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aubmindlab AUB MIND LAB university
2 models

How to use this model directly from the ๐Ÿค—/transformers library:

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from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aubmindlab/bert-base-arabert") model = AutoModel.from_pretrained("aubmindlab/bert-base-arabert")

AraBERT : Pre-training BERT for Arabic Language Understanding

AraBERT is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT PAPER and in the AraBERT Meetup

There are two version off the model AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the Farasa Segmenter.

The model was trained on ~70M sentences or ~23GB of Arabic text with ~3B words. The training corpora are a collection of publically available large scale raw arabic text (Arabic Wikidumps, The 1.5B words Arabic Corpus, The OSIAN Corpus, Assafir news articles, and 4 other manually crawled news websites (Al-Akhbar, Annahar, AL-Ahram, AL-Wafd) from the Wayback Machine)

We evalaute 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

Update 2 (21/5/2020) : Added support for the farasapy segmenter in the which is ~6x faster than the py4j.java_gateway, consider setting use_farasapy=True when calling preprocess and pass it an instance of FarasaSegmenter(interactive=True) with interactive set to True for faster segmentation.

Update 1 (21/4/2020) : Fixed an issue with ARCD fine-tuning which drastically improved performance. Initially we didn't account for the change of the answer_start during preprocessing.

Results (Acc.)

Task prev. SOTA mBERT AraBERTv0.1 AraBERTv1
HARD 95.7 ElJundi 95.7 96.2 96.1
ASTD 86.5 ElJundi 80.1 92.2 92.6
ArsenTD-Lev 52.4 ElJundi 51 58.9 59.4
AJGT 93 Dahou 83.6 93.1 93.8
LABR 87.5 Dahou 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

If you tested AraBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference

How to use

You can easily use AraBERT since it is almost fully compatible with existing codebases (Use this repo instead of the official BERT one, the only difference is in the file where we modify the _is_punctuation function to make it compatible with the "+" symbol and the "[" and "]" characters)

To use HuggingFace's Transformer repository you only need to provide a list of token that forces the model to not split them, also make sure that the text is pre-segmented: Not all libraries built on top of transformers support the never_split argument

from transformers import AutoTokenizer, AutoModel
from arabert.preprocess_arabert import never_split_tokens, preprocess
from farasa.segmenter import FarasaSegmenter

arabert_tokenizer = AutoTokenizer.from_pretrained(
arabert_model = AutoModel.from_pretrained("aubmindlab/bert-base-arabert")

#Preprocess the text to make it compatible with AraBERT using farasapy
farasa_segmenter = FarasaSegmenter(interactive=True)

#or you can use a py4j JavaGateway to the farasa Segmneter .jar but it's slower 
#(see update 2)
#from py4j.java_gateway import JavaGateway
#gateway = JavaGateway.launch_gateway(classpath='./PATH_TO_FARASA/FarasaSegmenterJar.jar')
#farasa =

text = "ูˆู„ู† ู†ุจุงู„ุบ ุฅุฐุง ู‚ู„ู†ุง ุฅู† ู‡ุงุชู ุฃูˆ ูƒู…ุจูŠูˆุชุฑ ุงู„ู…ูƒุชุจ ููŠ ุฒู…ู†ู†ุง ู‡ุฐุง ุถุฑูˆุฑูŠ"
text_preprocessed = preprocess( text,
                                do_farasa_tokenization = True,
                                farasa = farasa_segmenter,
                                use_farasapy = True)

>>>text_preprocessed: "ูˆ+ ู„ู† ู†ุจุงู„ุบ ุฅุฐุง ู‚ู„ +ู†ุง ุฅู† ู‡ุงุชู ุฃูˆ ูƒู…ุจูŠูˆุชุฑ ุงู„+ ู…ูƒุชุจ ููŠ ุฒู…ู† +ู†ุง ู‡ุฐุง ุถุฑูˆุฑูŠ"


>>> ['ูˆ+', 'ู„ู†', 'ู†ุจุงู„', '##ุบ', 'ุฅุฐุง', 'ู‚ู„', '+ู†ุง', 'ุฅู†', 'ู‡ุงุชู', 'ุฃูˆ', 'ูƒู…ุจูŠูˆุชุฑ', 'ุงู„+', 'ู…ูƒุชุจ', 'ููŠ', 'ุฒู…ู†', '+ู†ุง', 'ู‡ุฐุง', 'ุถุฑูˆุฑูŠ']

AraBERTv0.1 is compatible with all existing libraries, since it needs no pre-segmentation.

from transformers import AutoTokenizer, AutoModel

arabert_tokenizer = AutoTokenizer.from_pretrained("aubmindlab/bert-base-arabertv01",do_lower_case=False)
arabert_model = AutoModel.from_pretrained("aubmindlab/bert-base-arabertv01")

text = "ูˆู„ู† ู†ุจุงู„ุบ ุฅุฐุง ู‚ู„ู†ุง ุฅู† ู‡ุงุชู ุฃูˆ ูƒู…ุจูŠูˆุชุฑ ุงู„ู…ูƒุชุจ ููŠ ุฒู…ู†ู†ุง ู‡ุฐุง ุถุฑูˆุฑูŠ"

>>> ['ูˆู„ู†', 'ู†', '##ุจุงู„ุบ', 'ุฅุฐุง', 'ู‚ู„ู†ุง', 'ุฅู†', 'ู‡ุงุชู', 'ุฃูˆ', 'ูƒู…ุจูŠูˆุชุฑ', 'ุงู„ู…ูƒุชุจ', 'ููŠ', 'ุฒู…ู†', '##ู†', '##ุง', 'ู‡ุฐุง', 'ุถุฑูˆุฑูŠ']

The araBERT_(Updated_Demo_TF).ipynb Notebook is a small demo using the AJGT dataset using TensorFlow (GPU and TPU compatible).

Coming Soon : Fine-tunning demo using HuggingFace's Trainer API

AraBERT on ARCD During the preprocessing step the answer_start character position needs to be recalculated. You can use the file as shown below to clean, preprocess the ARCD dataset before running More detailed Colab notebook is available in the SOQAL repo.

python \
    --input_file="/PATH_TO/arcd-test.json" \
    --output_file="arcd-test-pre.json" \
    --do_farasa_tokenization=True \
    --use_farasapy=True \
python SOQAL/bert/ \
  --vocab_file="/PATH_TO_PRETRAINED_TF_CKPT/vocab.txt" \
  --bert_config_file="/PATH_TO_PRETRAINED_TF_CKPT/config.json" \
  --init_checkpoint="/PATH_TO_PRETRAINED_TF_CKPT/" \
  --do_train=True \
  --train_file=turk_combined_all_pre.json \
  --do_predict=True \
  --predict_file=arcd-test-pre.json \
  --train_batch_size=32 \
  --predict_batch_size=24 \
  --learning_rate=3e-5 \
  --num_train_epochs=4 \
  --max_seq_length=384 \
  --doc_stride=128 \
  --output_dir="/PATH_TO/OUTPUT_PATH"/ \
  --use_tpu=True \
  --tpu_name=$TPU_ADDRESS \

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:

  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},


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 (, for putting a face to AraBERT.


Wissam Antoun: Linkedin | Twitter | Github | |

Fady Baly: Linkedin | Twitter | Github | |