Instructions to use debbiesoon/summarise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debbiesoon/summarise with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("debbiesoon/summarise") model = AutoModelForSeq2SeqLM.from_pretrained("debbiesoon/summarise", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b1c64cb925d1880cf41e66e130dc646fdea2dc3f7c052e731731eeb3b704007
- Size of remote file:
- 3.44 kB
- SHA256:
- 1b449b6049d9bc48aa1917f53688b0255254a95f1a2b493bdd9c05b08bf1d534
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.