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 spiece.model from UBC-NLP/AraT5-tweet-base: direct link, hf CLI and curl.
- Browser
- Download file 2.44 MB
-
https://huggingface.co/UBC-NLP/AraT5-tweet-base/resolve/main/spiece.model
- Command line
-
hf download hf://UBC-NLP/AraT5-tweet-base/spiece.model
-
curl -L -o spiece.model https://huggingface.co/UBC-NLP/AraT5-tweet-base/resolve/main/spiece.model
2.44 MB
- Xet hash:
- 3691eff3ff08581d0322106e5279f26c58e9fd5fa12b4ede81bceeff1e78c407
- Size of remote file:
- 2.44 MB
- SHA256:
- 5c7ae4407451bf02b459edec774d0539a06615005dd34d5c85c5c06765ff1606
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