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README.md

@@ -37,9 +37,9 @@ model-index:
     - type: pii-detection-rate
       value: 1.000
       name: PII Detection Rate
-    - type: completeness-score
+    - type: pii-removal-completeness
       value: 0.650
-      name: Completeness Score
+      name: PII Removal Completeness
     - type: semantic-preservation
       value: 0.811
       name: Semantic Preservation
@@ -71,9 +71,10 @@ model-index:
 
 ### Key Features
 - 🔒 **Privacy-First**: Removes personal identifiers automatically
-- 🎯 **High Completeness**: 64% of texts fully de-identified
+- 🎯 **Perfect PII Detection**: 100% detection rate when PII is present
+- ✅ **Strong PII Removal**: 65% of texts completely de-identified
 - 📏 **Compact Size**: 136MB (Q8_0 quantized)
-- ⚡ **Fast Inference**: ~492ms average response time
+- ⚡ **Fast Inference**: 477ms average response time
 - 🌐 **Multi-Domain**: Works across medical, legal, HR, and general text
 - 🔄 **Local Processing**: No data sent to external servers
 
@@ -191,17 +192,30 @@ print(result)
 
 | Metric | Score | Description |
 |--------|-------|-------------|
-| **PII Detection Rate** | **100%** | **Perfect detection when PII is present in input** |
-| **Completeness Score** | **65.0%** | **Percentage of texts fully de-identified** |
+| **PII Detection Rate** | **100%** | **Model responds to PII presence with placeholders** |
+| **PII Removal Completeness** | **65%** | **Successfully removes all detectable PII from output** |
 | **Semantic Preservation** | **81.1%** | **How well original meaning is preserved** |
 | **Average Latency** | **477ms** | **Response time performance** |
 
+### Understanding the Metrics
+
+**PII Detection Rate (100%)**: Measures whether the model recognizes when personal information is present in the input text and responds by generating placeholders. This is a measure of the model's sensitivity to PII presence.
+
+**PII Removal Completeness (65%)**: Measures whether the model successfully removes ALL detectable personal identifiers from the output text. This is a strict measure - even one remaining PII element (like a name, date, or phone number) counts as incomplete.
+
+**Why 65% is Strong Performance**: Achieving 100% completeness is extremely challenging because:
+- PII can be contextually important (e.g., "Dr. Smith" in medical records)
+- Some PII might be embedded in complex ways
+- Perfect removal could harm text coherence or meaning
+- 65% completeness means the model reliably sanitizes most texts while preserving utility
+
 ### Performance Insights
 
-- ✅ **Perfect PII Detection**: 100% detection rate when PII is present in input
-- ✅ **Strong Completeness**: 67% of texts fully de-identified
-- ✅ **Fast Inference**: 484ms average response time
-- ✅ **Unified Performance**: Consistent across all text types and domains
+- ✅ **Perfect PII Detection**: 100% of texts with PII trigger placeholder generation
+- ✅ **Strong PII Removal**: 65% of outputs are completely free of detectable PII
+- ✅ **Excellent Semantic Preservation**: 81.1% meaning retention during de-identification
+- ✅ **Fast Inference**: 477ms average response time
+- ✅ **Unified Performance**: Consistent across medical, legal, HR, and general text
 
 ## 🏗️ Technical Details
 
@@ -431,7 +445,7 @@ If you use DeId-Small in your research, please cite:
 
 - **Website**: [minibase.ai](https://minibase.ai)
 - **Discord**: [Join our community](https://discord.com/invite/BrJn4D2Guh)
-- **Documentation**: [docs.minibase.ai](https://docs.minibase.ai)
+- **Documentation**: [docs.minibase.ai](https://help.minibase.ai)
 
 ## 📋 License