language: ja
license: cc-by-sa-4.0
tags:
- finance
widget:
- text: 流動[MASK]は、1億円となりました。
Additional pretrained BERT base Japanese finance
This is a BERT 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 BERT small in the original BERT paper; 12 layers, 768 dimensions of hidden states, and 12 attention heads.
Training Data
The models are additionally trained on financial corpus from Tohoku University's BERT base Japanese model (cl-tohoku/bert-base-japanese).
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 consists of approximately 27M sentences.
Tokenization
You can use tokenizer Tohoku University's BERT base Japanese model (cl-tohoku/bert-base-japanese).
You can use the tokenizer:
tokenizer = transformers.BertJapaneseTokenizer.from_pretrained('cl-tohoku/bert-base-japanese')
Training
The models are trained with the same configuration as BERT base in the original BERT paper; 512 tokens per instance, 256 instances per batch, and 1M training steps.
Citation
@article{Suzuki-etal-2023-ipm,
title = {Constructing and analyzing domain-specific language model for financial text mining}
author = {Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi},
journal = {Information Processing & Management},
volume = {60},
number = {2},
pages = {103194},
year = {2023},
doi = {10.1016/j.ipm.2022.103194}
}
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 and JST-Mirai Program Grant Number JPMJMI20B1.