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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("microsoft/ssr-base") model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/ssr-base") - Notebooks
- Google Colab
- Kaggle
Canwen Xu commited on
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README.md
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---
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language:
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- en
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datasets:
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- c4
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tags:
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- summarization
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- text2text-generation
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---
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# SSR-base
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SSR-base model as in EMNLP 2021 paper [Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting](https://arxiv.org/abs/2101.00416).
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