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NeuML
/
pubmedbert-base-colbert

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

Instructions to use NeuML/pubmedbert-base-colbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use NeuML/pubmedbert-base-colbert with sentence-transformers:

    from pylate import models
    
    queries = [
        "Which planet is known as the Red Planet?",
        "What is the largest planet in our solar system?",
    ]
    
    documents = [
        ["Mars is the Red Planet.", "Venus is Earth's twin."],
        ["Jupiter is the largest planet.", "Saturn has rings."],
    ]
    
    model = models.ColBERT(model_name_or_path="NeuML/pubmedbert-base-colbert")
    
    queries_emb = model.encode(queries, is_query=True)
    docs_emb = model.encode(documents, is_query=False)
  • Notebooks
  • Google Colab
  • Kaggle
pubmedbert-base-colbert
439 MB
Ctrl+K
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  • 1 contributor
History: 3 commits
davidmezzetti's picture
davidmezzetti
Update README.md
6726d68 verified 5 months ago
  • 1_Dense
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  • eval
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  • .gitattributes
    1.52 kB
    initial commit 8 months ago
  • README.md
    5.49 kB
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  • added_tokens.json
    37 Bytes
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  • config.json
    667 Bytes
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  • config_sentence_transformers.json
    665 Bytes
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  • model.safetensors
    438 MB
    xet
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  • modules.json
    216 Bytes
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  • sentence_bert_config.json
    54 Bytes
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  • special_tokens_map.json
    126 Bytes
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  • tokenizer.json
    707 kB
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  • tokenizer_config.json
    1.63 kB
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  • triplet_evaluation_results.csv
    254 Bytes
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  • vocab.txt
    226 kB
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