llm-config-optimizer

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: distilbert-base-uncased
• Number of Classes: 4
• Total Examples: 60
• Embedding Dimension: 768

Class Distribution

T0.0_P1.0_PP0.0_FP0.0: 18 examples (30.0%)
T0.7_P1.0_PP0.0_FP0.0: 22 examples (36.7%)
T1.0_P0.1_PP0.0_FP0.0: 1 examples (1.7%)
T1.0_P1.0_PP0.0_FP0.0: 19 examples (31.7%)

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: 51
• 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}
}