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-msa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/AraT5-msa-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UBC-NLP/AraT5-msa-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab498597297262535e4d9b3ad7a5a05c5d3958d9cd5488b8aec2b8f461bbec19
- Size of remote file:
- 1.13 GB
- SHA256:
- a9f78acdf23ea08cddde9925d6065cb8a80d8d742e9a4c7da210535c1110a551
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