Instructions to use ahmed0189/mT5-Arabic-text-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmed0189/mT5-Arabic-text-summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ahmed0189/mT5-Arabic-text-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("ahmed0189/mT5-Arabic-text-summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d4162fb691a8bfdb5d69af790f8bde801a5d162c13d2fdffb98e678e8820a4b
- Size of remote file:
- 1.2 GB
- SHA256:
- 15d5d9b5e47e505e515aa8242dd80ca11364f2e297cbc12f3624169e6b595a5a
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