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wooseoko
/
clip-roberta-finetuned_GQA

Feature Extraction
Transformers
PyTorch
vision-text-dual-encoder
Generated from Trainer
Model card Files Files and versions
xet
Community

Instructions to use wooseoko/clip-roberta-finetuned_GQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use wooseoko/clip-roberta-finetuned_GQA with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="wooseoko/clip-roberta-finetuned_GQA")
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("wooseoko/clip-roberta-finetuned_GQA")
    model = AutoModel.from_pretrained("wooseoko/clip-roberta-finetuned_GQA")
  • Notebooks
  • Google Colab
  • Kaggle
clip-roberta-finetuned_GQA
852 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 34 commits
wooseoko's picture
wooseoko
update model card README.md
ef6c1d7 about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, step 500 about 3 years ago
  • README.md
    1.14 kB
    update model card README.md about 3 years ago
  • all_results.json
    309 Bytes
    End of training about 3 years ago
  • config.json
    4.66 kB
    Training in progress, step 500 about 3 years ago
  • eval_results.json
    162 Bytes
    End of training about 3 years ago
  • pytorch_model.bin
    852 MB
    xet
    Model save about 3 years ago
  • train_results.json
    167 Bytes
    End of training about 3 years ago
  • trainer_state.json
    8.53 kB
    End of training about 3 years ago
  • training_args.bin
    3.5 kB
    xet
    Training in progress, step 500 about 3 years ago