language: ja
license: cc-by-sa-4.0
tags:
- finance
datasets:
- wikipedia
- securities reports
- summaries of financial results
widget:
- text: 流動[MASK]は1億円となりました。
ELECTRA small Japanese finance discriminator
This is a ELECTRA model pretrained on texts in the Japanese language.
The codes for the pretraining are available at retarfi/language-pretraining.
Model architecture
The model architecture is the same as ELECTRA small in the original ELECTRA paper; 12 layers, 256 dimensions of hidden states, and 4 attention heads.
Training Data
The models are trained on the Japanese version of Wikipedia.
The training corpus is generated from the Japanese version of Wikipedia, using Wikipedia dump file as of June 1, 2021.
The Wikipedia corpus file is 2.9GB, consisting of approximately 20M sentences.
The financial corpus consists of 2 corpora:
Summaries of financial results from October 9, 2012, to December 31, 2020
Securities reports from February 8, 2018, to December 31, 2020
The financial corpus file is 5.2GB, consisting of approximately 27M sentences.
Tokenization
The texts are first tokenized by MeCab with IPA dictionary and then split into subwords by the WordPiece algorithm.
The vocabulary size is 32768.
Training
The models are trained with the same configuration as ELECTRA small in the original ELECTRA paper; 128 tokens per instance, 128 instances per batch, and 1M training steps.
Citation
There will be another paper for this pretrained model. Be sure to check here again when you cite.
@inproceedings{suzuki2021fin-bert-electra,
title={金融文書を用いた事前学習言語モデルの構築と検証},
% title={Construction and Validation of a Pre-Trained Language Model Using Financial Documents},
author={鈴木 雅弘 and 坂地 泰紀 and 平野 正徳 and 和泉 潔},
% author={Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi},
booktitle={人工知能学会第27回金融情報学研究会(SIG-FIN)},
% booktitle={Proceedings of JSAI Special Interest Group on Financial Infomatics (SIG-FIN) 27},
pages={5-10},
year={2021}
}
Licenses
The pretrained models are distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0.
Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP21K12010.