Authors: Wissam Antoun, Fady Baly, Hazem Hajj
AraBERT is an Arabic pretrained language model based on Google’s BERT architecture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT PAPER and in the AraBERT Meetup
There is two versions of the model AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the Farasa Segmenter.
The model was trained on ~70M sentences or ~23GB of Arabic text with ~3B words.
Source Code Repository: https://github.com/aub-mind/arabert
Paper: https://www.aclweb.org/anthology/2020.osact-1.2.pdf
Results (Accuracy)
We evaluate both AraBERT models on different downstream tasks and compare it to mBERT, and other state of the art models (To the extent of our knowledge). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR, ArSaS), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
| Task | prev. SOTA | mBERT | AraBERTv0.1 | AraBERTv1 |
|---|---|---|---|---|
| HARD | 95.7 ElJundi et.al. | 95.7 | 96.2 | 96.1 |
| ASTD | 86.5 ElJundi et.al. | 80.1 | 92.2 | 92.6 |
| ArsenTD-Lev | 52.4 ElJundi et.al. | 51 | 58.9 | 59.4 |
| AJGT | 93 Dahou et.al. | 83.6 | 93.1 | 93.8 |
| LABR | 87.5 Dahou et.al. | 83 | 85.9 | 86.7 |
| ANERcorp | 81.7 (BiLSTM-CRF) | 78.4 | 84.2 | 81.9 |
| ARCD | mBERT | EM:34.2 F1: 61.3 | EM:51.14 F1:82.13 | EM:54.84 F1: 82.15 |
Model Weights and Vocab Download
| Models | AraBERTv0.1 | AraBERTv1 |
|---|---|---|
| TensorFlow | Drive Link | Drive Link |
| PyTorch | Drive_Link | Drive_Link |
You can find the PyTorch models in HuggingFace’s Transformer Library under the aubmindlab username
If you used this model please cite us as:
@inproceedings{antoun2020arabert,
title={AraBERT: Transformer-based Model for Arabic Language Understanding},
author={Antoun, Wissam and Baly, Fady and Hajj, Hazem},
booktitle={LREC 2020 Workshop Language Resources and Evaluation Conference 11--16 May 2020},
pages={9}
}
Acknowledgments
Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn’t have done it without this program, and to the AUB MIND Lab Members for the continous support. Also thanks to Yakshof and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT.
Contacts
Wissam Antoun: Linkedin | Twitter | Github | wfa07@mail.aub.edu | wissam.antoun@gmail.com
Fady Baly: Linkedin | Twitter | Github | fgb06@mail.aub.edu | baly.fady@gmail.com
We are looking for sponsors to train BERT-Large and other Transformer models, the sponsor only needs to cover to data storage and compute cost of the generating the pretraining data
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AraBERT, based on BERT architecture, offers two versions: AraBERTv0.1 and AraBERTv1. The latter employs pre-segmented text. Trained on 70M sentences (~23GB of Arabic text), it excels in various tasks like sentiment analysis, named entity recognition, and Arabic question answering. Results show superior performance compared to mBERT and other models. Models and vocab downloads are available. Cite as: Antoun et al., LREC 2020 Workshop. Acknowledgments to TFRC, AUB MIND Lab, Yakshof, Assafir, and artist Habib Rahal. Contacts for authors provided. Sponsors sought for training BERT-Large and other Transformer models.https://socialb.digital/
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