Revert README to honest re-host description (no unverified fine-tune claim)

README.md

@@ -1,64 +1,21 @@
 ---
 license: mit
 base_model: BAAI/bge-m3
-base_model_relation: finetune
-library_name: sentence-transformers
-pipeline_tag: feature-extraction
-language:
-  - en
-  - vi
 tags:
   - sentence-transformers
   - feature-extraction
   - embedding
-  - dense-retrieval
-  - contrastive-learning
-  - cve
-  - cybersecurity
-  - qdrant
   - secAI
 ---
 
 # sec-embedding
 
-Domain-adapted **dense retriever** for CVE / cybersecurity search. Fine-tuned from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) (MIT) on a CVE investigation-trajectory dataset with **hard-negative mining** against a local Qdrant collection (`cve_kb`, NVD/MITRE core chunks).
+This is a direct re-host of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) under the
+`secAI` project namespace, served as the embedding model in the secAI retrieval pipeline
+(via vLLM, pooling runner). Weights are unmodified from the base model.
 
-This is **not** a verbatim re-host of the base checkpoint. It is a contrastive LoRA fine-tune of bge-m3 for first-stage retrieval over the secAI CVE knowledge base.
+- **Base model:** BAAI/bge-m3 (MIT license)
+- **Usage:** must stay bge-m3 — the secAI knowledge-base index is built against its
+  embedding space; swapping models drops recall to ~0.
 
-## Training
-
-Recipe follows the secAI Colab trainer (`notebooks/BGE_M3_Colab.ipynb`):
-
-| | |
-|---|---|
-| Base | `BAAI/bge-m3` via Unsloth `FastSentenceTransformer` (`unsloth/bge-m3`) |
-| Architecture | Bi-encoder / dense embedding model (1024-d, same as bge-m3) |
-| Adapter | LoRA, `r=32`, target modules `key`, `query`, `value`, `dense` |
-| Loss | `CachedMultipleNegativesRankingLoss` (InfoNCE with in-batch negatives) |
-| Engine | `sentence-transformers` `SentenceTransformerTrainer` |
-| Max sequence length | 1024 |
-| Learning rate | 2e-5, bf16 |
-
-Each training example is a `(query, positive)` pair:
-
-- **Query** — a CVE investigation trajectory (Vietnamese or English): follow-up questions over CVE-ID, CWE, product, severity, year, CAPEC / ATT&CK, drawn from real KB metadata.
-- **Positive** — the matching CVE passage scrolled from the local Qdrant `cve_kb` collection.
-- **Hard negatives** — other CVE documents in the same mini-batch, all mined from that Qdrant index. In a homogeneous CVE corpus those in-batch negatives are near-misses (similar wording, wrong CVE), which is what Cached MNRL is trained to push apart.
-
-Source field on the dataset: `Qdrant cve_kb (NVD/MITRE)`. Full split is 40k train / 5k validation query–passage pairs.
-
-## Usage
-
-Drop-in replacement for bge-m3 in sentence-transformers. Rebuild the Qdrant index with **this** checkpoint; mixing it with vanilla bge-m3 vectors collapses recall.
-
-```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)
-```
-
-## Attribution & license
-
-Weights derive from [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3), released under the **MIT License**. All credit for the base model belongs to the BAAI authors. This card and the CVE/Qdrant fine-tune are part of the secAI retrieval stack.
+All credit for the underlying model goes to the original BAAI/bge-m3 authors.