readme: ---
  language: 
    - ja
  thumbnail: "https://raw.githubusercontent.com/megagonlabs/ginza/static/docs/images/GiNZA_logo_4c_s.png"
  tags:
  - PyTorch
  - Transformers
  - spaCy
  - ELECTRA
  - GiNZA
  - mC4
  - UD_Japanese-BCCWJ
  - GSK2014-A
  - ja
  - MIT
  license: "mit"
  datasets:
  - mC4
  - UD_Japanese_BCCWJ r2.8
  - GSK2014-A(2019)
  metrics:
  - UAS
  - LAS
  - UPOS
  ---
  
  # transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese)
  
  This is an [ELECTRA](https://github.com/google-research/electra) model pretrained on approximately 200M Japanese sentences extracted from the [mC4](https://huggingface.co/datasets/mc4) and finetuned by [spaCy v3](https://spacy.io/usage/v3) on [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html).
  
  The base pretrain model is [megagonlabs/transformers-ud-japanese-electra-base-discrimininator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator).
  
  The entire spaCy v3 model is distributed as a python package named [`ja_ginza_electra`](https://pypi.org/project/ja-ginza-electra/) from PyPI along with [`GiNZA v5`](https://github.com/megagonlabs/ginza) which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures.
  Try running it as below:
  ```console
  $ pip install ginza ja_ginza_electra
  $ ginza
  ```
  
  ## Licenses
  
  The models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php).
  
  ## Acknowledgments
  
  This model is permitted to be published under the `MIT License` under a joint research agreement between NINJAL (National Institute for Japanese Language and Linguistics) and Megagon Labs Tokyo.
  
  ## Citations
  - [mC4](https://huggingface.co/datasets/mc4)
  
  Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/).
  ```
  @article{2019t5,
      author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
      title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
      journal = {arXiv e-prints},
      year = {2019},
      archivePrefix = {arXiv},
      eprint = {1910.10683},
  }
  ```
  
  - [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html)
  
  ```
  Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S.,
  Matsumoto, Y., Omura, M., & Murawaki, Y. (2018).
  Universal Dependencies Version 2 for Japanese.
  In LREC-2018.
  ```
  
  - [GSK2014-A(2019)](https://www.gsk.or.jp/catalog/gsk2014-a/)
  