Instructions to use reemalyami/AraRoBERTa-SA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reemalyami/AraRoBERTa-SA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="reemalyami/AraRoBERTa-SA")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("reemalyami/AraRoBERTa-SA") model = AutoModelForMaskedLM.from_pretrained("reemalyami/AraRoBERTa-SA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
- AraRoBERTa-SA: Saudi Arabia (SA) dialect.
- AraRoBERTa-EGY: Egypt (EGY) dialect.
- AraRoBERTa-KU: Kuwait (KU) dialect.
- AraRoBERTa-OM: Oman (OM) dialect.
- AraRoBERTa-LB: Lebanon (LB) dialect.
- AraRoBERTa-JO: Jordan (JO) dialect.
- AraRoBERTa-DZ: Algeria (DZ) dialect
When using the model, please cite our paper:
@inproceedings{alyami-al-zaidy-2022-weakly,
title = "Weakly and Semi-Supervised Learning for {A}rabic Text Classification using Monodialectal Language Models",
author = "AlYami, Reem and Al-Zaidy, Rabah",
booktitle = "Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.24",
pages = "260--272",
}
Contact
Reem AlYami: Linkedin | reem.yami@kfupm.edu.sa | yami.m.reem@gmail.com
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