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Update model name

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  1. README.md +14 -14
README.md CHANGED
@@ -15,18 +15,18 @@ We release pre-trained language models for Modern Standard Arabic (MSA), dialect
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  We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth).
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  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)."*
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- This model card describes **CAMeLBERT-CA** (`bert-base-camelbert-ca`), a model pre-trained on the CA (classical Arabic) dataset.
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  ||Model|Variant|Size|#Word|
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  |-|-|:-:|-:|-:|
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- ||`bert-base-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
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- |✔|`bert-base-camelbert-ca`|CA|6GB|847M|
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- ||`bert-base-camelbert-da`|DA|54GB|5.8B|
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- ||`bert-base-camelbert-msa`|MSA|107GB|12.6B|
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- ||`bert-base-camelbert-msa-half`|MSA|53GB|6.3B|
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- ||`bert-base-camelbert-msa-quarter`|MSA|27GB|3.1B|
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- ||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B|
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- ||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M|
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  ## Intended uses
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  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).
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  You can use this model directly with a pipeline for masked language modeling:
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  ```python
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  >>> from transformers import pipeline
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- >>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-camelbert-ca')
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  >>> unmasker("الهدف من الحياة هو [MASK] .")
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  [{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
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  'score': 0.11048116534948349,
@@ -66,8 +66,8 @@ You can use this model directly with a pipeline for masked language modeling:
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  Here is how to use this model to get the features of a given text in PyTorch:
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  ```python
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  from transformers import AutoTokenizer, AutoModel
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- tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
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- model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
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  text = "مرحبا يا عالم."
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  encoded_input = tokenizer(text, return_tensors='pt')
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  output = model(**encoded_input)
@@ -76,8 +76,8 @@ output = model(**encoded_input)
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  and in TensorFlow:
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  ```python
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  from transformers import AutoTokenizer, TFAutoModel
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- tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
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- model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-camelbert-ca')
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  text = "مرحبا يا عالم."
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  encoded_input = tokenizer(text, return_tensors='tf')
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  output = model(encoded_input)
 
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  We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth).
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  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)."*
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+ This model card describes **CAMeLBERT-CA** (`bert-base-arabic-camelbert-ca`), a model pre-trained on the CA (classical Arabic) dataset.
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  ||Model|Variant|Size|#Word|
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  |-|-|:-:|-:|-:|
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+ ||`bert-base-arabic-camelbert-mix`|CA,DA,MSA|167GB|17.3B|
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+ |✔|`bert-base-arabic-camelbert-ca`|CA|6GB|847M|
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+ ||`bert-base-arabic-camelbert-da`|DA|54GB|5.8B|
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+ ||`bert-base-arabic-camelbert-msa`|MSA|107GB|12.6B|
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+ ||`bert-base-arabic-camelbert-msa-half`|MSA|53GB|6.3B|
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+ ||`bert-base-arabic-camelbert-msa-quarter`|MSA|27GB|3.1B|
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+ ||`bert-base-arabic-camelbert-msa-eighth`|MSA|14GB|1.6B|
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+ ||`bert-base-arabic-camelbert-msa-sixteenth`|MSA|6GB|746M|
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  ## Intended uses
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  You can use the released model for either masked language modeling or next sentence prediction.
 
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  You can use this model directly with a pipeline for masked language modeling:
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  ```python
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  >>> from transformers import pipeline
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+ >>> unmasker = pipeline('fill-mask', model='CAMeL-Lab/bert-base-arabic-camelbert-ca')
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  >>> unmasker("الهدف من الحياة هو [MASK] .")
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  [{'sequence': '[CLS] الهدف من الحياة هو الحياة. [SEP]',
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  'score': 0.11048116534948349,
 
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  Here is how to use this model to get the features of a given text in PyTorch:
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  ```python
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  from transformers import AutoTokenizer, AutoModel
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+ tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-ca')
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+ model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-ca')
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  text = "مرحبا يا عالم."
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  encoded_input = tokenizer(text, return_tensors='pt')
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  output = model(**encoded_input)
 
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  and in TensorFlow:
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  ```python
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  from transformers import AutoTokenizer, TFAutoModel
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+ tokenizer = AutoTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-ca')
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+ model = TFAutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-ca')
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  text = "مرحبا يا عالم."
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  encoded_input = tokenizer(text, return_tensors='tf')
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  output = model(encoded_input)