README.md

---
language:
- eng
- afr
- nbl
- xho
- zul
- sot
- nso
- tsn
- ssw
- ven
- tso
license: cc-by-4.0
task_categories:
- sentence-similarity
- translation
pretty_name: The Vuk'uzenzele South African Multilingual Corpus
tags:
- multilingual
- government
arxiv: 2303.0375
configs:
- config_name: afr-tsn
  data_files:
  - split: train
    path: afr-tsn/train-*
  - split: test
    path: afr-tsn/test-*
- config_name: afr-xho
  data_files:
  - split: train
    path: afr-xho/train-*
  - split: test
    path: afr-xho/test-*
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
- config_name: nbl-nso
  data_files:
  - split: train
    path: nbl-nso/train-*
  - split: test
    path: nbl-nso/test-*
- config_name: tso-ven
  data_files:
  - split: train
    path: tso-ven/train-*
  - split: test
    path: tso-ven/test-*
dataset_info:
- config_name: afr-tsn
  features:
  - name: afr
    dtype: string
  - name: tsn
    dtype: string
  - name: score
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 1153686
    num_examples: 3235
  - name: test
    num_bytes: 289346
    num_examples: 809
  download_size: 912706
  dataset_size: 1443032
- config_name: afr-xho
  features:
  - name: afr
    dtype: string
  - name: xho
    dtype: string
  - name: score
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 1124390
    num_examples: 3541
  - name: test
    num_bytes: 277280
    num_examples: 886
  download_size: 937590
  dataset_size: 1401670
- config_name: default
  features:
  - name: nbl
    dtype: string
  - name: nso
    dtype: string
  - name: score
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 128131
    num_examples: 315
  - name: test
    num_bytes: 31826
    num_examples: 79
  download_size: 113394
  dataset_size: 159957
- config_name: nbl-nso
  features:
  - name: nbl
    dtype: string
  - name: nso
    dtype: string
  - name: score
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 128131
    num_examples: 315
  - name: test
    num_bytes: 31826
    num_examples: 79
  download_size: 113394
  dataset_size: 159957
- config_name: tso-ven
  features:
  - name: tso
    dtype: string
  - name: ven
    dtype: string
  - name: score
    dtype: float64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 197128
    num_examples: 428
  - name: test
    num_bytes: 45408
    num_examples: 108
  download_size: 158793
  dataset_size: 242536
---

# The Vuk'uzenzele South African Multilingual Corpus

Github: [https://github.com/dsfsi/vukuzenzele-nlp/](https://github.com/dsfsi/vukuzenzele-nlp/)

Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7598539.svg)](https://doi.org/10.5281/zenodo.7598539)

Arxiv Preprint: [![arXiv](https://img.shields.io/badge/arXiv-2303.03750-b31b1b.svg)](https://arxiv.org/abs/2303.03750)

Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse)

# About
The dataset was obtained from the South African government magazine Vuk'uzenzele, created by the [Government Communication and Information System (GCIS)](https://www.gcis.gov.za/). 
The original raw PDFS were obtatined from the [Vuk'uzenzele website](https://www.vukuzenzele.gov.za/).

The datasets contain government magazine editions in 11 languages, namely:

|  Language  | Code  |  Language  | Code |
|------------|-------|------------|-------|
| English    | (eng) | Sepedi     | (sep) |
| Afrikaans  | (afr) | Setswana   | (tsn) |
| isiNdebele | (nbl) | Siswati    | (ssw) |
| isiXhosa   | (xho) | Tshivenda  | (ven) |
| isiZulu    | (zul) | Xitstonga  | (tso) |
| Sesotho    | (nso) |

## Available pairings

The alignment direction is bidrectional, i.e. xho-zul is zul-xho

afr-eng; afr-nbl; afr-nso; afr-sot; afr-ssw; afr-tsn; afr-tso; afr-ven; afr-xho; afr-zul  
eng-nbl; eng-nso; eng-sot ;eng-ssw; eng-tsn; eng-tso; eng-ven; eng-xho; eng-zul  
nbl-nso; nbl-sot; nbl-ssw; nbl-tsn; nbl-tso; nbl-ven; nbl-xho; nbl-zul  
nso-sot; nso-ssw; nso-tsn; nso-tso; nso-ven; nso-xho; nso-zul  
sot-ssw; sot-tsn; sot-tso; sot-ven; sot-xho; sot-zul  
ssw-tsn; ssw-tso; ssw-ven; ssw-xho; ssw-zul  
tsn-tso; tsn-ven; tsn-xho; tsn-zul  
tso-ven; tso-xho; tso-zul  
ven-xho; ven-zul  
xho-zul  

# Disclaimer

This dataset contains machine-readable data extracted from PDF documents, from https://www.vukuzenzele.gov.za/, provided by the Government Communication Information System (GCIS). While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Government Communication Information System (GCIS) bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information.

# Datasets
The datasets consist of pairwise sentence aligned data. There are 55 distinct datasets of paired sentences.
The data is obtained by comparing [LASER](https://github.com/facebookresearch/LASER) embeddings of sentence tokens between 2 languages. If the similarity is high, the sentences are deemed semantic equivalents of one another and the observation is outputted. 

Naming convention:  
The naming structure of the pairwise_sentence_aligned folder is `aligned-{src_lang_code}-{tgt_lang_code}.csv`.  
For example, `aligned-afr-zul.csv` is the aligned sentences between Afrikaans and isiZulu. 

The data is in .csv format and the columns are `src_text`,`tgt_text`,`cosine_score` where:  
- `src_text` is the source sentence
- `tgt_text` is the target sentence
- `cosine_score` is the cosine similarity score obtained by comparing the sentence embeddings, it ranges from 0 to 1

**Note:** The notion of source (src) and target (tgt) are only necessary for distinction between the languages used in the aligned pair, as the sentence semantics should be bidirectional. (hallo <-> sawubona)

# Citation
Vukosi Marivate, Andani Madodonga, Daniel Njini, Richard Lastrucci, Isheanesu Dzingirai, Jenalea Rajab. **The Vuk'uzenzele South African Multilingual Corpus**, 2023

> @dataset{marivate_vukosi_2023_7598540,
  author       = {Marivate, Vukosi and
                  Njini, Daniel and
                  Madodonga, Andani and
                  Lastrucci, Richard and
                  Dzingirai, Isheanesu
                  Rajab, Jenalea},
  title        = {The Vuk'uzenzele South African Multilingual Corpus},
  month        = feb,
  year         = 2023,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.7598539},
  url          = {https://doi.org/10.5281/zenodo.7598539}
}

### Licence
* Licence for Data - [CC 4.0 BY](LICENSE.md)