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adaptive-classifier/
llm-hallucination-detector

Name: llm-hallucination-detector
Availability: InStock

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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-large
Number of Classes: 2
Total Examples: 200
Embedding Dimension: 1024

Class Distribution

HALLUCINATED: 100 examples (50.0%)
NOT_HALLUCINATED: 100 examples (50.0%)

Usage

python

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: 36
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 100 examples per class
Updates prototypes every 10 examples

Citation

bibtex

@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}
}

adaptive-classifier/llm-hallucination-detector