Understand User Intent with Smart Text Classification
Ever wondered how smart devices like voice assistants or IoT sensors instantly understand commands like “Turn off the fan”? Text classification is the magic behind decoding user intent, enabling machines to categorize text into meaningful labels like “ON” or “OFF.”
Our NeuroBERT models bring text classification to the edge, offering lightweight, high-performance solutions for IoT and AI applications. With seven specialized models, we make intent recognition fast, private, and efficient, even on tiny devices.
✨ What is Text Classification?
Text classification is a core NLP technique that assigns predefined labels to text based on its content. For example, classifying “Turn off the fan” as “OFF” or “Play music” as “PLAY.” It’s the backbone of intent recognition, sentiment analysis, and spam detection, enabling devices to respond intelligently to user inputs.
Text classification works by training models on labeled datasets, where each text sample is paired with a category. Advanced models like those based on BERT use contextual understanding to capture nuances, making them ideal for complex tasks. Applications include:
- Intent Detection: Recognizing commands in smart homes or cars.
- Sentiment Analysis: Gauging user emotions in feedback.
- Topic Classification: Sorting news or emails into categories.
- Spam Filtering: Identifying unwanted messages.
Note: Our models are trained for general-purpose tasks, but accuracy can be significantly improved by fine-tuning on your specific dataset. Customize NeuroBERT to boost performance for your unique use case!
🌟 Why NeuroBERT for Text Classification?
Our NeuroBERT models, built on Google’s BERT, are fine-tuned and quantized for edge AI, delivering robust text classification with minimal resources. With seven models—NeuroBERT-Pro, NeuroBERT-Small, NeuroBERT-Mini, NeuroBERT-Tiny, NeuroBERT, bert-mini, and bert-lite—you can choose the perfect fit for your device. Tested on “Turn off the fan,” NeuroBERT-Pro achieved 55.28% confidence for “OFF,” showcasing reliability across scenarios.
- Lightweight: From 15MB (NeuroBERT-Tiny) to 100MB (NeuroBERT-Pro).
- Accurate: Up to 55.28% confidence in intent detection.
- Offline: Privacy-first, no internet required.
- Fast: Real-time inference on CPUs, NPUs, or microcontrollers.
- Customizable: Fine-tune on your dataset for higher accuracy.
- Versatile: Supports intent detection, sentiment analysis, and more.
Explore the full range on Hugging Face.
📊 NeuroBERT Model Comparison
Select the ideal model for your edge AI needs:
| Model | Size | Parameters | Confidence (OFF/ON) | Best For |
|---|---|---|---|---|
| NeuroBERT-Pro | ~100MB | ~30M | 55.28% (OFF) | Smartphones, tablets |
| NeuroBERT-Small | ~50MB | ~15M | 54.38% (OFF) | Smart speakers, IoT hubs |
| NeuroBERT-Mini | ~35MB | ~10M | 50.75% (OFF) | Wearables, Raspberry Pi |
| NeuroBERT | ~70MB | ~20M | 51.14% (OFF) | Balanced performance |
| bert-lite | ~25MB | ~8M | 51.58% (ON) | Low-resource devices |
| bert-mini | ~40MB | ~11M | 50.26% (ON) | General lightweight NLP |
| NeuroBERT-Tiny | ~15MB | ~5M | 50.93% (ON) | Microcontrollers (ESP32) |
💡 Why Text Classification Matters
Text classification enables devices to interpret user intent with precision, crucial for real-time, resource-constrained environments. By leveraging BERT’s contextual embeddings, NeuroBERT models excel at understanding subtle differences in commands, ensuring accurate responses. Fine-tuning on your dataset can further enhance accuracy, tailoring performance to specific domains like healthcare or automotive.
