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eric920609
/
20-multilingual-e5-large_fold_1

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:69363
loss:EpochLossWrapper
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use eric920609/20-multilingual-e5-large_fold_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use eric920609/20-multilingual-e5-large_fold_1 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("eric920609/20-multilingual-e5-large_fold_1")
    
    sentences = [
        "query: MSI 微星 MSI PRO MP165 E6 可攜式螢幕 (16型/FHD/Type C/喇叭/IPS)+SPACE LED觸控護眼三色螢幕掛燈 33cm款",
        "passage: 六合一清潔組【Apple】AirPods 4",
        "passage: 【MSI 微星】PRO MP251 E2 25型 平面商用螢幕(FHD/IPS/120Hz/DP+HDMI+D-Sub/眼部護理技術)",
        "passage: 【Apple】Apple Watch Series 11 GPS+行動網路 42mm(鋁金屬錶殼搭配運動型錶帶)"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
20-multilingual-e5-large_fold_1
2.26 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
eric920609's picture
eric920609
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1538fd3 verified 3 months ago
  • 1_Pooling
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  • .gitattributes
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  • README.md
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  • config.json
    657 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • model.safetensors
    2.24 GB
    xet
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  • modules.json
    349 Bytes
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  • sentence_bert_config.json
    57 Bytes
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.18 kB
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