It was trained on a CVE investigation-trajectory dataset with hard-negative mining from a local Qdrant collection (cve_kb, NVD/MITRE core chunks). It is not a raw copy of the base checkpoint. It is the retriever component of the secAI stack, paired with DuyTa/sec-rerank and DuyTa/Cyber-F1-AWQ.

Training

From notebooks/BGE_M3_Colab.ipynb:


Base	`BAAI/bge-m3` via Unsloth `FastSentenceTransformer` (`unsloth/bge-m3`)
Role	Bi-encoder / dense retriever (1024-d, same geometry as bge-m3)
Adapter	LoRA, `r=32`, modules `key`, `query`, `value`, `dense`
Loss	`CachedMultipleNegativesRankingLoss` (InfoNCE, in-batch hard negatives)
Engine	`sentence-transformers` `SentenceTransformerTrainer`
Max sequence length	1024
Learning rate	2e-5, bf16

Each example is a (query, positive) pair:

Query — CVE investigation trajectory (Vietnamese or English) over CVE-ID, CWE, product, severity, year, CAPEC / ATT&CK, filled from real KB metadata.
Positive — matching CVE passage from local Qdrant cve_kb.
Hard negatives — other CVE documents in the same mini-batch, all mined from that Qdrant index (near-miss CVEs: similar wording, wrong ID).

Dataset source field: Qdrant cve_kb (NVD/MITRE). Split: 40k train / 5k validation.

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, DuyTa/cve-kgrag-db.

Training hardware: 2×A100 80GB.

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
Retrieval quality — Hit Rate@10	98.78%	> 96%	✅
Throughput	2,662 emb/s (concurrency 32)	≥ 1,200 emb/s	✅

Raw per-sample logs (embedding-hit-rate-at-10.jsonl) and evaluation code are delivered with the acceptance package.

Usage

python

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("DuyTa/sec-embedding")
query_emb = model.encode("CVE-2021-44228 impact on log4j", normalize_embeddings=True)
doc_emb = model.encode(passage, normalize_embeddings=True)

Rebuild the Qdrant index with this checkpoint. Mixing vectors with vanilla BAAI/bge-m3 drops recall.

Attribution & license

Released under Apache-2.0. Derived from BAAI/bge-m3 (MIT License); the MIT notice of the base model is retained and credit for the base weights belongs to the BAAI authors.