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:
- f2915c1510ddec8c0b8e32c32223b2ebf9acc17d8583394bd7f9c29067fc64f3
- Size of remote file:
- 2.28 GB
- SHA256:
- bd5d5a4c9ecb515130257649f05913daab0e9eb15484c7d3c9cfa77f43ec6d6c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.