Fix path

README.md

@@ -36,7 +36,7 @@ We release our fine-tuninig code [here](https://github.com/CAMeL-Lab/CAMeLBERT).
 You can use this model directly with a pipeline for masked language modeling:
 ```python
 >>> from transformers import pipeline
->>> unmasker = pipeline('fill-mask', model='bert-base-camelbert-ca')
+>>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-camelbert-ca')
 >>> unmasker("الهدف من الحياة هو [MASK] .")
 [{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
   'score': 0.11048116534948349,
@@ -63,8 +63,8 @@ You can use this model directly with a pipeline for masked language modeling:
 Here is how to use this model to get the features of a given text in PyTorch:
 ```python
 from transformers import AutoTokenizer, AutoModel
-tokenizer = AutoTokenizer.from_pretrained('bert-base-camelbert-ca')
-model = AutoModel.from_pretrained('bert-base-camelbert-ca')
+tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
+model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
 text = "مرحبا يا عالم."
 encoded_input = tokenizer(text, return_tensors='pt')
 output = model(**encoded_input)
@@ -73,8 +73,8 @@ output = model(**encoded_input)
 and in TensorFlow:
 ```python
 from transformers import AutoTokenizer, TFAutoModel
-tokenizer = AutoTokenizer.from_pretrained('bert-base-camelbert-ca')
-model = TFAutoModel.from_pretrained('bert-base-camelbert-ca')
+tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
+model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
 text = "مرحبا يا عالم."
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)