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sysresearch101
/
t5-large-finetuned-xsum

Summarization
Transformers
PyTorch
English
t5
text2text-generation
t5-large-summarization
pipeline:summarization
Eval Results (legacy)
text-generation-inference
Model card Files Files and versions
xet
Community
8

Instructions to use sysresearch101/t5-large-finetuned-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use sysresearch101/t5-large-finetuned-xsum 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="sysresearch101/t5-large-finetuned-xsum")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("sysresearch101/t5-large-finetuned-xsum")
    model = AutoModelForSeq2SeqLM.from_pretrained("sysresearch101/t5-large-finetuned-xsum")
  • Notebooks
  • Google Colab
  • Kaggle
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Adding `safetensors` variant of this model

#8 opened 9 months ago by
SFconvertbot

Adding `safetensors` variant of this model

#7 opened over 1 year ago by
SFconvertbot

Add evaluation results on the default config and test split of xsum

#6 opened over 2 years ago by
autoevaluator

Adding `safetensors` variant of this model

#5 opened about 3 years ago by
SFconvertbot

Add evaluation results on the 3.0.0 config of cnn_dailymail

#3 opened almost 4 years ago by
autoevaluator

Add evaluation results on the 3.0.0 config of cnn_dailymail

#2 opened almost 4 years ago by
autoevaluator
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