Update README.md

README.md

@@ -20,8 +20,8 @@ from transformers import AutoTokenizer, AutoModel, DistilBertForMaskedLM
 import torch
 import torch.nn.functional as F
 
-tokenizer = AutoTokenizer.from_pretrained('bongsoo/mbertV2.1', do_lower_case=False)
-model = DistilBertForMaskedLM.from_pretrained('bongsoo/mbertV2.1')
+tokenizer = AutoTokenizer.from_pretrained('bongsoo/mdistilbertV2.1', do_lower_case=False)
+model = DistilBertForMaskedLM.from_pretrained('bongsoo/mdistilbertV2.1')
 
 text = ['한국의 수도는 [MASK] 이다', '에펠탑은 [MASK]에 있다', '충무공 이순신은 [MASK]에 최고의 장수였다']
 tokenized_input = tokenizer(text, max_length=128, truncation=True, padding='max_length', return_tensors='pt')
@@ -81,8 +81,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('bongsoo/mbertV2.1')
-model = AutoModel.from_pretrained('bongsoo/mbertV2.1')
+tokenizer = AutoTokenizer.from_pretrained('bongsoo/mdistilbertV2.1')
+model = AutoModel.from_pretrained('bongsoo/mdistilbertV2.1')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')