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kalpeshk2011
/
rankgen-t5-base-all

Feature Extraction
Transformers
PyTorch
English
t5
contrastive learning
ranking
decoding
metric learning
text generation
retrieval
custom_code
Model card Files Files and versions
xet
Community
2

Instructions to use kalpeshk2011/rankgen-t5-base-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use kalpeshk2011/rankgen-t5-base-all with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True)
    model = AutoModel.from_pretrained("kalpeshk2011/rankgen-t5-base-all", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Update model metadata to set pipeline tag to the new `text-ranking`

#2 opened about 1 year ago by
tomaarsen

Adding `safetensors` variant of this model

#1 opened over 1 year ago by
SFconvertbot
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