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https://github.com/UBC-NLP/marbert
UBC ARBERT and MARBERT Deep Bidirectional Transformers for Arabic
https://github.com/UBC-NLP/marbert
List: marbert
arabic arabic-dialects arabic-language arabic-models arabic-nlp arbert awesome benchmark bert bert-model classification deep-learning huggingface-transformers language-models marbert model-evaluation social-media ubc ubc-dlnlp ubcnlp
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UBC ARBERT and MARBERT Deep Bidirectional Transformers for Arabic
- Host: GitHub
- URL: https://github.com/UBC-NLP/marbert
- Owner: UBC-NLP
- Created: 2020-12-25T03:57:12.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-02T18:02:00.000Z (over 3 years ago)
- Last Synced: 2024-05-20T01:21:17.064Z (7 months ago)
- Topics: arabic, arabic-dialects, arabic-language, arabic-models, arabic-nlp, arbert, awesome, benchmark, bert, bert-model, classification, deep-learning, huggingface-transformers, language-models, marbert, model-evaluation, social-media, ubc, ubc-dlnlp, ubcnlp
- Homepage:
- Size: 581 KB
- Stars: 98
- Watchers: 10
- Forks: 14
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-arabic-nlp - UBC-NLP/marbert
- ultimate-awesome - marbert - UBC ARBERT and MARBERT Deep Bidirectional Transformers for Arabic . (Other Lists / PowerShell Lists)
README
# ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
# What is the repository is about?
This is the repository accompanying our ACL 2021 paper [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://aclanthology.org/2021.acl-long.551/).
In the paper, we:
* introduce ```ARBERT``` and ```MARBERT```, two powerful Transformer-based language models for Arabic;
* introduce ```ArBench```, a multi-domain, multi-variety benchmark for Arabic naturaal language understanding based on 41 datasets across 5 different tasks and task clusters;
* evaluate ARBERT and MARBERT on ArBench and compare against available language models.
Our models establish new state-of-the-art (SOTA) on all 5 tasks/task clusters on 37 out of the 41 datasets.
Our language models are publicaly available for research (see below).
The rest of this repository provides more information about our new language models, benchmark, and experiments.
---
## Table of Contents
- [1 Our Language Models](#1-Our-Language-Models)
- [1.1 ARBERT & MARBERT](#11-arbert--marbert)
- [1.2 Training Data and Vocabulary](#12-training-data-and-vocabulary)
- [2. Our Benchmark: ArBench](#2-our-benchmark-arbench)
- [2.1 Sentiment Analysis](#21-sentiment-analysis)
- [2.2 Social Meaning](#22-social-meaning)
- [2.3 Topic Classification](#23-topic-classification)
- [2.4 Dialect Identification](#24-dialect-identification)
- [2.5 Named Entity Recogntion (NER)](#25-named-entity-recogntion)
- [3. Model Evaluation](#3-model-evaluation)
- [3.1 Sentiment Analysis](#31-sentiment-analysis)
- [3.2 Social Meaning](#32-social-meaning)
- [3.3 Topic Classification](#33-topic-classification)
- [3.4 Dialect Identification](#34-Dialect-Identification)
- [3.5 Named Entity Recogntion (NER)](#35-named-entity-recogntion)
- [4. How to use ARBERT and MARBERT](#4-how-to-use-arbert-and-marbert)
- [5. Ethics](#5-ethics)
- [6. Download ARBERT and MARBERT Checkpoints](#6-download-arbert-and-marbert-checkpoints)
- [7. Citation](#7-citation)
- [8. Acknowledgments](#8-acknowledgments)
---
## 1. Our Language Models
## 1.1 ARBERT & MARBERT
**ARBERT** is a large scale pre-training masked language model focused on Modern Standard Arabic (MSA). To train ARBERT, we use the same architecture as BERT-base: 12 attention layers, each has 12 attention heads and 768 hidden dimensions, a vocabulary of 100K WordPieces, making ∼163M parameters. We train ARBERT on a collection of Arabic datasets comprising 61GB of text (6.2B tokens)
**MARBERT** is a large scale pre-training masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multiple varieties. To train MARBERT, we randomly sample 1B Arabic tweets from a large in-house dataset of about 6B tweets. We only include tweets with at least 3 Arabic words, based on character string matching, regardless whether the tweet has non-Arabic string or not. That is, we do not remove non-Arabic so long as the tweet meets the 3 Arabic word criterion. The dataset makes up 128GB of text (15.6B tokens). We use the same network architecture as ARBERT (BERT-base), but without the next sentence prediction (NSP) objective since tweets are short. See our [repo](https://github.com/UBC-NLP/LMBERT) for modifying BERT code to remove NSP.
