Update README.md

README.md

@@ -38,8 +38,8 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='nairaxo/bantu-bert')
->>> unmasker("rais wa [MASK] ya tanzania.")
+>>> unmasker = pipeline('fill-mask', model='nairaxo/bantulm')
+>>> unmasker("rais wa [MASK] ya tanzania")
 
 
 ```
@@ -48,9 +48,9 @@ Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
 from transformers import BertTokenizer, BertModel
-tokenizer = BertTokenizer.from_pretrained('nairaxo/bantu-bert')
-model = BertModel.from_pretrained("nairaxo/bantu-bert")
-text = "Replace me by any text you'd like."
+tokenizer = BertTokenizer.from_pretrained('nairaxo/bantulm')
+model = BertModel.from_pretrained("nairaxo/bantulm")
+text = "rais wa jamhuri ya tanzania"
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
 ```
@@ -59,9 +59,9 @@ and in TensorFlow:
 
 ```python
 from transformers import BertTokenizer, TFBertModel
-tokenizer = BertTokenizer.from_pretrained('nairaxo/bantu-bert')
-model = TFBertModel.from_pretrained("nairaxo/bantu-bert")
-text = "Replace me by any text you'd like."
+tokenizer = BertTokenizer.from_pretrained('nairaxo/bantulm')
+model = TFBertModel.from_pretrained("nairaxo/bantulm")
+text = "rais wa jamhuri ya tanzania"
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)
 ```