File size: 1,610 Bytes
48b24d3 4b9c532 48b24d3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
---
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/distilbert-base-cased
- Number of Classes: 2
- Total Examples: 616
- Embedding Dimension: 768
## Class Distribution
```
HIGH: 308 examples (50.0%)
LOW: 308 examples (50.0%)
```
## Usage
```python
from adaptive_classifier import AdaptiveClassifier
# Load the model
classifier = AdaptiveClassifier.from_pretrained("adaptive-classifier/llm-router")
# 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: 20
- 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 500 examples per class
- Updates prototypes every 50 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}
}
```
|