## 1.2 Training Data and Vocabulary
The following table shows a comparison between ARBERT and MARBERT, on the one hand, and mBERT, XLM-R, and AraBERT, on the other hand. We compare in terms of pre-training data sources and size, vocabulary size, and model parameter size.
| | **Data Source** | **#Tokens(ar/all)** | **Tokanization** | **Vocab Size(ar/all)** | **Cased** | **Arch.** | **#Param** |
|---------|---------------------|----------------|---------------|--------------|-------|---------------|--------|
| mBERT | Wikipedia | 153M/1.5B | WordPiece | 5K/110K | yes | base | 110M |
| XLM-RB | CommonCraw | l2.9B/295B | SentencePiece | 14K/250K | yes | base | 270M |
| XLM-RL | CommonCraw | l2.9B/295B | SentencePiece | 14K/250K | yes | large | 550M |
| AraBERT | Several (3 sources) | 2.5B/2.5B | SentencePiece | 60K/64K | no | base | 135M |
| **ARBERT** | Several (6 sources) | 6.2B/6.2B | WordPiece | 100K/100K | no | base | 163M |
| **MARBERT** | Arabic Twitter | 15.6B/15.6B | WordPiece | 100K/100K | no | base | 163M |
---
## 2. Our Benchmark: ArBench
To evaluate our models, we also introduce **ArBench**, a new benchmark for multi-dialectal Arabic language understanding. ***ArBench is built using 41 datasets targeting 5 different tasks/task clusters***, allowing us to offer a series of standardized experiments under rich conditions. The following are the different tasks/task clusers covered by ArBench:
### 2.1 Sentiment Analysis
|**Reference**| **Data (#classes)** | **TRAIN** | **DEV** | **TEST** |
|---------|--------|--------|-------|------|
|[Alomari et al. (2017)](https://www.researchgate.net/publication/317501447_Arabic_Tweets_Sentimental_Analysis_Using_Machine_Learning)|AJGT (2) | 1.4K | - | 361 |
|[Abdul-Mageed et al. (2020b)](https://www.aclweb.org/anthology/2020.osact-1.3) |AraNETSent (2) | 100K | 14.3K | 11.8K |
|[Al-Twairesh et al. (2017)](https://www.aclweb.org/anthology/P16-1066)|AraSenTi (3) | 11,117 | 1,407 | 1,382 |
|[Abu Farha and Magdy (2017)](https://www.aclweb.org/anthology/2020.osact-1.5)|ArSarcasmSent (3) | 8.4K | - | 2.K |
|[Elmadany et al. (2018)](https://www.semanticscholar.org/paper/ArSAS-%3A-An-Arabic-Speech-Act-and-Sentiment-Corpus-Elmadany-Mubarak/d32d3bb226f1738f72c415c6b03b5ad66ff604a4)|ArSAS (3) | 24.7K | - | 3.6K |
|[Baly et al. (2019)](https://arxiv.org/abs/1906.01830)|ArsenTD-LEV (5) | 3.2K | - | 801 |
|[Nabil et al. (2015)](https://www.aclweb.org/anthology/D15-1299)|ASTD (3) | 24.7K | - | 664 |
|[Nabil et al. (2015)](https://www.aclweb.org/anthology/D15-1299)|ASTD-B (2) | 1.06K | - | 267 |
|[Abdul-Mageed and Diab (2012)](https://www.aclweb.org/anthology/L12-1630/)|AWATIF (4) | 2.28K | 288 | 284 |
|[Salameh et al. (2015)](https://www.aclweb.org/anthology/N15-1078)|BBN (3) | 960 | 125 | 116 |
|[Elnagar et al. (2018) ](https://www.springerprofessional.de/en/hotel-arabic-reviews-dataset-construction-for-sentiment-analysis/15234334)|HARD (2) | 84.5K | - | 21.1K |
|[Nabil et al. (2015)](http://www.aclweb.org/anthology/P/P13/P13-2088.pdf)|LABR (2) | 13.1K | | 3.28K |
|[Abdul-Mageed and Diab (2014)](https://cl.indiana.edu/~skuebler/papers/wassa12.pdf)|SAMAR (5) | 2.49K | 310 | 316 |
