A flexible, adaptive classification system that allows for dynamic addition of new classes and continuous learning from examples. Built on top of transformers from HuggingFace, this library provides an easy-to-use interface for creating and updating text classifiers.
- 🚀 Works with any transformer classifier model
- 📈 Continuous learning capabilities
- 🎯 Dynamic class addition
- 💾 Safe and efficient state persistence
- 🔄 Prototype-based learning
- 🧠 Neural adaptation layer
| Use Case | Demonstrates | Link |
|---|---|---|
| Basic Example (Cat or Dog) | Continuous learning |  |
| Support Ticket Classification | Realistic examples | |
| Query Classification | Different configurations | |
| Multilingual Sentiment Analysis | Ensemble of classifiers | |
| Product Category Classification | Batch processing | |
pip install adaptive-classifierfrom adaptive_classifier import AdaptiveClassifier
# Initialize with any HuggingFace model
classifier = AdaptiveClassifier("bert-base-uncased")
# Add some examples
texts = [
"The product works great!",
"Terrible experience",
"Neutral about this purchase"
]
labels = ["positive", "negative", "neutral"]
classifier.add_examples(texts, labels)
# Make predictions
predictions = classifier.predict("This is amazing!")
print(predictions) # [('positive', 0.85), ('neutral', 0.12), ('negative', 0.03)]
# Save the classifier
classifier.save("./my_classifier")
# Load it later
loaded_classifier = AdaptiveClassifier.load("./my_classifier")# Add a completely new class
new_texts = [
"Error code 404 appeared",
"System crashed after update"
]
new_labels = ["technical"] * 2
classifier.add_examples(new_texts, new_labels)# Add more examples to existing classes
more_examples = [
"Best purchase ever!",
"Highly recommend this"
]
more_labels = ["positive"] * 2
classifier.add_examples(more_examples, more_labels)The system combines three key components:
-
Transformer Embeddings: Uses state-of-the-art language models for text representation
-
Prototype Memory: Maintains class prototypes for quick adaptation to new examples
-
Adaptive Neural Layer: Learns refined decision boundaries through continuous training
- Python ≥ 3.8
- PyTorch ≥ 2.0
- transformers ≥ 4.30.0
- safetensors ≥ 0.3.1
- faiss-cpu ≥ 1.7.4 (or faiss-gpu for GPU support)
- Transformer^2: Self-adaptive LLMs
- Lamini Classifier Agent Toolkit
- Protoformer: Embedding Prototypes for Transformers
- Overcoming catastrophic forgetting in neural networks
If you use this library in your research, please cite:
@software{adaptive_classifier,
title = {Adaptive Classifier: Dynamic Text Classification with Continuous Learning},
author = {Asankhaya Sharma},
year = {2025},
publisher = {GitHub},
url = {https://github.com/codelion/adaptive-classifier}
}