Transformers
PyTorch
TensorFlow
Arabic
t5
text2text-generation
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-title-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UBC-NLP/AraT5-base-title-generation with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/AraT5-base-title-generation") model = AutoModelForSeq2SeqLM.from_pretrained("UBC-NLP/AraT5-base-title-generation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1010742ee4f1d9a78fa600c5ae07a0dbc8a9dbb70292fbd197a74d5b507ddb80
- Size of remote file:
- 1.13 GB
- SHA256:
- 16a70c7a7d1ca015bfc9589ff08aa58611c6dd358d6750c986cc92ff672ef558
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