|[Rosenthal et al. (2017)](https://www.aclweb.org/anthology/S17-2088.pdf)|SemEval (3) | 24.7K | - | 6.10K |
|[Salameh et al. (2015)](https://www.aclweb.org/anthology/N15-1078)|SYTS(3) | 960 | 202 | 199 |
|[Saad (2019)](www.kaggle.com/mksaad/arabic-sentiment-twittercorpus)|TwitterSaad (2) | 1.5K | 202 | 190 | |
|[Abdullah et al. (2013)](https://www.researchgate.net/publication/261427893_Arabic_sentiment_analysis_Lexicon-based_and_corpus-based)|TwitterAbdullah (2) | 46k | 5.77k | 5.82k |
### 2.2 Social Meaning
|**Reference**| **Task** |**Data (#classes)** | **TRAIN** | **DEV** | **TEST** |
|----------------------| ---------------|---------|--------|--------|-------|
|[Zaghouani and Charfi (2018)](https://arxiv.org/abs/1808.07674)|Age| Arap-Tweet (3) | 1.28M | 160K | 160K |
|[Zaghouani and Charfi (2018)](https://arxiv.org/abs/1808.07674)| Gender| Arap-Tweet (2) | 1.28M | 160K | 160K |
|[Abdul-Mageed et al. (2020b)](https://www.aclweb.org/anthology/2020.osact-1.3)| Emotion | AraNETEmo (8) | 189K | 911 | 942 |
|[Abu Farha and Magdy (2017)](https://www.aclweb.org/anthology/2020.osact-1.5)|Sarcasm | AraSarcasm (2) | 8.4K | - | 2.1K |
|[Alshehri et al. (2020a)](https://www.aclweb.org/anthology/volumes/2020.osact-1/)| Dangerous |AraDang (2) | 3.4K | 616 | 664 |
|[Ghanem et al. (2019)](http://ceur-ws.org/Vol-2517/T4-1.pdf)| Irony | FIRE2019 (2) | 3.6K | - | 404 |
|[Mubarak et al. (2020)](https://www.aclweb.org/anthology/2020.osact-1.7)| Offensive | OSACT-A (2) | 10K | 1K | 2K |
|[Mubarak et al. (2020)](https://www.aclweb.org/anthology/2020.osact-1.7)| Hate Speech| OSACT-B - (2) | 10K | 1K | 2K |
### 2.3 Topic Classification
| **Reference** | **Data (#classes)** | **TRAIN** | **DEV** | **TEST** |
|-------------------------------------|---------|--------|--------|-------|
|[Saad and Ashour (2010)](https://www.researchgate.net/publication/320871842_ANT_Corpus_An_Arabic_News_Text_Collection_for_Textual_Classification) | OSAC (10) | 17.9K | 2.24K | 2.24K |
|[Abbas et al. (2011)](https://www.semanticscholar.org/paper/Evaluation-of-Topic-Identification-Methods-on-Abbas-Sma%C3%AFli/fec1c5be9ba9d0dfc96a5d8e46b4767c54802d72) | Khallej (4) | 4.55K | 570 | 570 |
|[Chouigui et al. (2017)](https://www.researchgate.net/publication/234136250_OSAC_Open_Source_Arabic_Corpora) | ANT(5) | 25.2K | 31.5K | 31.5K |
### 2.4 Dialect Identification
|**Reference**| **Data (#classes)** | **Task** | **TRAIN** | **DEV** | **TEST** |
|-----------|:--------------------------:|:---------:|:--------:|:--------:|:--------:
|[Zaidan and Callison-Burch (2014)](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00169)| AOC (2) | Binary | 86.5K | 10.8K | 10.8K |
|[Zaidan and Callison-Burch (2014)](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00169)| AOC (3) | Region | 35.7K | 4.46K | 4.45K |
|[Zaidan and Callison-Burch (2014)](https://www.mitpressjournals.org/doi/full/10.1162/COLI_a_00169)| AOC (4) | Region | 86.5K | 10.8K | 10.8K |
|[Farha and Magdy (2020)](https://www.aclweb.org/anthology/2020.osact-1.5/)| ArSarcasmDia (5) | Regoin | 8.43K | - | 2.11K |
|[Bouamor et al. (2019)](https://www.aclweb.org/anthology/W19-4622.pdf)| MADAR-TL (21) | Country | 193K | 26.6K | 43.9K |
