NGA-KR/ko-embed-midm

Korean text embedding model (2.3B parameters, 1792 dimensions) fine-tuned from K-intelligence/Midm-2.0-Mini-Instruct, KT's Mi:dm 2.0 Mini.

This is an independent fine-tune by NGA-KR. It is not an official KT release.

Results

MTEB(kor, v2), 20 tasks, average of task-type means:

Mean	Classification	Clustering	PairClassification	Reranking	Retrieval	STS
70.00	62.73	62.26	69.62	58.93	70.85	81.69

MTEB(kor, v1) (6 tasks) mean: 70.03. Per-task scores are on the MTEB leaderboard.

Training data

Only public English datasets were used. No Korean training set and no benchmark test split was used.

Source	Rows
MS MARCO, Natural Questions, HotpotQA, GooAQ (retrieval)	200,000
SNLI, MultiNLI (anchor, entailment, contradiction triplets)	100,000
STS Benchmark train split (1,406 pairs with score ≥ 4.0 of 5, both directions)	2,812
Total	302,812

These datasets are declared as training_datasets in the MTEB model metadata, which gives 100% zero-shot on MTEB(kor, v1) and MTEB(kor, v2). Note that KorSTS and KorNLI are Korean translations of STS-B and SNLI/MultiNLI. This model used only the English train splits of those sources.

Usage

Requires sentence-transformers>=6.0.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("NGA-KR/ko-embed-midm")
q = model.encode(["전세 계약서에 기간을 안 썼으면 얼마 동안 유효해?"], prompt_name="query")
d = model.encode(["임대차 기간을 정하지 아니한 경우 그 기간을 2년으로 본다."])
print(model.similarity(q, d))

Queries take the instruction prompt stored in the model config (prompt_name="query"):

Instruct: Given a question, retrieve passages that answer the question
Query: 

Documents and symmetric inputs (STS, classification, clustering) are encoded without a prompt.

Training details

• Base: Midm-2.0-Mini-Instruct with the chat template removed
• Pooling: last token, L2-normalised
• Loss: CachedMultipleNegativesRankingLoss (in-batch negatives + 1 hard negative), temperature 0.02
• Full fine-tuning, batch 1024, lr 1e-5, cosine schedule, warmup 5%, 1 epoch, bf16 autocast
• Max sequence length: 256 in training, 512 at inference

License

MIT. The base model, Midm-2.0-Mini-Instruct, is released by KT Corporation under the MIT License (Copyright (c) 2025 KT Corporation). The full notice is in LICENSE.