Update README.md

README.md

@@ -68,7 +68,7 @@ output = model(**encoded_input)
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 ## Training data
-We used a Japanese Wikipedia dump (as of 20230101, 15GB).
+We used a Japanese Wikipedia dump (as of 20230101, 3.3GB).
 
 ## Training procedure
 We first segmented the texts into words by KyTea and then tokenized the words into subwords using WordPiece with a vocabulary size of 32,000. We pre-trained the BERT model using [transformers](https://github.com/huggingface/transformers) library. The training took about 8 days using 4 NVIDIA A100-SXM4-80GB GPUs.