--- language: multilingual tags: - adaptive-classifier - text-classification - continuous-learning license: apache-2.0 --- # Adaptive Classifier This model is an instance of an [adaptive-classifier](https://github.com/codelion/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 ```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: 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 ```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} } ```