| --- |
| 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: answerdotai/ModernBERT-base |
| - Number of Classes: 5 |
| - Total Examples: 25 |
| - Embedding Dimension: 768 |
|
|
| ## Class Distribution |
|
|
| ``` |
| contract: 5 examples (20.0%) |
| email: 5 examples (20.0%) |
| invoice: 5 examples (20.0%) |
| memo: 5 examples (20.0%) |
| report: 5 examples (20.0%) |
| ``` |
|
|
| ## 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: 13 |
| - 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 |
|
|
| ```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} |
| } |
| ``` |
|
|
