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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cdc3f9016c934ab368c74f9b0cb5d99420cd636a0c40086adb6d9f61c5fecc1
- Size of remote file:
- 1.13 GB
- SHA256:
- 7267decadd09e2fafcab6f3d25edaa5bb7ca72b018635ac039b2422215af18a6
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