--- language: - en - ja task_categories: - translation dataset_info: features: - name: translation struct: - name: en dtype: string - name: ja dtype: string splits: - name: train num_bytes: 1084069907 num_examples: 3669859 download_size: 603669921 dataset_size: 1084069907 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for JParaCrawl ### Dataset Summary Cleaned JParaCrawl data. For more information, see website below! **[https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/](https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/)** JParaCrawl is the largest publicly available English-Japanese parallel corpus created by NTT. It was created by largely crawling the web and automatically aligning parallel sentences. ### How to use ``` from datasets import load_dataset dataset = load_dataset("Hoshikuzu/JParaCrawl") ``` If data loading times are too long and boring, use Streaming. ``` from datasets import load_dataset dataset = load_dataset("Hoshikuzu/JParaCrawl", streaming=True) ``` ### Data Instances For example: ```json { 'en': 'Of course, we’ll keep the important stuff, but we’ll try to sell as much as possible of the stuff we don’t need. afterwards I feel like we can save money by reducing things and making life related patterns too.', 'ja': 'もちろん大切なものは取っておきますが、なくても困らないものはなるべく売るようにします。 さいごに ものを減らして、生活関連もパターン化することでお金は貯まる気がしています。' } ``` ### Licensing Information ### JParaCrawl is distributed under its own licence. Check the **[https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/](https://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/)**. ### Data Splits Only a `train` split is provided. ### Citation Information ### ```json @inproceedings{morishita-etal-2020-jparacrawl, title = "{JP}ara{C}rawl: A Large Scale Web-Based {E}nglish-{J}apanese Parallel Corpus", author = "Morishita, Makoto and Suzuki, Jun and Nagata, Masaaki", booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.443", pages = "3603--3609", ISBN = "979-10-95546-34-4", } ```