Instructions to use JulianS/bart-base-finetuned-summscreen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JulianS/bart-base-finetuned-summscreen with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JulianS/bart-base-finetuned-summscreen") model = AutoModelForSeq2SeqLM.from_pretrained("JulianS/bart-base-finetuned-summscreen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JulianS/bart-base-finetuned-summscreen: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/JulianS/bart-base-finetuned-summscreen/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JulianS/bart-base-finetuned-summscreen/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JulianS/bart-base-finetuned-summscreen/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- ef2eac1287df44e904966fba354d0f4e5213a5b4bfc7f94b0714236ff7d647ec
- Size of remote file:
- 558 MB
- SHA256:
- 67d1013edb9e63fce9eb63e5660dfb4d62e1adde541996e682af394f72e18f5b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.