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zbum
/
klue-roberta-base-klue-sts

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:10501
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use zbum/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 zbum/klue-roberta-base-klue-sts with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("zbum/klue-roberta-base-klue-sts")
    
    sentences = [
        "노르웨이는 2025년, 덴마크와 네덜란드 2030년, 영국 2035년, 프랑스 2040년 등 이미 해외 각국에서는 내연차 판매·등록금지를 선언한 바 있다.",
        "지난 2011년에 도입된 ‘공공부문 온실가스 목표관리제’는 올해까지 기준배출량(2007~2009년 평균 배출량) 대비 30% 감축목표를 설정한 바 있다.",
        "잘 때 선풍기 시간 조절 어떻게 해?",
        "시중의 마스크 부족 원인이 복지부와 식약처의 마스크 주문 취소 때문이다?"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
klue-roberta-base-klue-sts
444 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
zbum's picture
zbum
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03f3bd7 verified about 1 year ago
  • 1_Pooling
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  • eval
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  • .gitattributes
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  • README.md
    15.5 kB
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  • config.json
    704 Bytes
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  • config_sentence_transformers.json
    277 Bytes
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  • model.safetensors
    442 MB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    57 Bytes
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  • special_tokens_map.json
    971 Bytes
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  • tokenizer.json
    752 kB
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  • tokenizer_config.json
    1.31 kB
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  • vocab.txt
    248 kB
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