Adaptive Classifier

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.

Features

• 🚀 Works with any transformer classifier model
• 📈 Continuous learning capabilities
• 🎯 Dynamic class addition
• 💾 Safe and efficient state persistence
• 🔄 Prototype-based learning
• 🧠 Neural adaptation layer

Try Now

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

Installation

pip install adaptive-classifier

Quick Start

from 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")

# The library is also integrated with Hugging Face. So you can push and load from HF Hub.

# Save to Hub
classifier.push_to_hub("adaptive-classifier/model-name")

# Load from Hub
classifier = AdaptiveClassifier.from_pretrained("adaptive-classifier/model-name")

Advanced Usage

Adding New Classes Dynamically

# 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)

Continuous Learning

# 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)

How It Works

The system combines three key components:

1. Transformer Embeddings: Uses state-of-the-art language models for text representation

2. Prototype Memory: Maintains class prototypes for quick adaptation to new examples

3. Adaptive Neural Layer: Learns refined decision boundaries through continuous training

Requirements

• 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)

Benefits of Adaptive Classification in LLM Routing

We evaluate the effectiveness of adaptive classification in optimizing LLM routing decisions. Using the arena-hard-auto-v0.1 dataset with 500 queries, we compared routing performance with and without adaptation while maintaining consistent overall success rates.

Key Results

Metric	Without Adaptation	With Adaptation	Impact
High Model Routes	113 (22.6%)	98 (19.6%)	0.87x
Low Model Routes	387 (77.4%)	402 (80.4%)	1.04x
High Model Success Rate	40.71%	29.59%	0.73x
Low Model Success Rate	16.54%	20.15%	1.22x
Overall Success Rate	22.00%	22.00%	1.00x
Cost Savings*	25.60%	32.40%	1.27x

*Cost savings calculation assumes high-cost model is 2x the cost of low-cost model

Analysis

The results highlight several key benefits of adaptive classification:

1. Improved Cost Efficiency: While maintaining the same overall success rate (22%), the adaptive classifier achieved 32.40% cost savings compared to 25.60% without adaptation - a relative improvement of 1.27x in cost efficiency.

2. Better Resource Utilization: The adaptive system routed more queries to the low-cost model (402 vs 387) while reducing high-cost model usage (98 vs 113), demonstrating better resource allocation.

3. Learning from Experience: Through adaptation, the system improved the success rate of low-model routes from 16.54% to 20.15% (1.22x increase), showing effective learning from successful cases.

4. ROI on Adaptation: The system adapted to 110 new examples during evaluation, leading to a 6.80% improvement in cost savings while maintaining quality - demonstrating significant return on the adaptation investment.

This real-world evaluation demonstrates that adaptive classification can significantly improve cost efficiency in LLM routing without compromising overall performance.

References

• RouteLLM: Learning to Route LLMs with Preference Data
• Transformer^2: Self-adaptive LLMs
• Lamini Classifier Agent Toolkit
• Protoformer: Embedding Prototypes for Transformers
• Overcoming catastrophic forgetting in neural networks

Citation

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}
}