Bert Large Arabertv2

🚀 AraBERT v1 & v2: Pre-training BERT for Arabic Language Understanding

AraBERT is an Arabic pre-trained language model based on Google's BERT architecture. It uses the same BERT-Base configuration. More details can be found in the AraBERT Paper and 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 are split using the Farasa Segmenter.

We evaluate AraBERT models on different downstream tasks and compare them with mBERT and other state-of-the-art models (to the best 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

AraBERTv2

What's New!

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

All models are available on 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 problem was that punctuations and numbers were still attached to words when learning the wordpiece vocab. Now, we 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 approximately 3.5 times more data and trained for a longer time. For dataset sources, see the Dataset Section.

📦 Installation

It is recommended to apply our preprocessing function before training/testing on any dataset. 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-arabertv2"
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)
>>>"و+ لن نبالغ إذا قل +نا إن هاتف أو كمبيوتر ال+ مكتب في زمن +نا هذا ضروري"

📚 Documentation

Dataset

The pre-training data used for the new AraBERT model is also used for Arabic GPT2 and ELECTRA. The dataset consists of 77GB, 200,095,961 lines, 8,655,948,860 words, or 82,232,988,358 characters (before applying Farasa Segmentation).

For the new dataset, we added the unshuffled OSCAR corpus, after thorough filtering, to the previous dataset used in AraBERTv1, excluding the previously crawled websites:

• OSCAR unshuffled and filtered.
• Arabic Wikipedia dump from 2020/09/01
• The 1.5B words Arabic Corpus
• The OSIAN Corpus
• Assafir news articles. We are very grateful to Assafir for providing us with the data.

Preprocessing

It is recommended to apply our preprocessing function before training/testing on any dataset. Install the arabert python package to segment text for AraBERT v1 & v2 or to clean your data pip install arabert

TensorFlow 1.x models

The TF1.x models 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 (e.g., 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 the TensorFlow Research Cloud (TFRC) for free access to Cloud TPUs. We couldn't have done it without this program. Thanks also to the AUB MIND Lab members for their continuous support. Additionally, we thank Yakshof and Assafir for providing data and storage access. Special thanks to Habib Rahal (https://www.behance.net/rahalhabib) for giving AraBERT a visual identity.

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