| [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. | |