Instructions to use uclanlp/plbart-single_task-static-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uclanlp/plbart-single_task-static-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-single_task-static-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("uclanlp/plbart-single_task-static-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8af079d918e2672eaaef17983ee0a7125f62dbcab97c6007ffb0b62f95f02768
- Size of remote file:
- 557 MB
- SHA256:
- c3cc83ef155b00dbd6b4c24b3c7fac4968aae5553871838af4d83e0536adddae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.