Instructions to use tau/bart-base-sled-summscreenfd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tau/bart-base-sled-summscreenfd with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tau/bart-base-sled-summscreenfd", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bd9a120fe90b832e616d490ccd4775b228679324ad709459edcc1333cf236db
- Size of remote file:
- 558 MB
- SHA256:
- edcd7f1b5874f372124539f952b40bb797cf84de646eaff07743f231e495dc99
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.