DziriBERT: a Pre-trained Language Model for the Algerian Dialect

Overview

DziriBERT

dziribert drawing

DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets).

The model is publicly available at: https://huggingface.co/alger-ia/dziribert.

For more information, please visit our paper: https://arxiv.org/pdf/2109.12346.pdf

Evaluation

The Twifil dataset was used to compare DziriBERT with current multilingual, standard Arabic and dialectal Arabic models:

Model Sentiment acc. Emotion acc.
bert-base-multilingual-cased 73.6 % 59.4 %
aubmindlab/bert-base-arabert 72.1 % 61.2 %
CAMeL-Lab/bert-base-arabic-camelbert-mix 77.1 % 65.7 %
qarib/bert-base-qarib 77.7 % 67.6 %
UBC-NLP/MARBERT 80.1 % 68.4 %
alger-ia/dziribert 80.3 % 69.3 %

In order to reproduce these results, please install the following requirements:

pip install -r requirements.txt

Then, run the following evaluation script:

python3 evaluate_model.py

These results have been obtained on a Tesla K80 GPU.

Pretrained DziriBERT

DziriBERT has been uploaded to the HuggingFace hub in order to facilitate its use: https://huggingface.co/alger-ia/dziribert.

It can be easily downloaded and loaded using the transformers library:

from transformers import BertTokenizer, BertForMaskedLM

tokenizer = BertTokenizer.from_pretrained("alger-ia/dziribert")
model = BertForMaskedLM.from_pretrained("alger-ia/dziribert")

How to cite

@article{dziribert,
  title={DziriBERT: a Pre-trained Language Model for the Algerian Dialect},
  author={Abdaoui, Amine and Berrimi, Mohamed and Oussalah, Mourad and Moussaoui, Abdelouahab},
  journal={arXiv preprint arXiv:2109.12346},
  year={2021}
}

Contact

Please contact [email protected] for any question, feedback or request.

Code for SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes (NeurIPS 2021)

SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes (NeurIPS 2021) SyncTwin is a treatment effect estimation method tailored for observat

Zhaozhi Qian 3 Nov 03, 2022
OoD Minimum Anomaly Score GAN - Code for the Paper 'OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary'

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary Out-of-Distribution Minimum Anomaly Score GAN (OMASGAN) C

- 8 Sep 27, 2022
Download from Onlyfans.com.

OnlySave: Onlyfans downloader Getting Started: Download the setup executable from the latest release. Install and run. Only works on Windows currently

4 May 30, 2022
🧠 A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation.', ECCV 2016

Deep CORAL A PyTorch implementation of 'Deep CORAL: Correlation Alignment for Deep Domain Adaptation. B Sun, K Saenko, ECCV 2016' Deep CORAL can learn

Andy Hsu 200 Dec 25, 2022
Text to image synthesis using thought vectors

Text To Image Synthesis Using Thought Vectors This is an experimental tensorflow implementation of synthesizing images from captions using Skip Though

Paarth Neekhara 2.1k Jan 05, 2023
Official Implementation of Swapping Autoencoder for Deep Image Manipulation (NeurIPS 2020)

Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang UC

449 Dec 27, 2022
An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions

Agar.io_Q-Learning_AI An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available act

1 Jun 09, 2022
xitorch: differentiable scientific computing library

xitorch is a PyTorch-based library of differentiable functions and functionals that can be widely used in scientific computing applications as well as deep learning.

24 Apr 15, 2021
IJON is an annotation mechanism that analysts can use to guide fuzzers such as AFL.

IJON SPACE EXPLORER IJON is an annotation mechanism that analysts can use to guide fuzzers such as AFL. Using only a small (usually one line) annotati

Chair for Sys­tems Se­cu­ri­ty 146 Dec 16, 2022
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
Tackling Obstacle Tower Challenge using PPO & A2C combined with ICM.

Obstacle Tower Challenge using Deep Reinforcement Learning Unity Obstacle Tower is a challenging realistic 3D, third person perspective and procedural

Zhuoyu Feng 5 Feb 10, 2022
GraphGT: Machine Learning Datasets for Graph Generation and Transformation

GraphGT: Machine Learning Datasets for Graph Generation and Transformation Dataset Website | Paper Installation Using pip To install the core environm

y6q9 50 Aug 18, 2022
Simulated garment dataset for virtual try-on

Simulated garment dataset for virtual try-on This repository contains the dataset used in the following papers: Self-Supervised Collision Handling via

33 Dec 20, 2022
PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon.

Hand Mesh Reconstruction Introduction This repo is the PyTorch implementation of hand mesh reconstruction described in CMR and MobRecon. Update 2021-1

Xingyu Chen 236 Dec 29, 2022
PyTorch implementation of federated learning framework based on the acceleration of global momentum

Federated Learning with Acceleration of Global Momentum PyTorch implementation of federated learning framework based on the acceleration of global mom

0 Dec 23, 2021
Train the HRNet model on ImageNet

High-resolution networks (HRNets) for Image classification News [2021/01/20] Add some stronger ImageNet pretrained models, e.g., the HRNet_W48_C_ssld_

HRNet 866 Jan 04, 2023
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

xmu-xiaoma66 7.7k Jan 05, 2023
WaveFake: A Data Set to Facilitate Audio DeepFake Detection

WaveFake: A Data Set to Facilitate Audio DeepFake Detection This is the code repository for our NeurIPS 2021 (Track on Datasets and Benchmarks) paper

Chair for Sys­tems Se­cu­ri­ty 27 Dec 22, 2022
ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing

ProFuzzBench - A Benchmark for Stateful Protocol Fuzzing ProFuzzBench is a benchmark for stateful fuzzing of network protocols. It includes a suite of

155 Jan 08, 2023