🚀 AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
AraBERT is an Arabic pretrained language model based on Google's BERT architecture. It uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup.
There are two versions of the model, AraBERTv0.1 and AraBERTv1. The difference is that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the Farasa Segmenter.
We evaluate AraBERT models on different downstream tasks and compare them to mBERT and other state-of-the-art models (To the extent of our knowledge). The tasks include Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD.
🚀 Quick Start
To start using AraBERT, you can follow the steps in the following sections for installation, preprocessing, and model usage.
✨ Features
AraBERTv2
What's New!
AraBERT now comes in 4 new variants to replace the old v1 versions:
All models are available in the HuggingFace
model page under the aubmindlab name. Checkpoints are available in PyTorch, TF2, and TF1 formats.
Better Pre-Processing and New Vocab
We identified an issue with AraBERTv1's wordpiece vocabulary. The issue came from punctuations and numbers that were still attached to words when learning the wordpiece vocab. We now insert a space between numbers and characters and around punctuation characters.
The new vocabulary was learned using the BertWordpieceTokenizer
from the tokenizers
library and should now support the Fast tokenizer implementation from the transformers
library.
P.S.: All the old BERT codes should work with the new BERT. Just change the model name and check the new preprocessing function. Please read the section on how to use the preprocessing function
Bigger Dataset and More Compute
We used ~3.5 times more data and trained for longer. For dataset sources, see the Dataset Section.
Model |
Hardware |
num of examples with seq len (128 / 512) |
128 (Batch Size/ Num of Steps) |
512 (Batch Size/ Num of Steps) |
Total Steps |
Total Time (in Days) |
AraBERTv0.2-base |
TPUv3-8 |
420M / 207M |
2560 / 1M |
384/ 2M |
3M |
- |
AraBERTv0.2-large |
TPUv3-128 |
420M / 207M |
13440 / 250K |
2056 / 300K |
550K |
7 |
AraBERTv2-base |
TPUv3-8 |
420M / 207M |
2560 / 1M |
384/ 2M |
3M |
- |
AraBERTv2-large |
TPUv3-128 |
520M / 245M |
13440 / 250K |
2056 / 300K |
550K |
7 |
AraBERT-base (v1/v0.1) |
TPUv2-8 |
- |
512 / 900K |
128 / 300K |
1.2M |
4 |
Dataset
The pretraining data used for the new AraBERT model is also used for Arabic GPT2 and ELECTRA.
The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation).
The dataset sources include:
📦 Installation
Install farasapy for text segmentation
pip install farasapy
💻 Usage Examples
Preprocessing
from arabert.preprocess import ArabertPreprocessor
model_name="bert-large-arabertv02"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري"
arabert_prep.preprocess(text)
Accepted Models
bert-base-arabertv01
bert-base-arabert
bert-base-arabertv02
bert-base-arabertv2
bert-large-arabertv02
bert-large-arabertv2
araelectra-base
aragpt2-base
aragpt2-medium
aragpt2-large
aragpt2-mega
TensorFlow 1.x models
You can download the TF1.x models as follows:
- via git-lfs: clone all the models in a repo
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/aubmindlab/MODEL_NAME
tar -C ./MODEL_NAME -zxvf /content/MODEL_NAME/tf1_model.tar.gz
where MODEL_NAME
is any model under the aubmindlab
name.
- via
wget
:
- Go to the tf1_model.tar.gz file on huggingface.co/models/aubmindlab/MODEL_NAME.
- copy the
oid sha256
- then run
wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/INSERT_THE_SHA_HERE
(ex: for aragpt2-base
: wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/3766fc03d7c2593ff2fb991d275e96b81b0ecb2098b71ff315611d052ce65248
)
📚 Documentation
Citation
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. We couldn't have done it without this program. Thanks also to the AUB MIND Lab Members for the continuous support. Additionally, thanks to Yakshof and Assafir for data and storage access. Another thanks to Habib Rahal (https://www.behance.net/rahalhabib) for putting a face to AraBERT.
Contacts