Update README.md

README.md

@@ -15,16 +15,24 @@ base_model:
 - answerdotai/ModernBERT-large
 ---
 
-# SecureModernBERT-NER
+# Model Overview
 
-SecureModernBERT-NER is a ModernBERT-base model fine-tuned to recognise named entities that appear in cyber-threat intelligence (CTI) narratives. It predicts BIO-formatted tags for 22 security-specific entity types (e.g., `MALWARE`, `THREAT-ACTOR`, `CVE`, `IPV4`, `URL`). The model is suitable for extracting indicators of compromise and contextual metadata from English-language threat reports, product advisories, and incident write-ups.
+**SecureModernBERT-NER** represents a new generation of cybersecurity-focused language models — combining the **state-of-the-art architecture of ModernBERT** with one of the **largest and most diverse CTI-labelled NER corpora ever built**.  
+
+Unlike conventional NER systems, SecureModernBERT-NER recognises **22 finely-grained, security-specific entity types**, covering the full spectrum of cyber-threat intelligence — from `THREAT-ACTOR` and `MALWARE` to `CVE`, `IPV4`, `DOMAIN`, and `REGISTRY-KEYS`.  
+
+Trained on more than **half a million manually curated spans** sourced from real-world threat reports, vulnerability advisories, and incident analyses, it achieves an exceptional balance of **accuracy, generalisation, and contextual depth**.  
+
+This model is designed to **parse complex security narratives with human-level precision**, extracting both contextual metadata (e.g., `ORG`, `PRODUCT`, `PLATFORM`) and highly technical indicators (e.g., `HASHES`, `URLS`, `NETWORK ADDRESSES`) — all within a single unified framework.  
+
+SecureModernBERT-NER sets a new standard for **automated CTI entity recognition**, enabling the next wave of **threat-intelligence automation, enrichment, and analytics**.  
 
 ## Quick Start
 
 ```python
 from transformers import pipeline
 
-model_id = "juanmcristobal/autotrain-sec4"
+model_id = "attack-vector/SecureModernBERT-NER"
 
 pipe = pipeline(
     task="token-classification",
@@ -155,21 +163,6 @@ These metrics were computed with the `seqeval` micro-average at the entity level
 
 The following tables report detailed results on a shared CTI validation set. **Do not compare the per-label values across models directly:** each checkpoint uses a different taxonomy or remapping strategy, so accuracy percentages can be misleading when labels are aligned or collapsed differently. Use the per-model tables to understand performance within a single schema, and interpret macro-accuracy scores with caution.
 
-### PranavaKailash/CyNER-2.0-DeBERTa-v3-base
-
-| Label | Used | Accuracy |
-|-------|------|----------|
-| Indicator | 35,936 | 0.7878 |
-| Location | 7,895 | 0.0113 |
-| Malware | 12,125 | 0.7800 |
-| O | 2,896 | 0.7652 |
-| Organization | 42,537 | 0.6556 |
-| System | 35,063 | 0.7259 |
-| TOOL | 4,820 | 0.0000 |
-| Threat Group | 9,522 | 0.0000 |
-| Vulnerability | 27,673 | 0.1876 |
-
-- **Macro accuracy:** 0.4348
 
 ### CyberPeace-Institute/SecureBERT-NER
 
@@ -193,7 +186,23 @@ The following tables report detailed results on a shared CTI validation set. **D
 | URL | 6,997 | 0.0795 |
 | VULID | 27,586 | 0.3849 |
 
-- **Macro accuracy:** not reported (schema differs substantially from the others).
+- **Macro accuracy:** 0.3820
+
+### PranavaKailash/CyNER-2.0-DeBERTa-v3-base
+
+| Label | Used | Accuracy |
+|-------|------|----------|
+| Indicator | 35,936 | 0.7878 |
+| Location | 7,895 | 0.0113 |
+| Malware | 12,125 | 0.7800 |
+| O | 2,896 | 0.7652 |
+| Organization | 42,537 | 0.6556 |
+| System | 35,063 | 0.7259 |
+| TOOL | 4,820 | 0.0000 |
+| Threat Group | 9,522 | 0.0000 |
+| Vulnerability | 27,673 | 0.1876 |
+
+- **Macro accuracy:** 0.4348
 
 ### cisco-ai/SecureBERT2.0-NER