Instructions to use valurank/xsum_headline_generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use valurank/xsum_headline_generator 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="valurank/xsum_headline_generator")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("valurank/xsum_headline_generator") model = AutoModelForSeq2SeqLM.from_pretrained("valurank/xsum_headline_generator") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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tags:
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- generated_from_trainer
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model-index:
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- name: xsum_headline_generator_depreciated
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results: []
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license: other
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pipeline_tag: summarization
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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tags:
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- generated_from_trainer
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pipeline_tag: summarization
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base_model: google/pegasus-multi_news
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model-index:
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- name: xsum_headline_generator_depreciated
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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