한국어와 영어의 nli, sts데이터를 klue/roberta-base에 학습시킨 모델입니다.

```python
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
query = ['그는 그녀를 좋아한다.']
sentences = ["he love her", "he hate her", '그녀는 그를 싫어한다.','attention is all you need']

emb1 = model.encode(query)
emb2 = model.encode(sentences)
print(cosine_similarity(emb1,emb2))
-> array([[0.62751913, 0.23996451, 0.30788696, 0.08123618]], dtype=float32)
```


{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
sentence-transformers pytorch roberta feature-extraction sentence-similarity transformers endpoints_compatible region:us
TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 

한국어와 영어의 nli, sts데이터를 klue/roberta-base에 학습시킨 모델입니다.




TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 

TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us 
