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[Needs More Information]
# Dataset Card for allenai/wmt22_african
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset was created based on [metadata](https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african) for mined bitext released by Meta AI. It contains bitext for 248 pairs for the African languages that are part of the [2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages](https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html).
#### How to use the data
There are two ways to access the data:
* Via the Hugging Face Python datasets library
```
from datasets import load_dataset
dataset = load_dataset("allenai/wmt22_african")
```
* Clone the git repo
```
git lfs install
git clone https://huggingface.co/datasets/allenai/wmt22_african
```
### Supported Tasks and Leaderboards
This dataset is one of resources allowed under the Constrained Track for the [2022 WMT Shared Task on Large Scale Machine Translation Evaluation for African Languages](https://www.statmt.org/wmt22/large-scale-multilingual-translation-task.html).
### Languages
#### Focus languages
| Language | Code |
| -------- | ---- |
| Afrikaans | afr |
| Amharic | amh |
| Chichewa | nya |
| Nigerian Fulfulde | fuv |
| Hausa | hau |
| Igbo | ibo |
| Kamba | kam |
| Kinyarwanda | kin |
| Lingala | lin |
| Luganda | lug |
| Luo | luo |
| Northern Sotho | nso |
| Oroma | orm |
| Shona | sna |
| Somali | som |
| Swahili | swh |
| Swati | ssw |
| Tswana | tsn |
| Umbundu | umb |
| Wolof | wol |
| Xhosa | xho |
| Xitsonga | tso |
| Yoruba | yor |
| Zulu | zul |
Colonial linguae francae: English - eng, French - fra
## Dataset Structure
The dataset contains gzipped tab delimited text files for each direction. Each text file contains lines with parallel sentences.
### Data Instances
The dataset contains 248 language pairs.
Columns are:
source_sentence target_sentence laser_score source_sentence_lid target_sentence_lid where lid is language classification probability
Here are the sentence counts for each pair:\
1621007 afr-eng \
1172757 afr-som \
497739 amh-eng\
1888196 amh-fra\
566422 amh-nya\
89763 amh-orm\
844829 amh-sna\
491233 amh-som\
52337 amh-ssw\
1013477 amh-swh\
257342 amh-tsn\
231190 amh-tso\
99902 amh-umb\
508311 amh-xho\
399634 amh-yor\
834986 amh-zul\
1372999 eng-fuv\
2309758 eng-hau\
172973 eng-ibo\
1656141 eng-kam\
9732858 eng-kin\
2890688 eng-lin\
3450573 eng-lug\
2767100 eng-luo\
3043677 eng-nso\
1548650 eng-nya\
2793755 eng-orm\
8782707 eng-sna\
576601 eng-som\
165712 eng-ssw\
23358739 eng-swh\
5931529 eng-tsn\
630860 eng-tso\
302901 eng-umb\
95678 eng-wol\
8690985 eng-xho\
1455571 eng-yor\
3862020 eng-zul\
372003 fra-hau\
630593 fra-ibo\
198309 fra-kam\
1289491 fra-kin\
347026 fra-lin\
377017 fra-lug\
295465 fra-luo\
321118 fra-nso\
1170250 fra-nya\
319649 fra-orm\
1256559 fra-som\
119523 fra-ssw\
2607867 fra-swh\
630801 fra-tsn\
440861 fra-tso\
236624 fra-umb\
189659 fra-wol\
1092123 fra-xho\
1760905 fra-zul\
227958 fuv-hau\
89652 fuv-ibo\
13571 fuv-kam\
192596 fuv-kin\
79341 fuv-lug\
50756 fuv-luo\
42429 fuv-nso\
189176 fuv-nya
67398 fuv-orm\
106809 fuv-sna\
203640 fuv-som\
19283 fuv-ssw\
275428 fuv-swh\
74068 fuv-tsn\
55015 fuv-tso\
27888 fuv-umb\
138286 fuv-xho\
331301 fuv-yor\
150846 fuv-zul\
247694 hau-ibo\
90033 hau-kam\
317291 hau-kin\
169056 hau-lug\
152246 hau-luo\
158432 hau-nso\
1141968 hau-nya\
101928 hau-orm\
780160 hau-sna\
490683 hau-som\
73076 hau-ssw\
893732 hau-swh\
265892 hau-tsn\
213552 hau-tso\
111124 hau-umb\
596312 hau-xho\
762819 hau-yor\
796053 hau-zul\
33966 ibo-kam\
154467 ibo-kin\
91272 ibo-lug\
71387 ibo-luo\
81767 ibo-nso\
486357 ibo-nya\
52249 ibo-orm\
444070 ibo-sna\
337727 ibo-som\
36426 ibo-ssw\
479101 ibo-swh\
131142 ibo-tsn\
99214 ibo-tso\
48163 ibo-umb\
323382 ibo-xho\
378378 ibo-yor\
491925 ibo-zul\
74809 kam-kin\
52158 kam-lug\
39193 kam-luo\
35061 kam-nso\
92704 kam-nya\
33964 kam-orm\
94385 kam-sna\
84297 kam-som\
16222 kam-ssw\
223474 kam-swh\
69242 kam-tsn\
73198 kam-tso\
41157 kam-umb\
80998 kam-xho\
69432 kam-yor\
114922 kam-zul\
188222 kin-lug\
157234 kin-luo\
196675 kin-nso\
389725 kin-nya\
101820 kin-orm\
