Bert Base Arabertv02

🚀 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.

✨ Features

• Two Model Versions: AraBERT comes in two versions, AraBERTv0.1 and AraBERTv1, with different text pre - processing methods.
• Downstream Task Evaluation: Evaluated on multiple downstream tasks and compared with other models.

🚀 Quick Start

For a quick start, you can follow the pre - processing and usage steps described in the following sections.

📦 Installation

Install the arabert python package to segment text for AraBERT v1 & v2 or to clean your data:

pip install arabert

💻 Usage Examples

Basic Usage

from arabert.preprocess import ArabertPreprocessor

model_name="aubmindlab/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)

>>> output: ولن نبالغ إذا قلنا : إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري

📚 Documentation

AraBERTv2

What's New!

AraBERT now comes in 4 new variants to replace the old v1 versions. More details are in the AraBERT folder, the README, and the AraBERT Paper.

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.

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).

For the new dataset, we added the unshuffled OSCAR corpus, after thoroughly filtering it, to the previous dataset used in AraBERTv1 but without the websites that we previously crawled:

• OSCAR unshuffled and filtered.
• [Arabic Wikipedia dump](https://archive.org/details/arwiki - 20190201) from 2020/09/01
• [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5 - billion - words - Arabic - Corpus - El - Khair/f3eeef4afb81223df96575adadf808fe7fe440b4)
• [The OSIAN Corpus](https://www.aclweb.org/anthology/W19 - 4619)
• Assafir news articles. Huge thank you for Assafir for providing us the data.

Preprocessing

It is recommended to apply our preprocessing function before training/testing on any dataset.

TensorFlow 1.x models

The TF1.x model are available in the HuggingFace models repo. You can download them 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)

📄 License

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. 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

Wissam Antoun: [Linkedin](https://www.linkedin.com/in/wissam - antoun - 622142b4/) | Twitter | Github | wfa07@mail.aub.edu | wissam.antoun@gmail.com

Fady Baly: Linkedin | Twitter | Github | fgb06@mail.aub.edu | baly.fady@gmail.com