Update README.md

README.md

@@ -63,7 +63,7 @@ def preprocess(text):
 
 from transformers import BertTokenizer, RobertaModel
 tokenizer = BertTokenizer.from_pretrained('ku-accms/roberta-base-japanese-ssuw')
-model = BertModel.from_pretrained("ku-accms/roberta-base-japanese-ssuw")
+model = RobertaModel.from_pretrained("ku-accms/roberta-base-japanese-ssuw")
 text = "京都大学で自然言語処理を専攻する。"
 encoded_input = tokenizer(preprocess(text), return_tensors='pt')
 output = model(**encoded_input)
@@ -73,7 +73,7 @@ output = model(**encoded_input)
 We used a Japanese Wikipedia dump (as of 20230101, 3.3GB) and a Japanese portion of CC100 (70GB).
 
 ## 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 7 days using 4 NVIDIA A100-SXM4-80GB GPUs.
+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 RoBERTa model using [transformers](https://github.com/huggingface/transformers) library. The training took about 7 days using 4 NVIDIA A100-SXM4-80GB GPUs.
 
 The following hyperparameters were used for the pre-training.