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--- |
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language: ja |
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license: cc-by-sa-4.0 |
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tags: |
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- finance |
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widget: |
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- text: 流動[MASK]は、1億円となりました。 |
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--- |
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# BERT small Japanese finance |
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This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. |
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The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0). |
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## Model architecture |
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The model architecture is the same as BERT small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 12 layers, 256 dimensions of hidden states, and 4 attention heads. |
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## Training Data |
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The models are trained on Wikipedia corpus and financial corpus. |
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The Wikipedia corpus is generated from the Japanese Wikipedia dump file as of June 1, 2021. |
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The corpus file is 2.9GB, consisting of approximately 20M sentences. |
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The financial corpus consists of 2 corpora: |
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- Summaries of financial results from October 9, 2012, to December 31, 2020 |
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- Securities reports from February 8, 2018, to December 31, 2020 |
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The financial corpus file is 5.2GB, consisting of approximately 27M sentences. |
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## Tokenization |
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The texts are first tokenized by MeCab with IPA dictionary and then split into subwords by the WordPiece algorithm. |
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The vocabulary size is 32768. |
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## Training |
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The models are trained with the same configuration as BERT small in the [original ELECTRA paper](https://arxiv.org/abs/2003.10555); 128 tokens per instance, 128 instances per batch, and 1.45M training steps. |
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## Citation |
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``` |
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@article{Suzuki-etal-2023-ipm, |
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title = {Constructing and analyzing domain-specific language model for financial text mining} |
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author = {Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi}, |
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journal = {Information Processing & Management}, |
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volume = {60}, |
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number = {2}, |
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pages = {103194}, |
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year = {2023}, |
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doi = {10.1016/j.ipm.2022.103194} |
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} |
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``` |
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## Licenses |
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The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/). |
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## Acknowledgments |
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This work was supported by JSPS KAKENHI Grant Number JP21K12010. |
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