Instructions to use dnrso/koBART_Sum_Review_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnrso/koBART_Sum_Review_finetuning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dnrso/koBART_Sum_Review_finetuning") model = AutoModelForSeq2SeqLM.from_pretrained("dnrso/koBART_Sum_Review_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dnrso/koBART_Sum_Review_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/dnrso/koBART_Sum_Review_finetuning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dnrso/koBART_Sum_Review_finetuning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dnrso/koBART_Sum_Review_finetuning/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- ba508a53575648f679a3c796966d066d0f7777db87557e8a51e657a49017636f
- Size of remote file:
- 496 MB
- SHA256:
- 3579c839462f79f883bcc932ab8fc140fbaac45c19ae22b8fcd32b2756fa4841
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.