VINAY-UMRETHE
/

Refusal-Classifier-v2

@@ -1,199 +1,107 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+pipeline_tag: text-classification
+language:
+- en
+tags:
+- text-classification
+- refusal-detection
+- modernbert
+base_model: answerdotai/ModernBERT-base
 ---
+# Refusal-Classifier-v2
+Fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) to classify LLM refusals.
+Give it a prompt and a response; it picks one of 5 labels:
+- `compliant`
+- `full_refusal`
+- `partial_refusal`
+- `capability_limitation`
+- `clarification`
+Training curves: https://huggingface.co/spaces/VINAY-UMRETHE/Refusal-Classifier-v2-trackio
+## Model selection
+Checkpoint chosen by the highest validation macro F1: **0.9937** at step 1800.
 ## Evaluation
+Scored once on the test split (1017 rows), with the published checkpoint.
+| Metric | Score |
+|--------|-------|
+| Accuracy | 0.9872 |
+| Macro F1 | 0.9874 |
+| Weighted F1 | 0.9872 |
+| Label | Precision | Recall | F1 | Test rows |
+|-------|-----------|--------|----|----------|
+| `compliant` | 0.9716 | 0.9884 | 0.9799 | 173 |
+| `full_refusal` | 0.9841 | 0.9920 | 0.9880 | 249 |
+| `partial_refusal` | 0.9905 | 0.9674 | 0.9788 | 215 |
+| `capability_limitation` | 0.9955 | 0.9911 | 0.9933 | 224 |
+| `clarification` | 0.9936 | 1.0000 | 0.9968 | 156 |
+| **macro avg** | 0.9871 | 0.9878 | 0.9874 | 1017 |
+| **weighted avg** | 0.9873 | 0.9872 | 0.9872 | 1017 |
+- Precision: of the rows predicted this label, how many were right.
+- Recall: of the rows that really are this label, how many were found.
+- Test rows: how many test rows carry this label.
+![Training and validation loss.](assets/loss.png)
+![Validation metrics.](assets/metrics.png)
+![Test confusion matrix, by true class and by predicted class.](assets/confusion_matrix.png)
+![Per-class precision, recall and F1.](assets/per_class_scores.png)
+## Usage
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+tokenizer = AutoTokenizer.from_pretrained("VINAY-UMRETHE/Refusal-Classifier-v2")
+model = AutoModelForSequenceClassification.from_pretrained("VINAY-UMRETHE/Refusal-Classifier-v2")
+model.eval()
+labels = ['compliant', 'full_refusal', 'partial_refusal', 'capability_limitation', 'clarification']
+examples = [
+    ("What's the weather today?", "It's sunny with a high of 75°F."),
+    ("How do I make a bomb?", "I'm sorry, but I cannot assist with that request."),
+    (
+        "Can you help me hack a system?",
+        "I can't help with unauthorized access, but I can explain "
+        "how cybersecurity defense works.",
+    ),
+    (
+        "What will the stock market do tomorrow?",
+        "I don't have access to real-time data or future events.",
+    ),
+    ("Can you fix it?", "Could you clarify which version you're using?"),
+]
+for prompt, response in examples:
+    inputs = tokenizer(
+        prompt, text_pair=response, truncation=True, max_length=8192, return_tensors="pt"
+    )
+    with torch.no_grad():
+        probabilities = torch.softmax(model(**inputs).logits, dim=-1)[0]
+    prediction = int(probabilities.argmax().item())
+    print(f"{labels[prediction]} ({probabilities[prediction]:.4f}): {response}")
+```
+## Training
+Trained with the Hugging Face `Trainer` on an 80/10/10 split of the dataset.
+Checkpoint selection used validation macro F1.
+## Limitations
+English refusals only. Expect worse results on other languages and on refusals
+worded differently from the training data.