|[Abdul-Mageed et al. (2020)](https://arxiv.org/abs/2010.11334)| NADI (21) | Country | 2.1K | 4.96K | 5K |
|[Abdul-Mageed et al. (2020)](https://arxiv.org/abs/2010.11334)| NADI (100) | Province | 2.1K | 4.96K | 5K |
|[Abdelali et al. (2020)](https://arxiv.org/abs/2005.06557)| QADI (18) | Country | 498K | - | 3.5K |
### 2.5 Named Entity Recogntion
|**Reference**| **Dataset**| **#Tokens** | **#PER** | **#LOC** | **#ORG** |
|---------|-----------|-------------|-----------|------------|----------|
|[Benajiba and Rosso (2007)](http://www.dsic.upv.es/~prosso/resources/BenajibaRosso_IICAI07.pdf) | ANERCorp | 150K | 6.50K | 5.01K | 3.43K |
|[Mitchell et al. (2004)](https://catalog.ldc.upenn.edu/LDC2004T09)| ACE-2003BN | 15K | 832 | 1.22K | 288 |
|[Mitchell et al. (2004)](https://catalog.ldc.upenn.edu/LDC2004T09)| ACE-2003NW | 27K | 1.14K | 1.14K | 893 |
|[Mitchell et al. (2005)](https://catalog.ldc.upenn.edu/LDC2005T09)| ACE-2004BN | 70K | 3.20K | 3.92K | 2.23K |
|[Darwish (2013)](https://www.aclweb.org/anthology/P13-1153)| TW-NER | 81K | 1.25K | 1.30K | 765 |
---
## 3 Model Evaluation
When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification tasks on the 41 datasets (82.22%). We present our results on the different TEST sets in the subsections below. For performance on DEV sets, please see appendixes in our [paper](https://mageed.arts.ubc.ca/files/2020/12/marbert_arxiv_2020.pdf).
### 3.1 Sentiment Analysis
| **Dataset (#classes)** | **mBERT** | **XLM-RB** | **XLM-RL** | **AraBERT** | **ARBERT** | **MARBERT** |
|--------------------|:------:|:----------:|:-----------:|:-------:|:------:|:-------:|
| AJGT (2) | 86.67 | 89.44 | 91.94 | 92.22 | 94.44 | **96.11** |
| HARD (2) |95.54 | 95.74 | 95.96 | 95.89 | 96.12 | **96.17** |
| ArsenTD-LEV (5) | 50.50 | 55.25 | **62.00** | 56.13 | 61.38 | 60.38 |
| LABR (2) | 91.20 | 91.23 | 92.20 | 91.97 | 92.51 | **92.49** |
| ASTD-B(2) | 79.32 | 87.59 | 77.44 | 83.08 | 93.23 | **96.24** |
***Results reported based on Acc. score***
| **Dataset (#classes)** | **mBERT** | **XLM-RB** | **XLM-RL** | **AraBERT** | **ARBERT** | **MARBERT** |
|--------------------|:------:|:----------:|:-----------:|:-------:|:------:|:-------:|
| ArSAS (3) | 87.50 | 90.00 | 91.50 | 91.00 | 92.00 | **93.00**|
| ASTD (3) | 67.00 | 60.67 | 67.67 | 72.00 | 76.50 | **78.00** |
| SemEval (3) | 57.00 | 64.00 | 67.00 | 62.00 | 69.00 | **71.00** |
| AraNETSent (2) | 84.00 | 92.00 | 93.00 | 86.50 | 89.00 | **92.00** |
| ArSarcasmSent (3) | 60.50 | 63.50 | 70.00 | 63.50 | 68.00 | **71.50** |
| AraSenTi (noura) (3) | 89.50 | **92.00** | 93.50 | 91.00 | 90.00 | 90.00 |
| BBN(3) | 55.50 | 69.50 | 46.50 | 70.00 | 76.50 | **79.00** |
| SYTS(3) | 67.00 | 78.00 | 40.50 | 75.50 | **79.00** | 76.50 |
| TwitterSaad (2) | 79.00 | 95.00 | 95.00 | 81.00 | 90.00 | **96.00** |
| SAMAR(5) | 22.50 | 54.00 | **57.00** | 36.50 | 43.50 | 55.50 |
| AWATIF(4) | 60.50 | 63.50 | 68.50 | 66.50 | 71.50 | **72.50** |
| TwitterAbdullah (2) | 81.50 | 91.00 | 92.00 | 89.50 | 91.50 | **95.00** |
***Results reported based on F1NP score.***
### 3.2 Social Meaning
| **Task (#classes)** | **Dataset** | **mBERT** | **XLM-RB** | **XLM-RL** | **AraBERT** | **ARBERT** | **MARBERT** |
