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KYUNGHYUN9
/
ko-sroberta-ggd-prototype

Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
Generated from Trainer
dataset_size:574458
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

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
ko-sroberta-ggd-prototype
444 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
KYUNGHYUN9's picture
KYUNGHYUN9
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  • 1_Pooling
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  • .gitattributes
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  • README.md
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  • config.json
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  • config_sentence_transformers.json
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  • model.safetensors
    442 MB
    xet
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  • modules.json
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  • sentence_bert_config.json
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  • similarity_evaluation_sts-test_results.csv
    299 Bytes
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • vocab.txt
    248 kB
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