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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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datasets: |
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- wikipedia |
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- securities reports |
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- summaries of financial results |
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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 the Japanese version of Wikipedia. |
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The training corpus is generated from the Japanese version of Wikipedia, using 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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## 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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**There will be another paper for this pretrained model. Be sure to check here again when you cite.** |
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``` |
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@inproceedings{bert_electra_japanese, |
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title = {Construction and Validation of a Pre-Trained Language Model |
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Using Financial Documents} |
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author = {Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi}, |
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month = {oct}, |
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year = {2021}, |
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booktitle = {"Proceedings of JSAI Special Interest Group on Financial Infomatics (SIG-FIN) 27"} |
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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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