metadata
language: multilingual
tags:
- adaptive-classifier
- text-classification
- continuous-learning
license: apache-2.0
Adaptive Classifier
This model is an instance of an adaptive-classifier that allows for continuous learning and dynamic class addition.
Installation
IMPORTANT: To use this model, you must first install the adaptive-classifier library. You do NOT need trust_remote_code=True.
pip install adaptive-classifier
Model Details
- Base Model: bert-base-uncased
- Number of Classes: 4
- Total Examples: 809
- Embedding Dimension: 768
Class Distribution
google/gemini-2.5-flash: 34 examples (4.2%)
google/gemini-2.5-flash-lite: 99 examples (12.2%)
openai/gpt-4o: 215 examples (26.6%)
openai/gpt-4o-mini: 461 examples (57.0%)
Usage
After installing the adaptive-classifier library, you can load and use this model:
from adaptive_classifier import AdaptiveClassifier
# Load the model (no trust_remote_code needed!)
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 for continuous learning
texts = ["Example 1", "Example 2"]
labels = ["class1", "class2"]
classifier.add_examples(texts, labels)
Note: This model uses the adaptive-classifier library distributed via PyPI. You do NOT need to set trust_remote_code=True - just install the library first.
Training Details
- Training Steps: 1
- 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 1000 examples per class
- Updates prototypes every 100 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}
}