Instructions to use acmc/summarizer_google_long-t5-tglobal-base_mesh_faceted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acmc/summarizer_google_long-t5-tglobal-base_mesh_faceted with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("acmc/summarizer_google_long-t5-tglobal-base_mesh_faceted") model = AutoModelForSeq2SeqLM.from_pretrained("acmc/summarizer_google_long-t5-tglobal-base_mesh_faceted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6086bb9f40fef0443806e29474f7efbf9e5cf383267573434c10c7725d83a7e
- Size of remote file:
- 4.28 kB
- SHA256:
- fdf845ccfcb772f706e05355afd4ba8a86d255da83b254aabf000e6c364b8022
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.