Update model name

README.md

@@ -15,18 +15,18 @@ We release pre-trained language models for Modern Standard Arabic (MSA), dialect
 We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth).
 The details are described in the paper *"[The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://arxiv.org/abs/2103.06678)."*
 
-This model card describes **CAMeLBERT-MSA** (`bert-base-camelbert-msa`), a model pre-trained on the entire MSA dataset.
+This model card describes **CAMeLBERT-MSA** (`bert-base-arabic-camelbert-msa`), a model pre-trained on the entire MSA dataset.
 
 ||Model|Variant|Size|#Word|
 |-|-|:-:|-:|-:|
-||`bert-base-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
-||`bert-base-camelbert-ca`|CA|6GB|847M|
-||`bert-base-camelbert-da`|DA|54GB|5.8B|
-|✔|`bert-base-camelbert-msa`|MSA|107GB|12.6B|
-||`bert-base-camelbert-msa-half`|MSA|53GB|6.3B|
-||`bert-base-camelbert-msa-quarter`|MSA|27GB|3.1B|
-||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B|
-||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M|
+||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
+||`bert-base-arabic-camelbert-ca`|CA|6GB|847M|
+||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B|
+|✔|`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B|
+||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B|
+||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B|
+||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B|
+||`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M|
 
 ## Intended uses
 You can use the released model for either masked language modeling or next sentence prediction.
@@ -37,7 +37,7 @@ 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='CAMeL-Lab/bert-base-camelbert-msa')
+>>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-msa')
 >>> unmasker("الهدف من الحياة هو [MASK] .")
 [{'sequence': '[CLS] الهدف من الحياة هو العمل. [SEP]',
   'score': 0.08507660031318665,
@@ -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('CAMeL-Lab/bert-base-camelbert-msa')
-model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-msa')
+tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa')
+model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa')
 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('CAMeL-Lab/bert-base-camelbert-msa')
-model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-msa')
+tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa')
+model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-msa')
 text = "مرحبا يا عالم."
 encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)