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DIACDE
/
stjiris_tjgo_diacde_sts

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

Instructions to use DIACDE/stjiris_tjgo_diacde_sts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use DIACDE/stjiris_tjgo_diacde_sts with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("DIACDE/stjiris_tjgo_diacde_sts")
    
    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
stjiris_tjgo_diacde_sts
1.34 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 7 commits
DIACDE's picture
DIACDE
Update README.md
d90206a verified 2 months ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit 11 months ago
  • README.md
    18.6 kB
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  • config.json
    798 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • model.safetensors
    1.34 GB
    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
    695 Bytes
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  • tokenizer.json
    678 kB
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  • tokenizer_config.json
    1.47 kB
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  • vocab.txt
    210 kB
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