Update README.md

README.md

@@ -6,12 +6,12 @@ widget:
  - text: "الهدف من الحياة هو [MASK] ."
 ---
 
-# CAMeLBERT-DA
+# CAMeLBERT: A collection of pre-trained models for Arabic NLP tasks
 
 ## Model description
 
-**CAMeLBERT** is a BERT model pre-trained on Arabic texts with different sizes and variants.
-The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."*
+**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
+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)."*
 We release eight models with different sizes and variants as follows:
 
 ||Model|Variant|Size|#Word|
@@ -25,7 +25,7 @@ We release eight models with different sizes and variants as follows:
 ||`bert-base-camelbert-msa-eighth`|MSA|14GB|1.6B|
 ||`bert-base-camelbert-msa-sixteenth`|MSA|6GB|746M|
 
-This model card describes `bert-base-camelbert-da`, a model pre-trained on the DA dataset.
+This model card describes **CAMeLBERT-DA** (`bert-base-camelbert-da`), a model pre-trained on the DA dataset.
 
 ## Intended uses
 You can use the released model for either masked language modeling or next sentence prediction.
@@ -60,6 +60,8 @@ You can use this model directly with a pipeline for masked language modeling:
   'token_str': 'الحب'}]
 ```
 
+*Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.
+
 Here is how to use this model to get the features of a given text in PyTorch:
 ```python
 from transformers import AutoTokenizer, AutoModel
@@ -82,7 +84,7 @@ output = model(encoded_input)
 
 ## Training data
 - DA
-  - A collection of dialectal Arabic data described in our paper.
+  - A collection of dialectal Arabic data described in [our paper](https://github.com/CAMeL-Lab/CAMeLBERT).
 
 ## Training procedure
 We use [the original implementation](https://github.com/google-research/bert) released by Google for pre-training.