README.md

metadata

title: README
emoji: 📚
colorFrom: blue
colorTo: purple
sdk: static
pinned: false
license: apache-2.0

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.

Usage

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

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