license: apache-2.0
base_model: BAAI/bge-m3
base_model_relation: finetune
library_name: sentence-transformers
pipeline_tag: feature-extraction
language:
- en
- vi
pretty_name: sec-embedding (fine-tuned BGE-M3)
tags:
- sentence-transformers
- feature-extraction
- embedding
- dense-retrieval
- contrastive-learning
- cve
- cybersecurity
- qdrant
- secAI
datasets:
- DuyTa/Cyber_F1_v2
- DuyTa/cve-kgrag-db
sec-embedding
This is a fine-tuned version of BAAI/bge-m3 for CVE / cybersecurity dense retrieval.
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
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.