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heack
/
HeackMT5-ZhSum100k

Summarization
Transformers
PyTorch
Chinese
mt5
text2text-generation
mT5
Model card Files Files and versions
xet
Community
5

Instructions to use heack/HeackMT5-ZhSum100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use heack/HeackMT5-ZhSum100k 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="heack/HeackMT5-ZhSum100k")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("heack/HeackMT5-ZhSum100k")
    model = AutoModelForSeq2SeqLM.from_pretrained("heack/HeackMT5-ZhSum100k")
  • Notebooks
  • Google Colab
  • Kaggle
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Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Adding `safetensors` variant of this model

#5 opened over 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#4 opened over 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#3 opened over 1 year ago by
SFconvertbot

Adding `safetensors` variant of this model

#2 opened over 2 years ago by
SFconvertbot

Adding `safetensors` variant of this model

#1 opened almost 3 years ago by
SFconvertbot
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