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
- Xet hash:
- 7d883bddc0015cda7e223599d7dcd0b2b38f5733dd93dc8b1734f63e59997718
- Size of remote file:
- 3.06 kB
- SHA256:
- 0ee6f4c3d1abf409cf9201a637276f9612f6735d6c7890dcccce423bbb3e20c7
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