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は、アラビア語理解用に特別に設計されたBERTモデルの適応である。大規模なアラビア語コーパスで BERT を事前学習することで、AraBERT は、感情分析やテキスト分類など、アラビア語のさまざまな NLP タスクのパフォーマンスを向上させます。このモデルは、アラビア語圏における言語処理の精度と効率を大幅に向上させます。AraBERTのようなAIモデルがどのように言語理解を変革しているかについては、チャットgptをご覧ください。この統合は、言語技術の世界的な進歩におけるAIの広範な影響を浮き彫りにしています。
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