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microsoft
/
ssr-base

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

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

  • Libraries
  • Transformers

    How to use microsoft/ssr-base 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="microsoft/ssr-base")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("microsoft/ssr-base")
    model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/ssr-base", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • SSR-base

SSR-base

SSR-base model as in EMNLP 2021 paper Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting.

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Dataset used to train microsoft/ssr-base

legacy-datasets/c4

Updated Mar 5, 2024 โ€ข 10.1k โ€ข 250

Paper for microsoft/ssr-base

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

Paper โ€ข 2101.00416 โ€ข Published Jan 2, 2021
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