NGA-KR/ko-embed-cls

Korean text embedding model (568M) fine-tuned from BAAI/bge-m3 for topic classification.

Training data

• KLUE-TC (ynat) train split only (45,678 news titles, 7 topics), label-aware contrastive pairs (same-topic titles as positives, other-topic titles as hard negatives).
• No evaluation splits were used. Declared as KLUE-TC in the MTEB model metadata; base-model training data is inherited from bge-m3.

Evaluation (MTEB(kor, v2))

Task	Score
KLUE-TC (Classification, accuracy)	84.39

Training

CachedMultipleNegativesRankingLoss · batch 512 · lr 2e-5 · 1 epoch · max_len 128 · CLS pooling · bf16

Usage

from sentence_transformers import SentenceTransformer
m = SentenceTransformer("NGA-KR/ko-embed-cls")
e = m.encode(["삼성전자, 2분기 영업이익 10조 돌파", "손흥민 시즌 20호골"])

License

MIT (same as BAAI/bge-m3).