README.md

---
license: mit
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
---

# sec-embedding

**This is a fine-tuned version of [BAAI/bge-m3](https://huggingface.co/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.

## 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.

## 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

Derived from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) (**MIT License**). Credit for the base model belongs to the BAAI authors.