Sentence Similarity
sentence-transformers
Safetensors
Korean
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:744862
loss:ModifiedGISTEmbedLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use juyoungml/bge-m3-ko-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use juyoungml/bge-m3-ko-v1.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("juyoungml/bge-m3-ko-v1.1") sentences = [ "부산 중앙버스전용차로(BRT) 구간 횡단보도에서 처음으로 발생한 사고는 뭐지?", "지난 30일 오후 11시 10분쯤 부산 해운대구 우동 동백역 버스정류소 앞 도로에서 1차로를 달리던 A(75)씨의 SM5 승용차가 정류소 방향으로 횡단보도를 건너는 B(70·여)씨를 치었다.", "광원은 광감작제가 최대 광독성능을 나타내는 \\( 630 \\mathrm{~nm} \\) diode laser (Biolitec, Germany)를 사용하였다.", "중앙버스 정류장 및 버스전용차로 설치(31개 노선) " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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