Instructions to use Amdalotaibi/AraBART-traffics-summarization-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amdalotaibi/AraBART-traffics-summarization-2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Amdalotaibi/AraBART-traffics-summarization-2") model = AutoModelForSeq2SeqLM.from_pretrained("Amdalotaibi/AraBART-traffics-summarization-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f4a2611887e3bd1f935ebee1273bcc02612072a590cb074f8fa2e5a0ce9055e
- Size of remote file:
- 5.43 kB
- SHA256:
- d364bcdef92a8d92dfee4a986e9518e5931245f68754413fdd8b1942c8c6a824
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.