Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:574458
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use KYUNGHYUN9/ko-sroberta-ggd-prototype with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KYUNGHYUN9/ko-sroberta-ggd-prototype with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KYUNGHYUN9/ko-sroberta-ggd-prototype") sentences = [ "왜 토마스의 책은 캐논에서 제외되었는가?", "나토는 북한의 핵실험이 세계 평화에 중대한 위협이라고 말한다", "왜 더 많은 예수의 말을 캐논에서 제외시키는가?", "마이크로소프트는 올해 초 개발자인 커넥틱스로부터 가상 PC를 인수했다." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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