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swardiantara
/
MultiSource-full-cdk3-m0.5-e5-b8-base

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use swardiantara/MultiSource-full-cdk3-m0.5-e5-b8-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use swardiantara/MultiSource-full-cdk3-m0.5-e5-b8-base with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("swardiantara/MultiSource-full-cdk3-m0.5-e5-b8-base")
    
    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
MultiSource-full-cdk3-m0.5-e5-b8-base
638 MB
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  • 1 contributor
History: 2 commits
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swardiantara
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  • 1_Pooling
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  • .gitattributes
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  • README.md
    2.51 kB
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  • config.json
    613 Bytes
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  • config_sentence_transformers.json
    164 Bytes
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  • distance_k3_positive_pairs.json
    508 kB
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  • distance_k3_selected_sample.xlsx
    164 kB
    xet
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  • model.safetensors
    438 MB
    xet
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  • modules.json
    349 Bytes
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  • negative_pairs.json
    198 MB
    xet
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  • running_time.json
    559 Bytes
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  • sentence_bert_config.json
    53 Bytes
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    711 kB
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  • tokenizer_config.json
    1.58 kB
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  • vocab.txt
    232 kB
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