Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:9334
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use HJUNN/klue_roberta-base-klue-sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HJUNN/klue_roberta-base-klue-sts with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HJUNN/klue_roberta-base-klue-sts") sentences = [ "니 생각엔 어떤 방법이 거실 청소를 할 때에 가장 효과적일 것 같아?", "한메일 서비스를 이용할 수 있는 기간은 언제까지 일까요?", "다음에 엘에이에 오면 또 머무를 계획입니다.", "너가 생각하기에 거실 청소하는데 가장 효과적인 방법은 뭐야?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
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| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |