Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:574417
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use KYUNGHYUN9/ko-sroberta-itos-training-example_v0.02 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_v0.02 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KYUNGHYUN9/ko-sroberta-itos-training-example_v0.02") sentences = [ "이집트 대통령 선거에서 가까운 여론조사", "알 카에다 충돌, 폭발로 예멘에서 35명의 군인이 사망", "보도자료 : 예멘 대통령 선거", "반 파이프에 스케이트보드를 신은 남자" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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