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UMCU
/
mirrorbert_medroberta.nl_clstoken_sbert

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

Instructions to use UMCU/mirrorbert_medroberta.nl_clstoken_sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use UMCU/mirrorbert_medroberta.nl_clstoken_sbert with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("UMCU/mirrorbert_medroberta.nl_clstoken_sbert")
    
    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
mirrorbert_medroberta.nl_clstoken_sbert
509 MB
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  • 1 contributor
History: 2 commits
UMCU's picture
UMCU
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e6091ef verified 10 months ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit 10 months ago
  • README.md
    4.13 kB
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  • config.json
    727 Bytes
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  • config_sentence_transformers.json
    283 Bytes
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  • merges.txt
    539 kB
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  • model.safetensors
    504 MB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    56 Bytes
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  • special_tokens_map.json
    958 Bytes
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  • tokenizer.json
    3.82 MB
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  • tokenizer_config.json
    1.27 kB
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  • vocab.json
    893 kB
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