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OpenMatch
/
Web-Graph-Embedding

Feature Extraction
Transformers
PyTorch
t5
Model card Files Files and versions
xet
Community
1

Instructions to use OpenMatch/Web-Graph-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use OpenMatch/Web-Graph-Embedding with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="OpenMatch/Web-Graph-Embedding")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("OpenMatch/Web-Graph-Embedding")
    model = AutoModelForMultimodalLM.from_pretrained("OpenMatch/Web-Graph-Embedding")
  • Notebooks
  • Google Colab
  • Kaggle
Web-Graph-Embedding
1.81 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
SFconvertbot's picture
SFconvertbot
Adding `safetensors` variant of this model
9c68ad1 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    418 Bytes
    Update README.md over 2 years ago
  • config.json
    733 Bytes
    Upload 8 files over 2 years ago
  • dict.txt
    385 kB
    Upload 8 files over 2 years ago
  • model.safetensors
    903 MB
    xet
    Adding `safetensors` variant of this model over 1 year ago
  • openmatch_config.json
    189 Bytes
    Upload 8 files over 2 years ago
  • pytorch_model.bin
    903 MB
    xet
    Upload 8 files over 2 years ago
  • sp.model
    825 kB
    xet
    Upload 8 files over 2 years ago
  • special_tokens_map.json
    141 Bytes
    Upload 8 files over 2 years ago
  • tokenizer_config.json
    421 Bytes
    Upload 8 files over 2 years ago
  • training_args.bin
    4.03 kB
    xet
    Upload 8 files over 2 years ago