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:
- e2d842ef51a24ec0d6ed09474316bd0c18af7b1394415c34e2e8c4c890c19860
- Size of remote file:
- 16.3 MB
- SHA256:
- 06c9c6286622394893280b896136dc020196c539ca8ea9bc40ca646354e9c684
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