NER Made Simple – Understand What Matters in Every Sentence | Named Entity Recognition with Lightweight NLP

🧠 Unlock Named Entity Recognition with Lightweight NLP 📍

How do smart devices extract key details like names, locations, or dates from text? Named Entity Recognition (NER) is the NLP technique that identifies and classifies entities, such as “Joe Biden” or “New York,” in sentences, powering everything from voice assistants to IoT analytics.

Our NeuroBERT models, optimized for edge AI, deliver fast and accurate NER on resource-constrained devices. With seven lightweight models, including the fine-tuned EntityBERT, we make entity extraction seamless and efficient. Explore them on Hugging Face.

✨ What is Named Entity Recognition (NER)?

NER is a specialized NLP task that identifies and categorizes named entities—such as people, organizations, locations, dates, and more—within text. For example, in “President Joe Biden visited New York,” NER tags “Joe Biden” as a person and “New York” as a location.

NER relies on contextual models like BERT to understand word relationships, making it essential for applications requiring structured data extraction. Key uses include:

• Information Extraction: Pulling names, places, or dates from documents.
• Search Optimization: Enhancing search engines with entity-based queries.
• Chatbots: Understanding user queries like “Book a flight to Paris.”
• Data Analytics: Extracting insights from IoT sensor logs or reports.

Note: Our models, including EntityBERT, are pre-trained for general-purpose NER. Fine-tuning on your specific dataset can significantly improve accuracy for domain-specific entities, such as medical terms or industrial jargon.

🌟 Why NeuroBERT for NER?

Our NeuroBERT models, built on Google’s BERT and fine-tuned for edge AI, excel at NER with minimal resources. The EntityBERT model, trained on the CoNLL-2025 NER dataset, sets the standard, while our seven models—NeuroBERT-Pro, NeuroBERT-Small, NeuroBERT-Mini, NeuroBERT-Tiny, NeuroBERT, bert-mini, and bert-lite—offer flexibility for various devices. From microcontrollers to smartphones, NeuroBERT delivers:

• Lightweight: Sizes from 15MB (NeuroBERT-Tiny) to 100MB (NeuroBERT-Pro).
• Accurate: EntityBERT achieves high precision on CoNLL-2025 entities.
• Offline: Privacy-first, no internet needed.
• Fast: Real-time inference on CPUs, NPUs, or microcontrollers.
• Customizable: Fine-tune for your domain to boost accuracy.
• Versatile: Supports NER, text classification, and more.

Discover the power of NER with NeuroBERT on Hugging Face.

📊 NeuroBERT Model Comparison

Choose the right model for your edge AI NER needs:

Model	Size	Parameters	NER Capability	Best For
NeuroBERT-Pro	~100MB	~30M	High accuracy	Smartphones, tablets
NeuroBERT-Small	~50MB	~15M	Balanced	Smart speakers, IoT hubs
NeuroBERT-Mini	~35MB	~10M	Efficient	Wearables, Raspberry Pi
NeuroBERT	~70MB	~20M	Versatile	Balanced performance
bert-lite	~25MB	~8M	Lightweight	Low-resource devices
bert-mini	~40MB	~11M	Compact	General lightweight NLP
NeuroBERT-Tiny	~15MB	~5M	Ultra-light	Microcontrollers (ESP32)

💡 Why NER Matters

NER transforms unstructured text into structured data, enabling devices to extract actionable insights. By identifying entities like “1275 Kinnear Rd” as an address or “Joe Biden” as a person, NER powers intelligent applications in resource-constrained environments. Fine-tuning NeuroBERT models, including EntityBERT, on your dataset ensures precision for specific domains, from legal texts to IoT logs.

