Instructions to use ncncomplete/t5-summarizer-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncncomplete/t5-summarizer-fast with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ncncomplete/t5-summarizer-fast") model = AutoModelForSeq2SeqLM.from_pretrained("ncncomplete/t5-summarizer-fast") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42c2536f0acc8b9b2141cb8c8718659e66040171f884924335636645da4e5b97
- Size of remote file:
- 5.33 kB
- SHA256:
- fac0b00b1acb393359d0a2a338fef24eb5654937e6af1619ea63747b78464426
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.