Update model card with YAML frontmatter and two-stage pipeline example

README.md

@@ -157,14 +157,29 @@ probs = 1 / (1 + np.exp(-logits))  # Sigmoid for multi-label
 
 ### Two-Stage Pipeline
 
+For best results, use with the binary detector:
+
 ```python
-from nci.transformers.two_stage_pipeline import TwoStagePipeline
+from transformers import pipeline
+
+# Stage 1: Binary detection (fast filter)
+detector = pipeline("text-classification", model="synapti/nci-binary-detector")
+
+# Stage 2: Technique classification
+classifier = pipeline("text-classification", model="synapti/nci-technique-classifier", top_k=None)
 
-pipeline = TwoStagePipeline.from_pretrained()
-result = pipeline.analyze("Text to analyze...")
+text = "Your text to analyze..."
 
-print(f"Has propaganda: {result.has_propaganda}")
-print(f"Techniques: {[t.name for t in result.techniques]}")
+# Quick check first
+detection = detector(text)[0]
+if detection["label"] == "has_propaganda" and detection["score"] > 0.5:
+    # Detailed technique analysis
+    techniques = classifier(text)[0]
+    detected = [t for t in techniques if t["score"] > 0.3]
+    for t in detected:
+        print(f"{t['label']}: {t['score']:.2%}")
+else:
+    print("No propaganda detected")
 ```
 
 ## Calibration Config