385576 kin-sna\
258130 kin-som\
85684 kin-ssw\
743661 kin-swh\
268221 kin-tsn\
315691 kin-tso\
122759 kin-umb\
361464 kin-xho\
213902 kin-yor\
492158 kin-zul\
105776 lug-luo\
107569 lug-nso\
183247 lug-nya\
64732 lug-orm\
197359 lug-sna\
131828 lug-som\
51518 lug-ssw\
325122 lug-swh\
175387 lug-tsn\
148662 lug-tso\
75469 lug-umb\
154149 lug-xho\
137179 lug-yor\
194564 lug-zul\
87376 luo-nso\
166259 luo-nya\
48212 luo-orm\
204663 luo-sna\
123244 luo-som\
38356 luo-ssw\
324826 luo-swh\
133960 luo-tsn\
132306 luo-tso\
68896 luo-umb\
143748 luo-xho\
110753 luo-yor\
196325 luo-zul\
154111 nso-nya\
70340 nso-orm\
155175 nso-sna\
130594 nso-som\
74696 nso-ssw\
307206 nso-swh\
234768 nso-tsn\
212052 nso-tso\
63006 nso-umb\
200563 nso-xho\
148906 nso-yor\
230661 nso-zul\
82514 nya-orm\
976015 nya-sna\
516451 nya-som\
76598 nya-ssw\
1078568 nya-swh\
276998 nya-tsn\
350167 nya-tso\
141972 nya-umb\
698857 nya-xho\
512418 nya-yor\
1062461 nya-zul\
91493 orm-sna\
83049 orm-som\
31701 orm-ssw\
178212 orm-swh\
97553 orm-tsn\
78559 orm-tso\
44331 orm-umb\
95505 orm-xho\
73868 orm-yor\
92733 orm-zul\
511185 sna-som\
76168 sna-ssw\
1095473 sna-swh\
287574 sna-tsn\
336898 sna-tso\
152770 sna-umb\
842612 sna-xho\
524739 sna-yor\
1160370 sna-zul\
61247 som-ssw\
604372 som-swh\
179485 som-tsn\
177327 som-tso\
93461 som-umb\
69318 som-wol\
362513 som-xho\
355099 som-yor\
506404 som-zul\
147869 ssw-swh\
85369 ssw-tsn\
101540 ssw-tso\
29533 ssw-umb\
97437 ssw-xho\
66000 ssw-yor\
142991 ssw-zul\
480942 swh-tsn\
553410 swh-tso\
276967 swh-umb\
785796 swh-xho\
559321 swh-yor\
1240423 swh-zul\
285124 tsn-tso\
107024 tsn-umb\
287133 tsn-xho\
194308 tsn-yor\
341119 tsn-zul\
128803 tso-umb\
383556 tso-xho\
168359 tso-yor\
471398 tso-zul\
132264 umb-xho\
81309 umb-yor\
181634 umb-zul\
371261 xho-yor\
1066327 xho-zul\
560858 yor-zul
### Data Fields
Every instance for a language pair contains the following fields: 'translation' (containing sentence pairs), 'laser_score', 'source_sentence_lid', 'target_sentence_lid'.
Example:
```
{
'translation':
{
'afr': 'In Mei 2007, in ooreenstemming met die spesifikasies van die Java Gemeenskapproses, het Sun Java tegnologie geherlisensieer onder die GNU General Public License.',
'eng': 'As of May 2007, in compliance with the specifications of the Java Community Process, Sun relicensed most of its Java technologies under the GNU General Public License.'
},
'laser_score': 1.0717015266418457,
'source_sentence_lid': 0.9996600151062012,
'target_sentence_lid': 0.9972000122070312
}
```
### Data Splits
The data is not split into train, dev, and test.
## Dataset Creation
### Curation Rationale
Parallel sentences from monolingual data in Common Crawl and ParaCrawl were identified via [Language-Agnostic Sentence Representation (LASER)](https://github.com/facebookresearch/LASER) encoders.
### Source Data
#### Initial Data Collection and Normalization
Monolingual data was obtained from Common Crawl and ParaCrawl.
#### Who are the source language producers?
Contributors to web text in Common Crawl and ParaCrawl.
### Annotations
#### Annotation process
The data was not human annotated. The metadata used to create the dataset can be found here: https://github.com/facebookresearch/LASER/tree/main/data/wmt22_african
#### Who are the annotators?
The data was not human annotated. Parallel text from Common Crawl and Para Crawl monolingual data were identified automatically via [LASER](https://github.com/facebookresearch/LASER) encoders.
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
This dataset provides data for training machine learning systems for many languages that have low resources available for NLP.
### Discussion of Biases
Biases in the data have not been studied.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
We are releasing this dataset under the terms of [CC-BY-NC](https://github.com/facebookresearch/LASER/blob/main/data/wmt22_african/LICENSE).
### Citation Information
Forthcoming research paper that describes the approach used to create the metadata. Citation Information will be updated with the paper information when that is available.
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