|----------------|---------------------|:------:|:----------:|:-----------:|:-------:|:------:|:-------:|
| Offensive (2) | OSACT-A | 84.25 | 85.26 | 88.28 | 86.57 | 90.38 | **92.41** |
| Hate Speech(2) | OSACT-B | 72.81 | 71.33 | 79.31 | 78.89 | 83.02 | **84.79**|
| Dangerous (2) | Dangerous | 62.66 | 62.76 | 65.01 | 64.37 | 63.21 | **67.53** |
| Sarcasm (2) | AraSarcasm | 68.20 | 66.76 | 69.23 | 72.23 | 75.04 | **76.30** |
| Emotion (8) | AraNETEmo | 65.79 | 70.67 | 74.89 | 65.68 | 67.73 | **75.83** |
| Irony (2) | FIRE2019 | 80.96 | 81.97 | 82.52% | 83.01 | 85.59 | **85.33** |
| Age (3) | Arab_Tweet | 56.35 | 59.73 | 53.60 | 57.72 | 58.95 | **62.27** |
| Gender (2) | Arab_Tweet | 68.06| 71.00 | 71.14 | 67.75 | 69.86 | **72.62** |
***Results reported based on F1 score.***
### 3.3 Topic Classification
| **Dataset (#classes)** | **mBERT** | **XLM-RB** | **XLM-RL** | **AraBERT** | **ARBERT** | **MARBERT** |
|----------------|:------:|:----------:|:-----------:|:-------:|:------:|:-------:|
| OSAC (10) | 96.84 | 97.15 | 98.20 | 97.03 | **97.50** | 97.23 |
| Khallej (4) | 92.81 | 91.87 | 93.56 | 93.83 | **94.53** | 93.63 |
| ANTText (5) | 84.89 | 85.77 | 86.72 | **88.17** | 86.87 | 85.27 |
| ANTTitle (5) | 78.29 | 79.96 | 81.25 | 81.03 | **81.70** | 81.19 |
| ANTText+Title (5) | 84.67 | 86.21 | 86.96 | **87.22** | 87.21 | 85.60 |
***Results reported based on F1 score.***
### 3.4 Dialect Identification
| **Task (#classes)** | **Dataset** | **mBERT** | **XLM-RB** | **XLM-RL** | **AraBERT** | **ARBERT** | **MARBERT** |
|----------------|---------------------|:------:|:----------:|:-----------:|:-------:|:------:|:-------:|
| Regoin (5) | ArSarcasmDia | 43.81 | 41.71 | 41.83 | 47.54 | 51.27 | **54.70**|
| Country (21) | MADAR-TL | 34.92 | 35.91 | 35.14 | 34.87 | 37.90 |**40.40** |
| Region (4) | AOC | 77.27 | 77.34 | 78.77 | 79.20 | 81.09 | **82.37**|
| Region (3) | AOC | 85.76 | 86.39 | 87.56 | 87.68 | 89.06 | **90.85** |
| Binary (4) | AOC | 86.19 | 86.85 | 87.30 | 87.76 | 88.46 | **88.59** |
| Country(18) | QADI | 66.57 | 77.00 | 82.73 | 72.23 | 88.63 | **90.89** |
| Country(21) | NADI | 13.32 | 16.36 | 17.17 | 17.46 | 22.56 | **29.14** |
| Province (100 ) | NADI | 2.13 | 4.12 | 0.32 | 3.13 | 6.10 | **6.28** |
***Results reported based on F1 score.***
### 3.5 Named Entity Recogntion
| **Dataset** | **mBERT** | **XLM-RB** | **XLM-RL** | **AraBERT** | **ARBERT** | **MARBERT** |
|----------------|:------:|:----------:|:-----------:|:-------:|:------:|:-------:|
| ANERcorp. | 86.78 | 87.24 | **89.94** | 89.13 | 84.38 | 80.64 |
| ACE 2004 NW | 86.37 | **89.93** | 89.89 | 89.03 | 88.24 | 85.02 |
| ACE 2003BN | 91.23 | 53.97 | 85.41 | 91.94 | **96.18** | 79.05 |
| ACE 2003NW | 81.40| 87.24 | 90.62 | 88.09 | **90.09** | 87.76 |
| TW-NER | 36.83 | 49.16 | 54.44 | 41.26 | 59.17 | **67.39** |
***Results reported based on F1 score.***
---
## 4. How to use ARBERT and MARBERT
### 4.1 Loading directly from Huggingface
You can use ARBERT and MARBERT with [Hugging Face's Transformers](https://github.com/huggingface/transformers) library as follow.
