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bozhou
/
DeBERTa-base

Image Feature Extraction
Transformers
PyTorch
deberta-v2
custom_code
Model card Files Files and versions
xet
Community

Instructions to use bozhou/DeBERTa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bozhou/DeBERTa-base with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-feature-extraction", model="bozhou/DeBERTa-base", trust_remote_code=True)
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("bozhou/DeBERTa-base", trust_remote_code=True)
    model = AutoModel.from_pretrained("bozhou/DeBERTa-base", trust_remote_code=True)
  • Notebooks
  • Google Colab
  • Kaggle
DeBERTa-base / modeling
84.4 kB
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  • 3 contributors
History: 5 commits
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3v324v23
update model name
e77f999 about 3 years ago
  • __init__.py
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  • bert.py
    13 kB
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  • cache_utils.py
    3.11 kB
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  • config.py
    8.52 kB
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  • da_utils.py
    1.32 kB
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  • deberta.py
    5.93 kB
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  • disentangled_attention.py
    11.6 kB
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  • file_utils.py
    8.02 kB
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  • gpt2_bpe_utils.py
    5.63 kB
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  • gpt2_tokenizer.py
    6.3 kB
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  • ops.py
    8.26 kB
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  • pooling.py
    2.84 kB
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  • spm_tokenizer.py
    9.58 kB
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  • tokenizers.py
    269 Bytes
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