Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:574408
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use KYUNGHYUN9/ko-sroberta-itos-training-example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KYUNGHYUN9/ko-sroberta-itos-training-example with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KYUNGHYUN9/ko-sroberta-itos-training-example") sentences = [ "한 여성이 뜨거운 물 냄비에 음식을 넣고 있다.", "한 여성이 고기를 튀기고 있다.", "세계 브리핑 아시아 : 미얀마 : 치명적인 반 무슬림 폭력 사태가 폭발했다.", "아기가 잠들고 있다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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