Transformers
PyTorch
TensorFlow
Arabic
t5
Arabic T5
MSA
Twitter
Arabic Dialect
Arabic Machine Translation
Arabic Text Summarization
Arabic News Title and Question Generation
Arabic Paraphrasing and Transliteration
Arabic Code-Switched Translation
text-generation-inference
Instructions to use UBC-NLP/AraT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/AraT5-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UBC-NLP/AraT5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5adf827048da5175ade86c0cc78737bb7b29fc2a64b906784fa4cd24522a892
- Size of remote file:
- 1.13 GB
- SHA256:
- 6836b6dac6f17ada3347f5cabbb123ae81df82928881fbb33a16ae8fe39d2eaf
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