Update README.md

README.md

@@ -56,7 +56,7 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='cservan/fralbert-base-cased')
+>>> unmasker = pipeline('fill-mask', model='cservan/french-albert-base-cased')
 >>> unmasker("Paris est la capitale de la  [MASK] .")
 [
   {
@@ -96,8 +96,8 @@ Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
 from transformers import AlbertTokenizer, AlbertModel
-tokenizer = AlbertTokenizer.from_pretrained('cservan/fralbert-base-cased')
-model = AlbertModel.from_pretrained("cservan/fralbert-base-cased")
+tokenizer = AlbertTokenizer.from_pretrained('cservan/french-albert-base-cased')
+model = AlbertModel.from_pretrained("cservan/french-albert-base-cased")
 text = "Remplacez-moi par le texte en français que vous souhaitez."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -107,8 +107,8 @@ and in TensorFlow:
 
 ```python
 from transformers import AlbertTokenizer, TFAlbertModel
-tokenizer = AlbertTokenizer.from_pretrained('cservan/fralbert-base-cased')
-model = TFAlbertModel.from_pretrained("cservan/fralbert-base-cased")
+tokenizer = AlbertTokenizer.from_pretrained('cservan/french-albert-base-cased')
+model = TFAlbertModel.from_pretrained("cservan/french-albert-base-cased")
 text = "Remplacez-moi par le texte en français que vous souhaitez."
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)