metadata

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/

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:

{
  '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/.

Data Splits

Only a train split is provided.

Citation Information

@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",
}