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xtie
/
ClinicalT5-PET-impression

Summarization
Transformers
PyTorch
English
t5
text2text-generation
medical
text-generation-inference
Model card Files Files and versions
xet
Community
2

Instructions to use xtie/ClinicalT5-PET-impression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use xtie/ClinicalT5-PET-impression with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "summarization" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("summarization", model="xtie/ClinicalT5-PET-impression")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("xtie/ClinicalT5-PET-impression")
    model = AutoModelForSeq2SeqLM.from_pretrained("xtie/ClinicalT5-PET-impression")
  • Notebooks
  • Google Colab
  • Kaggle
ClinicalT5-PET-impression
2.95 GB
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  • 1 contributor
History: 4 commits
xtie's picture
xtie
Update README.md
510d6cd over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    5.44 kB
    Update README.md over 2 years ago
  • config.json
    821 Bytes
    Initial commit over 2 years ago
  • generation_config.json
    142 Bytes
    Initial commit over 2 years ago
  • pytorch_model.bin
    2.95 GB
    xet
    Initial commit over 2 years ago
  • special_tokens_map.json
    2.2 kB
    Initial commit over 2 years ago
  • spiece.model
    792 kB
    xet
    Initial commit over 2 years ago
  • tokenizer.json
    2.42 MB
    Initial commit over 2 years ago
  • tokenizer_config.json
    2.35 kB
    Initial commit over 2 years ago