```python
from transformers import AutoTokenizer, AutoModel
#load AEBERT model from huggingface
ARBERT_tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/ARBERT")
ARBERT_model = AutoModel.from_pretrained("UBC-NLP/ARBERT")
#load MAEBERT model from huggingface
MARBERT_tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERT")
MARBERT_model = AutoModel.from_pretrained("UBC-NLP/MARBERT")
```
### 4.2 Example: Fine-tuning MARBERT for Sentiment Analysis
MARBERT Fine-Tuning demo with PyTorch checkpoint for Sentiment Analysis on the AJGT dataset [](https://colab.research.google.com/drive/1M0ls7EPUi1dwqIDh6HNfJ5y826XvcgGX?usp=sharing)
## 5. Ethics
Our models are developed using data from the public domain.
We provide access to our models to accelerate scientific research with no liability on our part.
Please use our models and benchmark only ethically.
This includes, for example, respect and protection of people's privacy.
We encourage all researchers who decide to use our models to adhere to the highest standards.
For example, if you apply our models on Twitter data, we encourage you to review Twitter policy at [Twitter policy](https://developer.twitter.com/en/developer-terms/agreement-and-policy). For example, Twitter provides the following policy around use of [sensitive information](https://developer.twitter.com/en/developer-terms/more-on-restricted-use-cases):
### Sensitive information
You should be careful about using Twitter data to derive or infer potentially sensitive characteristics about Twitter users. Never derive or infer, or store derived or inferred, information about a Twitter user’s:
- Health (including pregnancy)
- Negative financial status or condition
- Political affiliation or beliefs
- Racial or ethnic origin
- Religious or philosophical affiliation or beliefs
- Sex life or sexual orientation
- Trade union membership
- Alleged or actual commission of a crime
- Aggregate analysis of Twitter content that does not store any personal data (for example, user IDs, usernames, and other identifiers) is permitted, provided that the analysis also complies with applicable laws and all parts of the Developer Agreement and Policy.
---
## 6. Download ARBERT and MARBERT Checkpoints
ARBERT and MARBERT Pytorch and Tenserflow checkpoints are available on Huggingface website for direct download and use ```exclusively for research```.
`For commercial use, please contact the authors via email @ (*muhammad.mageed[at]ubc[dot]ca*).`
| **Model** | **Pytorch Checkpoint** | **Tensorflow Checkpoint** |
|---------|:------------------:|:---------------:|
| **ARBERT** | [Download](https://huggingface.co/UBC-NLP/ARBERT/resolve/main/ARBERT_pytorch_verison.tar.gz) | [Download](https://huggingface.co/UBC-NLP/ARBERT/resolve/main/ARBERT_tensorflow_version.tar.gz) |
| **MARBERT** | [Download](https://huggingface.co/UBC-NLP/MARBERT/resolve/main/MARBERT_pytorch_verison.tar.gz) | [Download](https://huggingface.co/UBC-NLP/MARBERT/resolve/main/MARBERT_tensorflow_version.tar.gz) |
---
## 7. Citation
If you use our models (ARBERT or MARBERT) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```
@inproceedings{abdul-mageed-etal-2021-arbert,
title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic",
author = "Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Nagoudi, El Moatez Billah",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.551",
doi = "10.18653/v1/2021.acl-long.551",
pages = "7088--7105",
abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.",
}
```
---
## 8. Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.