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README.md

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+# 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 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Zmvtb3XUFtUImEmYdKpkuqmxKVlRxzt9?usp=sharing) |
+| Support Ticket Classification| Realistic examples | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1yeVCi_Cdx2jtM7HI0gbU6VlZDJsg_m8u?usp=sharing) |
+| Query Classification  | Different configurations | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1b2q303CLDRQAkC65Rtwcoj09ovR0mGwz?usp=sharing) |
+| Multilingual Sentiment Analysis | Ensemble of classifiers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14tfRi_DtL-QgjBMgVRrsLwcov-zqbKBl?usp=sharing) |
+| Product Category Classification | Batch processing | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VyxVubB8LXXES6qElEYJL241emkV_Wxc?usp=sharing) |
+
+## Installation
+
+```bash
+pip install adaptive-classifier
+```
+
+## Quick Start
+
+```python
+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")
+```
+
+## Advanced Usage
+
+### Adding New Classes Dynamically
+
+```python
+# 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
+
+```python
+# 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](https://arxiv.org/abs/2406.18665)
+- [Transformer^2: Self-adaptive LLMs](https://arxiv.org/abs/2501.06252)
+- [Lamini Classifier Agent Toolkit](https://www.lamini.ai/blog/classifier-agent-toolkit)
+- [Protoformer: Embedding Prototypes for Transformers](https://arxiv.org/abs/2206.12710)
+- [Overcoming catastrophic forgetting in neural networks](https://arxiv.org/abs/1612.00796)
+
+## Citation
+
+If you use this library in your research, please cite:
+
+```bibtex
+@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}
+}
+```