--- license: apache-2.0 mutilinguality: - multilingual task_categories: - text-generation task_ids: - language-modeling language: - afr - amh - arz - eng - fra - hau - ibo - kin - mlg - nya - orm - por - sna - som - sot - swa - tir - xho - yor - zul viewer: true dataset_info: - config_name: afr features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 4549624636 num_examples: 1042812 - name: validation num_bytes: 504320368 num_examples: 115868 download_size: 5124049817 dataset_size: 5053945004 - config_name: amh features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 1115662532 num_examples: 135863 - name: validation num_bytes: 123858179 num_examples: 15095 download_size: 1248728162 dataset_size: 1239520711 - config_name: arz features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 621073489 num_examples: 1455662 - name: validation num_bytes: 69342976 num_examples: 161740 download_size: 753246622 dataset_size: 690416465 - config_name: eng features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 3795223480 num_examples: 1378555 - name: validation num_bytes: 423622310 num_examples: 153172 download_size: 4279723559 dataset_size: 4218845790 - config_name: fra features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 3340740638 num_examples: 1443177 - name: validation num_bytes: 368983958 num_examples: 160352 download_size: 3796280757 dataset_size: 3709724596 - config_name: hau features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 909342448 num_examples: 359881 - name: validation num_bytes: 101151882 num_examples: 39986 download_size: 1027800797 dataset_size: 1010494330 - config_name: ibo features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 193493918 num_examples: 51386 - name: validation num_bytes: 22265232 num_examples: 5709 download_size: 219266571 dataset_size: 215759150 - config_name: kin features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 208582172 num_examples: 97064 - name: validation num_bytes: 10662209 num_examples: 5831 download_size: 222938591 dataset_size: 219244381 - config_name: mlg features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 561868602 num_examples: 216210 - name: validation num_bytes: 62280728 num_examples: 24023 download_size: 635783521 dataset_size: 624149330 - config_name: nya features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 260737793 num_examples: 39647 - name: validation num_bytes: 29199589 num_examples: 4405 download_size: 293880333 dataset_size: 289937382 - config_name: orm features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 51725718 num_examples: 20169 - name: validation num_bytes: 5500617 num_examples: 2241 download_size: 58001407 dataset_size: 57226335 - config_name: por features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 2191644027 num_examples: 1089199 - name: validation num_bytes: 245338209 num_examples: 121022 download_size: 2498665351 dataset_size: 2436982236 - config_name: sna features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 225393219 num_examples: 60986 - name: validation num_bytes: 25595688 num_examples: 6776 download_size: 254964089 dataset_size: 250988907 - config_name: som features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 2165910731 num_examples: 976484 - name: validation num_bytes: 241175779 num_examples: 108498 download_size: 2451878912 dataset_size: 2407086510 - config_name: sot features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 199386007 num_examples: 38361 - name: validation num_bytes: 22324957 num_examples: 4262 download_size: 224556522 dataset_size: 221710964 - config_name: swa features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 3371589021 num_examples: 1036254 - name: validation num_bytes: 373326029 num_examples: 115139 download_size: 3804265021 dataset_size: 3744915050 - config_name: tir features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 32026542 num_examples: 8240 - name: validation num_bytes: 3589604 num_examples: 915 download_size: 35955368 dataset_size: 35616146 - config_name: xho features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 114450184 num_examples: 23892 - name: validation num_bytes: 13051255 num_examples: 2654 download_size: 129410950 dataset_size: 127501439 - config_name: yor features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 192473693 num_examples: 73473 - name: validation num_bytes: 21123764 num_examples: 8163 download_size: 217343993 dataset_size: 213597457 - config_name: zul features: - name: id dtype: string - name: headline dtype: string - name: content dtype: string - name: category dtype: string - name: url dtype: string splits: - name: train num_bytes: 279244495 num_examples: 65447 - name: validation num_bytes: 30487397 num_examples: 7271 download_size: 314070508 dataset_size: 309731892 --- # Dataset Summary `WURA` is a document-level dataset covering 16 African Languages and 4 high-resource languages widely spoken in Africa (English, French, Arabic and Portuguese). This dataset was created by auditing mC4 and crawling additional verified news sources. It was first used to train AfriTeVa V2. # Dataset Structure ``` >>> from datasets import load_dataset ``` Although the document-level dataset is loaded by default, you may also optionally load a passage-level dataset as follows ``` >>> data = load_dataset("castorini/wura, "yor", level="passage", verification_mode="no_checks") ``` Note that we must pass `verification_mode="no_checks` to prevent HF from verifying checksums against the document-level checksum infos. # Citation ``` @inproceedings{oladipo-etal-2023-better, title = "Better Quality Pre-training Data and T5 Models for {A}frican Languages", author = "Oladipo, Akintunde and Adeyemi, Mofetoluwa and Ahia, Orevaoghene and Owodunni, Abraham and Ogundepo, Odunayo and Adelani, David and Lin, Jimmy", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.11", pages = "158--168", abstract = "In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawls have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for 16 African languages, designed by carefully auditing existing pretraining corpora to understand and rectify prevalent quality issues. To compile this dataset, we undertake a rigorous examination of current data sources for thirteen languages within one of the most extensive multilingual web crawls, mC4, and extract cleaner data through meticulous auditing and improved web crawling strategies. Subsequently, we pretrain a new T5-based model on this dataset and evaluate its performance on multiple downstream tasks. Our model demonstrates better downstream effectiveness over existing pretrained models across four NLP tasks, underscoring the critical role data quality plays in pretraining language models in low-resource scenarios. Specifically, on cross-lingual QA evaluation, our new model is more than twice as effective as multilingual T5. All code, data and models are publicly available at https://github.com/castorini/AfriTeVa-keji.", } ```