Data Science for Social Impact Research Group @ University of Pretoria

Data Science for Social Impact Research Group @ University of Pretoria's repositories

textaugment

TextAugment: Text Augmentation Library

Language:PythonMIT380 8 23

covid19za

Coronavirus COVID-19 (2019-nCoV) Data Repository and Dashboard for South Africa

Language:Jupyter NotebookMIT255 32 185

deadlines

:alarm_clock: AI/ML/DS conference/workshop/event deadlines on the African continent

Language:HTML16 50

The dataset contains editions from the South African government magazine Vuk'uzenzele. Data was scraped from PDFs that have been placed in the data/raw folder. The PDFS were obtained from the Vuk'uzenzele website.

Language:Jupyter NotebookMIT6022

gov-za-multilingual

The data set contains cabinet statements from the South African government. Data was scraped from the governments website: https://www.gov.za/cabinet-statements

Language:Jupyter NotebookMIT4 3 8

PuoBERTa

A Roberta-based language model specially designed for Setswana, using the new PuoData dataset.

Language:MakefileNOASSERTION4 10

Higher_Education_EDA

This is an EDA Git for education researchers and practitioners

Language:Jupyter NotebookApache-2.03 2 1

dsfsi-datasets

Datasets made available for different small projects

Language:Jupyter NotebookMIT2 20

project-state-capture

Zondo Commission or State Capture Commission Transcripts

CC-BY-SA-4.02 2 3

PuoData

Curated corpora for Setswana. Used to train PuoBERTa.

CC-BY-SA-4.02 10

sa-parliament

South African Member Of Parliament Data

Language:PythonMIT2 20

za-terminology

DSFSI South African Terminlogy Lists and Lexicon Project

Language:MakefileNOASSERTION2 10

embedding-eval-data

Embedding Evaluation Data for South African Languages

1 10

gov-za-sona-multilingual

Language:PythonNOASSERTION1 1 4

izindaba-zesizulu

Categorised isiZulu News. Source data is the isiZulu news from the SABC social media posts.

1 10

za-bank-risk

This repository is an initial pipeline for reading, processing, labelling and classifying unstructured annual reports of South African (SA) banks with the aim of identifying financial risk. It leveraged work by the Corporate Financial Information Environment-Final Report Structure Extractor (CFIE–FRSE) of El-Haj et al. which created a corpus of annual reports of United Kingdom (UK) companies.

Language:Jupyter NotebookCC-BY-SA-4.01 10