NeuroLocale: Intelligent Local Search for Edge AI and IoT

Overview

NeuroLocale is a fine-tuned transformer model built on boltuix/NeuroBERT for intent classification, designed to interpret natural language queries and suggest 120+ local business categories. With ~50 MB footprint, it’s optimized for edge AI, IoT devices, and mobile applications, achieving 94.26% accuracy. Ideal for local search, chatbots, and smart assistants, NeuroLocale delivers real-time, offline-capable solutions.

NeuroLocale transforms local search by understanding user intent with precision and speed.

Key Features

• Intent-Driven: Understands queries like “My dog is sick” to suggest 🐾 pet stores or 🩺 clinics.
• High Accuracy: 94.26% test accuracy across 122 cases.
• 120+ Categories: Covers diverse local businesses, from 💼 accounting to 🦒 zoos.
• Edge-Optimized: Fast inference (<50ms) on resource-constrained devices.
• Offline Ready: No internet required.
• Extensible: Fine-tunable for additional NLP tasks.

Use Cases

• Local Search Apps: Suggest 🐾 pet stores or 🩺 clinics based on user needs.
• Chatbots: Enhance customer service with context-aware recommendations.
• E-Commerce: Guide users to nearby 💼 accounting firms or 📚 bookstores.
• Travel Apps: Recommend 🏨 hotels or 🗺️ attractions.
• Healthcare: Direct users to 🏥 hospitals or 💊 pharmacies.
• Smart Assistants: Enable hands-free local search on IoT devices.

Supported Categories

NeuroLocale supports 120+ local business categories, including:

• 💼 Accounting Firm
• ✈️ Airport
• 🎢 Amusement Park
• 🐠 Aquarium
• 🖼️ Art Gallery
• 🥐 Bakery
• 🏦 Bank
• 🍻 Bar
• 💈 Barber Shop
• 🏖️ Beach
• ...and many more (full list available on Hugging Face)

Extract all categories programmatically:

from transformers import AutoModelForSequenceClassification

# Load model
model = AutoModelForSequenceClassification.from_pretrained("boltuix/NeuroLocale")

# Extract labels
label_mapping = model.config.id2label
supported_labels = sorted(label_mapping.values())

# Print categories
print("Supported Categories:", supported_labels)
                        Copy Code

Getting Started

Inference Example

from transformers import pipeline

# Load classifier
classifier = pipeline("text-classification", model="boltuix/NeuroLocale")

# Predict intent
result = classifier("Where can I see ocean creatures behind glass?")
print(result)  # Output: [{'label': 'aquarium', 'score': 0.999}]
                        Copy Code

Installation

pip install transformers torch pandas scikit-learn tqdm
                        Copy Code

Requires Python 3.8+, ~50 MB for model.

Performance Metrics

Tested on 122 cases with 94.26% accuracy (115/122 correct):

Metric	Value
Accuracy	94.26%
F1 Score (Weighted)	~0.94 (estimated)
Processing Time	<50ms per query

Example results:

Query	Expected Category	Predicted Category	Confidence	Status
How do I catch the early ride to the runway?	✈️ Airport	✈️ Airport	0.997	✅
Are the roller coasters still running today?	🎢 Amusement Park	🎢 Amusement Park	0.997	✅
Where can I see ocean creatures behind glass?	🐠 Aquarium	🐠 Aquarium	1.000	✅

Fine-Tuning Guide

Fine-tune NeuroLocale on custom datasets:

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
from datasets import load_dataset
import torch

# Load dataset (CSV with text and label columns)
dataset = load_dataset("csv", data_files="custom_intent_dataset.csv")

# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("boltuix/NeuroLocale")
model = AutoModelForSequenceClassification.from_pretrained("boltuix/NeuroLocale")

# Tokenize dataset
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=128)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Training arguments
training_args = TrainingArguments(
    output_dir="./neurolocale_finetuned",
    eval_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
    report_to="none"
)

# Initialize trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["validation"]
)

# Train
trainer.train()

# Save model
model.save_pretrained("./neurolocale_finetuned")
tokenizer.save_pretrained("./neurolocale_finetuned")
                        Copy Code

Other Capabilities

While optimized for intent classification, NeuroLocale’s NeuroBERT base supports fine-tuning for:

• Question Answering: Answer queries like “What’s the nearest hospital?”
• Sentiment Analysis: Detect positive/negative sentiment in user queries.
• Semantic Similarity: Cluster similar intents for advanced routing.
• Named Entity Recognition: Extract entities (e.g., locations, organizations).

Dataset Details

• Source: Open-source and synthetic queries (e.g., ChatGPT, Grok).
• Format: CSV with text and label columns.
• Categories: 120+ local businesses.
• Size: Model footprint ~50 MB.

Comparison to Other Solutions

Solution	Categories	Accuracy	NLP Strength	Open Source
NeuroLocale	120+	94.26%	Strong	Yes
Google Maps API	~100	~85%	Moderate	No
Yelp API	~80	~80%	Weak	No
OpenStreetMap	Varies	Varies	Weak	Yes

Frequently Asked Questions (FAQ)

License

Apache-2.0 License: Free to use. See LICENSE.

Support & Community

• Hugging Face Model Page
• Transformers Documentation
• Contact: boltuix@gmail.com

Conclusion

NeuroLocale revolutionizes local search with 94.26% accuracy across 120+ categories, optimized for edge AI and IoT. From smart assistants to travel apps, it’s your go-to solution for intent-driven search in 2025. Explore it on Hugging Face!