Sentence Similarity
sentence-transformers
Safetensors
Korean
xlm-roberta
embedding
nli
pair-classification
mteb
korean
text-embeddings-inference
Instructions to use NGA-KR/ko-embed-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NGA-KR/ko-embed-nli with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NGA-KR/ko-embed-nli") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
NGA-KR/ko-embed-nli
Korean text embedding model (568M) fine-tuned from BAAI/bge-m3 for sentence-pair classification (NLI / paraphrase).
Training data (train splits only)
- KLUE-NLI, KorNLI (MNLI + SNLI translations): premise → entailment hypothesis as positive, contradiction hypotheses as hard negatives
- PAWS-X (ko): sentence1 → paraphrase (label 1) as positive, non-paraphrase (label 0) of the same sentence1 as hard negatives
Declared as KLUE-NLI, KorNLI, PawsXPairClassification in the MTEB model metadata; base-model training data is inherited from bge-m3.
Evaluation (MTEB(kor, v2), PairClassification)
| Task | AP |
|---|---|
| KorNLI | 84.13 |
| KLUE-NLI | 77.74 |
| PawsXPairClassification | 60.10 |
| Average | 73.99 |
Training
CachedMultipleNegativesRankingLoss · batch 512 · lr 1.5e-5 · 2 epochs · max_len 128 · CLS pooling · bf16
License
MIT (same as BAAI/bge-m3).
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Model tree for NGA-KR/ko-embed-nli
Base model
BAAI/bge-m3