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README.md

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+---
+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: bert-base-uncased
+- Number of Classes: 3
+- Total Examples: 3
+- Embedding Dimension: 768
+
+## Class Distribution
+
+```
+negative: 1 examples (33.3%)
+neutral: 1 examples (33.3%)
+positive: 1 examples (33.3%)
+```
+
+## 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: 1
+- 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}
+}
+```

config.json

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+{
+  "config": {
+    "batch_size": 32,
+    "cost_coefficients": {},
+    "cost_function_type": "separable",
+    "device_map": "auto",
+    "early_stopping_patience": 3,
+    "enable_strategic_mode": false,
+    "epochs": 10,
+    "ewc_lambda": 100.0,
+    "gradient_checkpointing": false,
+    "learning_rate": 0.001,
+    "max_examples_per_class": 1000,
+    "max_length": 512,
+    "min_confidence": 0.1,
+    "min_examples_per_class": 3,
+    "neural_weight": 0.3,
+    "num_representative_examples": 5,
+    "prototype_update_frequency": 100,
+    "prototype_weight": 0.7,
+    "quantization": null,
+    "similarity_threshold": 0.6,
+    "strategic_blend_regular_weight": 0.6,
+    "strategic_blend_strategic_weight": 0.4,
+    "strategic_lambda": 0.1,
+    "strategic_prediction_head_weight": 0.5,
+    "strategic_prediction_proto_weight": 0.5,
+    "strategic_robust_head_weight": 0.2,
+    "strategic_robust_proto_weight": 0.8,
+    "strategic_training_frequency": 10,
+    "warmup_steps": 0
+  },
+  "embedding_dim": 768,
+  "id_to_label": {
+    "0": "neutral",
+    "1": "positive",
+    "2": "negative"
+  },
+  "label_to_id": {
+    "negative": 2,
+    "neutral": 0,
+    "positive": 1
+  },
+  "model_name": "bert-base-uncased",
+  "train_steps": 1
+}

examples.json

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+      "label": "positive",
+      "text": "The product works great!"
+    }
+  ]
+}

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