if r["pass"] else "✗ FAIL"
    print(f"\n{r['sentence']}")
    print(f"Expected: {r['expected']}")
    print("Top - 5 Predictions (word : confidence):")
    for word, score in r['predictions']:
        print(f"   - {word:12} | {score:.4f}")
    print(status)

# Summary
pass_count = sum(r["pass"] for r in results)
print(f"\nTotal Passed: {pass_count}/{len(tests)}")

Sample Results (Hypothetical)

• Sentence: She is a [MASK] at the local hospital.
  Expected: nurse
  Top - 5: [nurse (0.50), doctor (0.20), surgeon (0.15), technician (0.10), assistant (0.05)]
  Result: ✓ PASS
• Sentence: Turn off the lights after [MASK] minutes.
  Expected: five
  Top - 5: [five (0.45), ten (0.25), three (0.15), fifteen (0.10), two (0.05)]
  Result: ✓ PASS
• Total Passed: ~10/10 (depends on fine - tuning).

Evaluation Metrics

Note: Metrics vary based on hardware (e.g., Raspberry Pi 4, Android devices) and fine - tuning. Test on your target device for accurate results.

📄 License

NeuroBERT - Pro is released under the MIT license.