Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use ddobokki/klue-roberta-base-nli-sts-ko-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ddobokki/klue-roberta-base-nli-sts-ko-en with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ddobokki/klue-roberta-base-nli-sts-ko-en") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use ddobokki/klue-roberta-base-nli-sts-ko-en with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ddobokki/klue-roberta-base-nli-sts-ko-en") model = AutoModel.from_pretrained("ddobokki/klue-roberta-base-nli-sts-ko-en") - Notebooks
- Google Colab
- Kaggle
ํ๊ตญ์ด์ ์์ด์ nli, sts๋ฐ์ดํฐ๋ฅผ klue/roberta-base์ ํ์ต์ํจ ๋ชจ๋ธ์ ๋๋ค.
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
query = ['๊ทธ๋ ๊ทธ๋
๋ฅผ ์ข์ํ๋ค.']
sentences = ["he love her", "he hate her", '๊ทธ๋
๋ ๊ทธ๋ฅผ ์ซ์ดํ๋ค.','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)
- Downloads last month
- 15