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-tweet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/AraT5-tweet-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UBC-NLP/AraT5-tweet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UBC-NLP/AraT5-tweet-base: direct link, hf CLI and curl.
- Browser
- Download file 1.13 GB
-
https://huggingface.co/UBC-NLP/AraT5-tweet-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UBC-NLP/AraT5-tweet-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UBC-NLP/AraT5-tweet-base/resolve/main/pytorch_model.bin
1.13 GB
- Xet hash:
- 5785280ad818f635e5f75a49b650ca1c9699cdf102a65e35b7ec871400271999
- Size of remote file:
- 1.13 GB
- SHA256:
- 49703cb0c1019f10aa72115ec8dee3c48b08441ea7977fb85c8f64bf4951b426
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