Update README.md

README.md

@@ -22,7 +22,7 @@ After downloading the dataset, we went on the way to mask LM.
 
 ```py
 from transformers import AutoTokenizer, AutoModel
-bnbert_tokenizer = AutoTokenizer.from_pretrained("Kowsher/bert-base-test")
+bnbert_tokenizer = AutoTokenizer.from_pretrained("Kowsher/bert-base-bangla")
 text = "খাঁটি সোনার চাইতে খাঁটি আমার দেশের মাটি"
 bnbert_tokenizer.tokenize(text)
 # output: ['খাটি', 'সে', '##ানার', 'চাইতে', 'খাটি', 'আমার', 'দেশের', 'মাটি']
@@ -31,8 +31,8 @@ bnbert_tokenizer.tokenize(text)
 here, we can use bert base bangla model as for masked language modeling:
 ```py
 from transformers import BertForMaskedLM, BertTokenizer, pipeline
-model = BertForMaskedLM.from_pretrained("Kowsher/bert-base-test")
-tokenizer = BertTokenizer.from_pretrained("Kowsher/bert-base-test")
+model = BertForMaskedLM.from_pretrained("Kowsher/bert-base-bangla")
+tokenizer = BertTokenizer.from_pretrained("Kowsher/bert-base-bangla")
 
 nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
 for pred in nlp(f"আমি বাংলার গান {nlp.tokenizer.mask_token}"):