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--- |
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language: multilingual |
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tags: |
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- adaptive-classifier |
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- text-classification |
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- continuous-learning |
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license: apache-2.0 |
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--- |
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# Adaptive Classifier |
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This model is an instance of an [adaptive-classifier](https://github.com/codelion/adaptive-classifier) that allows for continuous learning and dynamic class addition. |
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You can install it with `pip install adaptive-classifier`. |
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## Model Details |
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- Base Model: distilbert-base-uncased |
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- Number of Classes: 4 |
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- Total Examples: 60 |
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- Embedding Dimension: 768 |
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## Class Distribution |
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``` |
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T0.0_P1.0_PP0.0_FP0.0: 18 examples (30.0%) |
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T0.7_P1.0_PP0.0_FP0.0: 22 examples (36.7%) |
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T1.0_P0.1_PP0.0_FP0.0: 1 examples (1.7%) |
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T1.0_P1.0_PP0.0_FP0.0: 19 examples (31.7%) |
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``` |
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## Usage |
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```python |
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from adaptive_classifier import AdaptiveClassifier |
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# Load the model |
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classifier = AdaptiveClassifier.from_pretrained("adaptive-classifier/model-name") |
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# Make predictions |
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text = "Your text here" |
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predictions = classifier.predict(text) |
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print(predictions) # List of (label, confidence) tuples |
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# Add new examples |
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texts = ["Example 1", "Example 2"] |
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labels = ["class1", "class2"] |
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classifier.add_examples(texts, labels) |
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``` |
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## Training Details |
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- Training Steps: 51 |
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- Examples per Class: See distribution above |
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- Prototype Memory: Active |
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- Neural Adaptation: Active |
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## Limitations |
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This model: |
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- Requires at least 3 examples per class |
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- Has a maximum of 1000 examples per class |
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- Updates prototypes every 100 examples |
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## Citation |
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```bibtex |
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@software{adaptive_classifier, |
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title = {Adaptive Classifier: Dynamic Text Classification with Continuous Learning}, |
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author = {Sharma, Asankhaya}, |
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year = {2025}, |
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publisher = {GitHub}, |
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url = {https://github.com/codelion/adaptive-classifier} |
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} |
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``` |
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