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pinecone
/
ConstBERT

Feature Extraction
Transformers
Safetensors
English
bert
retrieval
constbert
colbert
multi-vector
embedding
custom_code
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use pinecone/ConstBERT with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="pinecone/ConstBERT", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("pinecone/ConstBERT", trust_remote_code=True)
    model = AutoModel.from_pretrained("pinecone/ConstBERT", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
ConstBERT
439 MB
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  • 1 contributor
History: 19 commits
caesar-one's picture
caesar-one
Update README.md
6605b00 verified 11 months ago
  • .gitattributes
    1.52 kB
    initial commit 12 months ago
  • README.md
    4.23 kB
    Update README.md 11 months ago
  • artifact.metadata
    1.54 kB
    Upload 4 files 12 months ago
  • colbert_configuration.py
    12 kB
    Upload ConstBERT 12 months ago
  • config.json
    710 Bytes
    Update config.json 12 months ago
  • model.safetensors
    438 MB
    xet
    Upload ConstBERT 12 months ago
  • modeling.py
    9.87 kB
    Upload ConstBERT 12 months ago
  • special_tokens_map.json
    695 Bytes
    Upload tokenizer 12 months ago
  • tokenization_utils.py
    7.65 kB
    Upload ConstBERT 12 months ago
  • tokenizer.json
    711 kB
    Upload tokenizer 12 months ago
  • tokenizer_config.json
    1.22 kB
    Upload tokenizer 12 months ago
  • vocab.txt
    232 kB
    Upload tokenizer 12 months ago