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README.md

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+---
+
+language: ja
+
+license: cc-by-sa-4.0
+
+datasets:
+
+- wikipedia
+
+widget:
+
+- text: 東京大学で[MASK]の研究をしています。 
+
+---
+
+# BERT small Japanese finance
+
+This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
+
+The codes for the pretraining are available at [retarfi/language-pretraining](https://github.com/retarfi/language-pretraining/tree/v1.0).
+
+## Model architecture
+
+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.
+
+## 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 corpus file is 2.9GB, consisting of approximately 20M 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 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.
+
+## Citation
+
+**There will be another paper for this pretrained model. Be sure to check here again when you cite.**
+
+```
+@inproceedings{bert_electra_japanese,
+  title = {Construction and Validation of a Pre-Trained Language Model
+Using Financial Documents}
+  author = {Masahiro Suzuki and Hiroki Sakaji and Masanori Hirano and Kiyoshi Izumi},
+  month = {oct},
+  year = {2021},
+  booktitle = {"Proceedings of JSAI Special Interest Group on Financial Infomatics (SIG-FIN) 27"}
+}
+```
+
+## Licenses
+
+The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
+
+## Acknowledgments
+
+This work was supported by JSPS KAKENHI Grant Number JP21K12010.