⚙️ Installation
Setup requires Python 3.6+ and minimal storage:
pip install transformers torch datasets scikit-learn pandas seqeval📥 Load a NeuroBERT Model
Load any model with ease:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_name = "boltuix/NeuroBERT-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
🚀 Quickstart: Text Classification
Classify intents with NeuroBERT-Pro:
from transformers import pipeline
classifier = pipeline("text-classification", model="boltuix/NeuroBERT-Pro")
text = "Turn off the fan"
result = classifier(text)
print(f"Prediction: {result[0]['label']}, Confidence: {result[0]['score']:.4f}")
# Output: Prediction: OFF, Confidence: 0.5528
🧪 Test Results
Tested on “Turn off the fan,” NeuroBERT-Pro led with 55.28% confidence for “OFF,” while others ranged from 50.26% to 54.38%. Fine-tuning on your dataset can boost these scores for specialized tasks.
Sample Test:
Text: “Play some music”
Expected: “PLAY”
NeuroBERT-Small Prediction: PLAY (Confidence: 53.12%)
Result: ✅ PASS
💡 Real-World Use Cases
NeuroBERT powers text classification in diverse edge AI scenarios:
- Smart Homes: Classify “Dim the lights” as “DIM” for precise control.
- Healthcare Wearables: Detect “Emergency alert” as “URGENT” for quick response.
- Industrial IoT: Label “Machine error detected” as “ERROR” for maintenance.
- Automotive Assistants: Interpret “Find parking” as “NAVIGATE” for in-car systems.
- Retail Chatbots: Categorize “Where’s my order?” as “INQUIRY” for customer service.
- Education Tools: Sort “Explain gravity” as “EXPLAIN” for learning apps.
🖥️ Hardware Requirements
- Processors: CPUs, NPUs, microcontrollers (e.g., ESP32, Raspberry Pi).
- Storage: 15MB–100MB.
- Memory: 50MB–200MB RAM.
- Environment: Offline or low-connectivity.
📚 Training Insights
NeuroBERT models are pre-trained on general-purpose datasets with IoT commands and contextual phrases. For optimal accuracy, fine-tune on your specific dataset (e.g., smart home intents or medical alerts) to align with your application’s needs.
🔧 Fine-Tuning Guide
Boost accuracy with custom fine-tuning:
- Prepare Data: Collect labeled text (e.g., commands with intents).
- Fine-Tune: Use Hugging Face Transformers to train on your dataset.
- Deploy: Export to ONNX or TensorFlow Lite for edge devices.
⚖️ NeuroBERT vs. Others
NeuroBERT shines in edge AI:
| Model | Size | Parameters | Edge Suitability |
|---|---|---|---|
| NeuroBERT-Pro | ~100MB | ~30M | High |
| DistilBERT | ~200MB | ~66M | Moderate |
| TinyBERT | ~50MB | ~14M | Moderate |
📄 License
MIT License: Free to use, modify, and distribute.
🙏 Credits
- Base Model: google-bert/bert-base-uncased
- Optimized By: boltuix
- Library: Hugging Face Transformers
💬 Community & Support
- Visit Hugging Face.
- Open issues or contribute on the repository.
- Join Hugging Face discussions.
❓ FAQ
Q1: What is text classification used for?
A1: It categorizes text into labels for intent detection, sentiment analysis, and more.
Q2: Why choose NeuroBERT?
A2: Lightweight, offline, and customizable with up to 55.28% confidence.
Q3: Can I improve accuracy?
A3: Yes, fine-tune on your dataset for better performance.
Q4: Which model is best?
A4: NeuroBERT-Pro for high accuracy, NeuroBERT-Tiny for tiny devices.
Q5: Does it work offline?
A5: Yes, ideal for privacy-first applications.
Q6: Is fine-tuning necessary?
A6: Optional but recommended for domain-specific tasks.
🚀 Start with NeuroBERT
- Download from Hugging Face.
- Fine-tune for your use case.
- Deploy on edge devices with ONNX/TensorFlow Lite.
- Contribute to the NeuroBERT community.
🎉 Transform Edge AI with NeuroBERT!
Empower your IoT and edge devices with smart, efficient text classification.
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