Instructions to use bakrianoo/t5-arabic-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bakrianoo/t5-arabic-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("bakrianoo/t5-arabic-base") model = AutoModelForSeq2SeqLM.from_pretrained("bakrianoo/t5-arabic-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from bakrianoo/t5-arabic-base: direct link, hf CLI and curl.
- Browser
- Download file 791 Bytes
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https://huggingface.co/bakrianoo/t5-arabic-base/resolve/main/README.md
- Command line
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hf download hf://bakrianoo/t5-arabic-base/README.md
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curl -L -o README.md https://huggingface.co/bakrianoo/t5-arabic-base/resolve/main/README.md
791 Bytes
metadata
language: Arabic
datasets:
- mc4
license: apache-2.0
Arabic T5 Base Model
A customized T5 Model for Arabic and English Task. It could be used as an alternative for google/mt5-base model, as it's much smaller and only targets Arabic and English based tasks.
About T5
T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format.
The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.