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NeuML
/
colbert-bert-tiny

Sentence Similarity
sentence-transformers
Safetensors
English
bert
ColBERT
multi-vector
feature-extraction
Generated from Trainer
dataset_size:497901
loss:Contrastive
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • sentence-transformers

    How to use NeuML/colbert-bert-tiny with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("NeuML/colbert-bert-tiny")
    
    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]
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
colbert-bert-tiny
18.6 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 4 commits
davidmezzetti's picture
davidmezzetti
tomaarsen's picture
tomaarsen HF Staff
Add Sentence Transformers usage (#1)
5967f70 3 days ago
  • .gitattributes
    1.52 kB
    initial commit about 1 year ago
  • README.md
    1.91 kB
    Add Sentence Transformers usage (#1) 3 days ago
  • added_tokens.json
    37 Bytes
    Add model about 1 year ago
  • artifact.metadata
    912 Bytes
    Update config 12 months ago
  • config.json
    637 Bytes
    Add model about 1 year ago
  • model.safetensors
    17.6 MB
    xet
    Add model about 1 year ago
  • special_tokens_map.json
    126 Bytes
    Add model about 1 year ago
  • tokenizer.json
    712 kB
    Add model about 1 year ago
  • tokenizer_config.json
    1.64 kB
    Add model about 1 year ago
  • vocab.txt
    232 kB
    Add model about 1 year ago