Adaptive Classifier
This model is an instance of an adaptive-classifier that allows for continuous learning and dynamic class addition.
You can install it with pip install adaptive-classifier.
Model Details
- Base Model: answerdotai/ModernBERT-base
- Number of Classes: 4
- Total Examples: 20
- Embedding Dimension: 768
Class Distribution
books: 5 examples (25.0%)
clothing: 5 examples (25.0%)
electronics: 5 examples (25.0%)
home_garden: 5 examples (25.0%)
Usage
from adaptive_classifier import AdaptiveClassifier
# Load the model
classifier = AdaptiveClassifier.from_pretrained("adaptive-classifier/model-name")
# Make predictions
text = "Your text here"
predictions = classifier.predict(text)
print(predictions) # List of (label, confidence) tuples
# Add new examples
texts = ["Example 1", "Example 2"]
labels = ["class1", "class2"]
classifier.add_examples(texts, labels)
Training Details
- Training Steps: 11
- Examples per Class: See distribution above
- Prototype Memory: Active
- Neural Adaptation: Active
Limitations
This model:
- Requires at least 3 examples per class
- Has a maximum of 150 examples per class
- Updates prototypes every 10 examples
Citation
@software{adaptive_classifier,
title = {Adaptive Classifier: Dynamic Text Classification with Continuous Learning},
author = {Sharma, Asankhaya},
year = {2025},
publisher = {GitHub},
url = {https://github.com/codelion/adaptive-classifier}
}