AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages

Overview

AfriBERTa: Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages

This repository contains the code for the paper Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages which appears in the first workshop on Multilingual Representation Learning at EMNLP 2021.

AfriBERTa was trained on 11 languages - Afaan Oromoo (also called Oromo), Amharic, Gahuza (a mixed language containing Kinyarwanda and Kirundi), Hausa, Igbo, Nigerian Pidgin, Somali, Swahili, Tigrinya and Yorùbá. AfriBERTa was evaluated on NER and text classification spanning 10 languages (some of which it was not pretrained on). It outperformed mBERT and XLM-R on several languages and is very competitive overall.

Pretrained models

We release the following pretrained models:

Reproducing Experiments

Datasets and Tokenizer

Below are details on how to obtain the datasets and trained sentencepiece tokenizer:

Language Modelling: The data for language modelling can be downloaded from this URL

NER: To obtain the NER dataset, please download it from this repository

Text Classification: To obtain the topic classification dataset, please download it from this repository

Tokenizer: The trained sentencepiece tokenizer can be downloaded from this URL

Training

To train AfriBERTa and evaluate on both downstream tasks, simply install all requirements in requirements.txt, download the relevant datasets and run the following script:

bash run_all.sh

This script will:

  1. Train the multilingual language model from scratch and save the model as well as relevant logs
  2. Evaluate the trained language model on NER for all ten languages over 5 seeds
  3. Evaluate the trained language model on text classification for all two languages over 5 seeds

Citation

@inproceedings{ogueji-etal-2021-small,
    title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
    author = "Ogueji, Kelechi  and
      Zhu, Yuxin  and
      Lin, Jimmy",
    booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.mrl-1.11",
    pages = "116--126",
}
Owner
Kelechi
nlp research
Kelechi
A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation.

TiSASRec.paddle A PaddlePaddle implementation of Time Interval Aware Self-Attentive Sequential Recommendation. Introduction 论文:Time Interval Aware Sel

Paddorch 2 Nov 28, 2021
Classification Modeling: Probability of Default

Credit Risk Modeling in Python Introduction: If you've ever applied for a credit card or loan, you know that financial firms process your information

Aktham Momani 2 Nov 07, 2022
Safe Policy Optimization with Local Features

Safe Policy Optimization with Local Feature (SPO-LF) This is the source-code for implementing the algorithms in the paper "Safe Policy Optimization wi

Akifumi Wachi 6 Jun 05, 2022
(ICCV'21) Official PyTorch implementation of Relational Embedding for Few-Shot Classification

Relational Embedding for Few-Shot Classification (ICCV 2021) Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho [paper], [project hompage] We propose t

Dahyun Kang 82 Dec 24, 2022
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2020 Links Doc

Sebastian Raschka 4.2k Jan 02, 2023
Genpass - A Passwors Generator App With Python3

Genpass Welcom again into another python3 App this is simply an Passwors Generat

Mal4D 1 Jan 09, 2022
kapre: Keras Audio Preprocessors

Kapre Keras Audio Preprocessors - compute STFT, ISTFT, Melspectrogram, and others on GPU real-time. Tested on Python 3.6 and 3.7 Why Kapre? vs. Pre-co

Keunwoo Choi 867 Dec 29, 2022
The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. Website • Key Features • How To Use • Docs •

Pytorch Lightning 21.1k Dec 29, 2022
Submission to Twitter's algorithmic bias bounty challenge

Twitter Ethics Challenge: Pixel Perfect Submission to Twitter's algorithmic bias bounty challenge, by Travis Hoppe (@metasemantic). Abstract We build

Travis Hoppe 4 Aug 19, 2022
Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

Naive-Bayes Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes Downloading Data Set Use our Breast Cancer Wisconsin Data Set Also you can

Faeze Habibi 0 Apr 06, 2022
Make your AirPlay devices as TTS speakers

Apple AirPlayer Home Assistant integration component, make your AirPlay devices as TTS speakers. Before Use 2021.6.X or earlier Apple Airplayer compon

George Zhao 117 Dec 15, 2022
Code for our paper at ECCV 2020: Post-Training Piecewise Linear Quantization for Deep Neural Networks

PWLQ Updates 2020/07/16 - We are working on getting permission from our institution to release our source code. We will release it once we are granted

54 Dec 15, 2022
Differentiable Simulation of Soft Multi-body Systems

Differentiable Simulation of Soft Multi-body Systems Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin [Paper] [Code] Updates The C++ backend s

YilingQiao 26 Dec 23, 2022
Applying curriculum to meta-learning for few shot classification

Curriculum Meta-Learning for Few-shot Classification We propose an adaptation of the curriculum training framework, applicable to state-of-the-art met

Stergiadis Manos 3 Oct 25, 2022
Attentional Focus Modulates Automatic Finger‑tapping Movements

"Attentional Focus Modulates Automatic Finger‑tapping Movements", in Scientific Reports

Xingxun Jiang 1 Dec 02, 2021
Official PyTorch implementation of "Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble" (NeurIPS'21)

Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble This is the code for reproducing the results of the paper Uncertainty-Bas

43 Nov 23, 2022
Implementation of Deformable Attention in Pytorch from the paper "Vision Transformer with Deformable Attention"

Deformable Attention Implementation of Deformable Attention from this paper in Pytorch, which appears to be an improvement to what was proposed in DET

Phil Wang 128 Dec 24, 2022
Repo for CVPR2021 paper "QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information"

QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information by Masato Tamura, Hiroki Ohashi, and Tomoaki Yosh

105 Dec 23, 2022
unofficial pytorch implementation of RefineGAN

RefineGAN unofficial pytorch implementation of RefineGAN (https://arxiv.org/abs/1709.00753) for CSMRI reconstruction, the official code using tensorpa

xinby17 5 Jul 21, 2022
Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline

Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline. The pipeline accepts english text as input and returns the French translation.

Afropunk Technologist 1 Jan 24, 2022