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bergum
/
product_title_encoder

Feature Extraction
Transformers
PyTorch
bert
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use bergum/product_title_encoder with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="bergum/product_title_encoder")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("bergum/product_title_encoder")
    model = AutoModel.from_pretrained("bergum/product_title_encoder")
  • Notebooks
  • Google Colab
  • Kaggle
product_title_encoder
91.8 MB
Ctrl+K
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  • 2 contributors
History: 7 commits
bergum's picture
bergum
Upload tokenizer
859265f over 3 years ago
  • .gitattributes
    1.48 kB
    initial commit over 3 years ago
  • config.json
    652 Bytes
    Upload model over 3 years ago
  • pytorch_model.bin
    90.9 MB
    xet
    Upload model over 3 years ago
  • special_tokens_map.json
    125 Bytes
    Upload tokenizer over 3 years ago
  • tokenizer.json
    712 kB
    Upload tokenizer over 3 years ago
  • tokenizer_config.json
    538 Bytes
    Upload tokenizer over 3 years ago
  • vocab.txt
    232 kB
    Upload tokenizer over 3 years ago