⚙️ Installation

Setup requires Python 3.6+ and minimal storage:

pip install transformers datasets tokenizers seqeval pandas pyarrow evaluate

📥 Load EntityBERT for NER

Load the fine-tuned EntityBERT model:

from transformers import AutoModelForTokenClassification, AutoTokenizer

model_name = "boltuix/EntityBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)

🚀 Quickstart: NER in Action

Extract entities with EntityBERT:

from transformers import pipeline

nlp = pipeline("token-classification", model="boltuix/EntityBERT")
text = "Joe Biden visited New York on July 4th, 2023."
results = nlp(text)

for item in results:
    print(f"Entity: {item['word']}, Type: {item['entity']}, Score: {item['score']:.4f}")
# Example Output:
# Entity: Joe, Type: B-PER, Score: 0.9987
# Entity: Biden, Type: I-PER, Score: 0.9991
# Entity: New, Type: B-LOC, Score: 0.9975
# Entity: York, Type: I-LOC, Score: 0.9982
# Entity: July, Type: B-DATE, Score: 0.9968

🧪 Test Results

EntityBERT, based on bert-mini, was fine-tuned on the CoNLL-2025 NER dataset, achieving high precision for entities like persons, locations, and dates. Other NeuroBERT models support NER with varying efficiency, from NeuroBERT-Pro’s robust accuracy to NeuroBERT-Tiny’s ultra-light footprint. Fine-tuning on your dataset can further optimize performance.

Sample Test:
Text: “1275 Kinnear Rd, Columbus, OH”
EntityBERT Output: Address (1275 Kinnear Rd, Columbus, OH)
Result: ✅ PASS

💡 Real-World Use Cases

NeuroBERT models, including EntityBERT, enable NER in diverse edge AI scenarios:

• Smart Assistants: Extract “Paris” from “Book a flight to Paris” as a location.
• Healthcare IoT: Identify “Dr. Smith” as a person in medical reports.
• Industrial IoT: Tag “Factory A” as a location in sensor logs.
• Navigation Systems: Recognize “1275 Kinnear Rd” as an address for routing.
• Legal Tech: Extract “July 4th, 2023” as a date from contracts.
• Retail Chatbots: Identify “New York” in customer queries for localized service.

🖥️ 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

EntityBERT was fine-tuned on the CoNLL-2025 NER dataset, covering entities like persons, organizations, and locations. Other NeuroBERT models are pre-trained for general NLP, with NER support. Fine-tuning on your dataset (e.g., industry-specific entities) enhances accuracy for specialized tasks.

🔧 Fine-Tuning Guide

Optimize NER performance:

• Prepare Data: Collect labeled text with entities (e.g., CoNLL format).
• Fine-Tune: Use Hugging Face Transformers (see EntityBERT.ipynb).
• Deploy: Export to ONNX or TensorFlow Lite for edge devices.

⚖️ NeuroBERT vs. Others

NeuroBERT models are edge-optimized:

Model	Size	Parameters	Edge Suitability
EntityBERT	~40MB	~11M	High
NeuroBERT-Pro	~100MB	~30M	High
DistilBERT	~200MB	~66M	Moderate
BERT-Base	~400MB	~110M	Low

📄 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.
• Check EntityBERT.ipynb for code.
• Open issues or contribute on the repository.

❓ FAQ

Q1: What is NER used for?
A1: NER extracts entities like names, places, or dates for analytics, search, or chatbots.

Q2: Why choose NeuroBERT?
A2: Lightweight, offline, and accurate, with EntityBERT optimized for NER.

Q3: Can I improve NER accuracy?
A3: Yes, fine-tune on your dataset for better results.

Q4: Which model is best?
A4: EntityBERT for NER, NeuroBERT-Pro for high accuracy, NeuroBERT-Tiny for tiny devices.

Q5: Does NER work offline?
A5: Yes, fully offline for privacy.

Q6: How to fine-tune EntityBERT?
A6: Follow the code in EntityBERT.ipynb on Hugging Face.

🚀 Start with NeuroBERT

• Download from Hugging Face.
• Fine-tune for your domain.
• 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 precise, lightweight NER.

SOURCE CODE: 

https://huggingface.co/boltuix/EntityBERT/blob/main/EntityBERT.ipynb