Update README.md

README.md

@@ -12,29 +12,27 @@ license: apache-2.0
 
 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
+## Usage
 
-- 🚀 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
+```python
 pip install adaptive-classifier
+
+from adaptive_classifier import AdaptiveClassifier
+# Load from Hub
+classifier = AdaptiveClassifier.from_pretrained("adaptive-classifier/model-name")
+# 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)]
 ```
 
 ## How It Works
@@ -46,25 +44,3 @@ The system combines three key components:
 2. **Prototype Memory**: Maintains class prototypes for quick adaptation to new examples
 
 3. **Adaptive Neural Layer**: Learns refined decision boundaries through continuous training
-
-## 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}
-}
-```