Update model card: set license to apache-2.0; add acceptance (nghiệm thu) model info & KPIs

README.md

@@ -18,13 +18,16 @@ tags:
   - cybersecurity
   - qdrant
   - secAI
+datasets:
+  - DuyTa/Cyber_F1_v2
+  - DuyTa/cve-kgrag-db
 ---
 
 # sec-rerank
 
 **This is a fine-tuned version of [Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B)** — a **listwise generative reranker** (second-stage ranker), not an embedding model.
 
-It was trained on grouped query–document examples whose **hard negatives were mined from a local Qdrant** collection (`cve_kb`) built from CVE investigation trajectories. It does not emit dense vectors; it reorders first-stage candidates (e.g. from [`DuyTa/sec-embedding`](https://huggingface.co/DuyTa/sec-embedding)).
+It was trained on grouped query–document examples whose **hard negatives were mined from a local Qdrant** collection (`cve_kb`) built from CVE investigation trajectories. It does not emit dense vectors; it reorders first-stage candidates (e.g. from [`DuyTa/sec-embedding`](https://huggingface.co/DuyTa/sec-embedding)). It is the reranker component of the **secAI** stack, paired with [`DuyTa/Cyber-F1-AWQ`](https://huggingface.co/DuyTa/Cyber-F1-AWQ).
 
 ## Training
 
@@ -58,6 +61,21 @@ Positives and negatives are real CVE core-chunk text from local Qdrant `cve_kb`
 
 Split: 33.6k train / 4.2k validation grouped examples.
 
+### Training corpus
+
+Built from **five years of authoritative cybersecurity sources**: NVD (173,473 CVEs), MITRE CWE (768 weakness types, mapped to ~92% of CVEs), CAPEC/ATT&CK (443/174 entries) and Exploit-DB (3,139 exploits, 2021–2026). Public datasets: [`DuyTa/Cyber_F1_v2`](https://huggingface.co/datasets/DuyTa/Cyber_F1_v2), [`DuyTa/cve-kgrag-db`](https://huggingface.co/datasets/DuyTa/cve-kgrag-db).
+
+## Acceptance (nghiệm thu) — reported KPIs
+
+Measured on **NVIDIA A100 80GB** in the full production chatflow (Hybrid Search → Rerank → LLM), on a 1,000-sample security test set (40% CVE identification/classification, 40% remediation advice, 20% real-world scenario reasoning):
+
+| Metric | Result | Target | Pass |
+|---|---|---|---|
+| Ranking quality — **Precision@1** | **98.23%** | > 90% | ✅ |
+| Throughput | **4,862 docs/s** (concurrency 128) | ≥ 180 rerank/s | ✅ |
+
+Raw per-sample logs (`reranking-precision-at-1.jsonl`) and evaluation code are delivered with the acceptance package.
+
 ## Inference
 
 Score each `(query, document)` with the native Qwen3-Reranker generative yes/no head. Use only to **rerank** a short candidate list from dense / hybrid retrieval.
@@ -74,4 +92,4 @@ Score each `(query, document)` with the native Qwen3-Reranker generative yes/no
 
 ## Attribution & license
 
-Derived from [Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) (**Apache-2.0**). Credit for the base reranker belongs to the Qwen team.
+Released under **Apache-2.0**. Derived from [Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) (**Apache-2.0**); credit for the base reranker belongs to the Qwen team.