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--- |
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language: |
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- ja |
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thumbnail: "https://raw.githubusercontent.com/megagonlabs/ginza/static/docs/images/GiNZA_logo_4c_s.png" |
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tags: |
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- PyTorch |
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- Transformers |
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- spaCy |
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- ELECTRA |
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- GiNZA |
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- mC4 |
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- UD_Japanese-BCCWJ |
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- GSK2014-A |
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- ja |
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- MIT |
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license: "mit" |
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datasets: |
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- mC4 |
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- UD_Japanese_BCCWJ r2.8 |
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- GSK2014-A(2019) |
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metrics: |
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- UAS |
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- LAS |
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- UPOS |
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--- |
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# transformers-ud-japanese-electra-ginza-510 (sudachitra-wordpiece, mC4 Japanese) |
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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). |
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The base pretrain model is [megagonlabs/transformers-ud-japanese-electra-base-discrimininator](https://huggingface.co/megagonlabs/transformers-ud-japanese-electra-base-discriminator). |
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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. |
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Try running it as below: |
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```console |
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$ pip install ginza ja_ginza_electra |
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$ ginza |
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``` |
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## Licenses |
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The models are distributed under the terms of the [MIT License](https://opensource.org/licenses/mit-license.php). |
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## Acknowledgments |
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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. |
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## Citations |
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- [mC4](https://huggingface.co/datasets/mc4) |
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Contains information from `mC4` which is made available under the [ODC Attribution License](https://opendatacommons.org/licenses/by/1-0/). |
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``` |
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@article{2019t5, |
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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}, |
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title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, |
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journal = {arXiv e-prints}, |
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year = {2019}, |
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archivePrefix = {arXiv}, |
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eprint = {1910.10683}, |
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} |
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``` |
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- [UD\_Japanese\_BCCWJ r2.8](https://universaldependencies.org/treebanks/ja_bccwj/index.html) |
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``` |
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Asahara, M., Kanayama, H., Tanaka, T., Miyao, Y., Uematsu, S., Mori, S., |
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Matsumoto, Y., Omura, M., & Murawaki, Y. (2018). |
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Universal Dependencies Version 2 for Japanese. |
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In LREC-2018. |
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``` |
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- [GSK2014-A(2019)](https://www.gsk.or.jp/catalog/gsk2014-a/) |
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