Model reference · open weights
transformers-ud-japanese-electra-ginza-510 is an open-weight embedding model from megagonlabs. transformers-ud-japanese-electra-base-ginza-510 (BF16) weighs 434 MB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the megagonlabs/transformers-ud-japanese-electra-base-ginza-510 model card, 2026-10-01
What it is
| Released by | megagonlabs |
|---|---|
| Released | 2022-03-02 |
| VRAM | 434 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
This is an ELECTRA model pretrained on approximately 200M Japanese sentences extracted from the mC4 and finetuned by spaCy v3 on UD_Japanese_BCCWJ r2.8.
The base pretrain model is megagonlabs/transformers-ud-japanese-electra-base-discrimininator.
The entire spaCy v3 model is distributed as a python package named ja_ginza_electra from PyPI along with GiNZA v5 which provides some custom pipeline components to recognize the Japanese bunsetu-phrase structures.
Try running it as below:
$ pip install ginza ja_ginza_electra
$ ginza
The models are distributed under the terms of the MIT License.
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.
Contains information from mC4 which is made available under the ODC Attribution License.
@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},
}
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.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.