Fine-tune BERT to extract precise answers from any context — build smart chatbots, help systems, and AI assistants that truly understand your users.
Our NeuroBERT models bring QA to the edge, offering lightweight, high-performance solutions for IoT and AI applications. With seven specialized models, we make question answering fast, private, and efficient on resource-constrained devices. Explore them on Hugging Face.
✨ What is Question Answering (QA)?
Question Answering (QA) is an NLP task where a model extracts precise answers from a given context in response to a question. For example, given the context “France’s capital is Paris” and the question “What is the capital of France?”, QA identifies “Paris” as the answer.
QA systems use contextual understanding, often powered by BERT-based models, to locate and extract relevant text spans. This makes QA ideal for applications requiring quick, accurate information retrieval, such as:
- Virtual Assistants: Answering user queries like “Where is the Eiffel Tower?”
- IoT Analytics: Extracting insights from sensor data descriptions.
- Educational Tools: Providing answers for learning apps.
- Customer Support: Responding to FAQs in chatbots.
Note: Our models are pre-trained for general-purpose QA. Fine-tuning on your specific dataset can significantly enhance accuracy for domain-specific questions, such as technical support or medical inquiries.
🌟 Why NeuroBERT for Question Answering?
Built on Google’s BERT architecture, our NeuroBERT models are fine-tuned and quantized for edge AI, delivering robust QA with minimal resources. The seven models—NeuroBERT-Pro, NeuroBERT-Small, NeuroBERT-Mini, NeuroBERT-Tiny, NeuroBERT, bert-mini, and bert-lite—offer flexibility for various devices. Tested on questions like “What is the capital of France?”, NeuroBERT-Small accurately extracted “Paris” from context, showcasing reliability.
- Lightweight: From 15MB (NeuroBERT-Tiny) to 100MB (NeuroBERT-Pro).
- Accurate: Precise answer extraction for diverse questions.
- Offline: Privacy-first, no internet required.
- Fast: Real-time inference on CPUs, NPUs, or microcontrollers.
- Customizable: Fine-tune for your domain to improve accuracy.
- Versatile: Supports QA, NER, text classification, and more.
Discover NeuroBERT’s QA capabilities on Hugging Face.
📊 NeuroBERT Model Comparison
Select the ideal model for your edge AI QA needs:
| Model | Size | Parameters | QA 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 Question Answering Matters
QA enables devices to provide instant, accurate responses, crucial for real-time applications in resource-constrained environments. By leveraging BERT’s contextual embeddings, NeuroBERT models excel at extracting answers from complex texts, ensuring reliable performance. Fine-tuning on your dataset can tailor QA to specific domains, such as education or customer support.
⚙️ Installation
Setup requires Python 3.6+ and minimal storage:
pip install transformers datasets torch📥 Load a NeuroBERT Model
Load a model for QA:
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
model_name = "boltuix/NeuroBERT-Small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
🚀 Quickstart: QA in Action
Answer questions with NeuroBERT-Small:
from transformers import pipeline
qa_pipeline = pipeline("question-answering", model="boltuix/NeuroBERT-Small")
result = qa_pipeline(
question="What is the capital of France?",
context="France's capital is Paris."
)
print(f"Answer: {result['answer']}, Score: {result['score']:.4f}")
# Output: Answer: Paris, Score: 0.8923
🧪 Test Results
NeuroBERT-Small was fine-tuned on a custom QA dataset, accurately answering “What is the capital of France?” with “Paris.” Other NeuroBERT models support QA with varying efficiency, from NeuroBERT-Pro’s high accuracy to NeuroBERT-Tiny’s ultra-light footprint. Fine-tuning on your dataset can further optimize performance.
Sample Test:
Question: “Where is the Eiffel Tower located?”
Context: “The Eiffel Tower is a famous landmark in Paris.”
NeuroBERT-Small Output: Paris (Score: 0.8756)
Result: ✅ PASS
💡 Real-World Use Cases
NeuroBERT models enable QA in diverse edge AI scenarios:
- Smart Assistants: Answer “What’s the weather in London?” from local data.
- Educational IoT: Respond to “Who invented the telephone?” with “Bell.”
- Industrial IoT: Extract “What caused the machine error?” from logs.
- Customer Support: Reply to “Where is my order?” with location details.
- Healthcare Devices: Answer “What is the patient’s condition?” from records.
- Navigation Systems: Provide “What’s the nearest gas station?” with addresses.
🖥️ Hardware Requirements
- Processors: CPUs, NPUs, microcontrollers (e.g., ESP32, Raspberry Pi).
- Storage: 15MB–500MB.
- Memory: 50MB–200MB RAM.
- Environment: Offline or low-connectivity.
📚 Training Insights
NeuroBERT models are pre-trained for general NLP, with QA support demonstrated through fine-tuning on custom datasets. Fine-tuning on your specific dataset (e.g., industry FAQs or educational content) ensures optimal accuracy for targeted QA tasks.
🔧 Fine-Tuning Guide
Optimize QA performance:
- Prepare Data: Create a dataset with questions, contexts, and answers.
- Fine-Tune: Use Hugging Face Transformers to train models.
- Deploy: Export to ONNX or TensorFlow Lite for edge devices.
⚖️ NeuroBERT vs. Others
NeuroBERT models are edge-optimized:
| Model | Size | Parameters | Edge Suitability |
|---|---|---|---|
| NeuroBERT-Small | ~50MB | ~15M | 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.
- Open issues or contribute on the repository.
- Join Hugging Face discussions.
❓ FAQ
Q1: What is QA used for?
A1: QA extracts answers from text for assistants, analytics, or education.
Q2: Why choose NeuroBERT?
A2: Lightweight, offline, and accurate for edge AI QA.
Q3: Can I improve QA accuracy?
A3: Yes, fine-tune on your dataset for better results.
Q4: Which model is best?
A4: NeuroBERT-Pro for high accuracy, NeuroBERT-Tiny for tiny devices.
Q5: Does QA work offline?
A5: Yes, fully offline for privacy.
Q6: How to fine-tune for QA?
A6: Use Hugging Face with a custom QA dataset.
🚀 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 question answering.
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