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hyunseop
/
bge-m3-ko

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
korean
embedding
contrastive-learning
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use hyunseop/bge-m3-ko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use hyunseop/bge-m3-ko with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("hyunseop/bge-m3-ko")
    
    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]
  • Notebooks
  • Google Colab
  • Kaggle
bge-m3-ko
1.15 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 12 commits
hyunseop's picture
hyunseop
docs: document training recipe (Nemotron-Personas-Korea, contrastive MNR, hyperparams)
f2198fe verified 2 months ago
  • 1_Pooling
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  • benchmark_results
    Add MIRACL benchmark details 4 months ago
  • .gitattributes
    1.57 kB
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  • README.md
    2.97 kB
    docs: document training recipe (Nemotron-Personas-Korea, contrastive MNR, hyperparams) 2 months ago
  • benchmark_results.json
    1.72 kB
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  • config.json
    744 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • eval_results_bge.json
    253 Bytes
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  • model.safetensors
    1.14 GB
    xet
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  • modules.json
    429 Bytes
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  • sentence_bert_config.json
    241 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    600 Bytes
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