Add description for abbreviations

README.md

@@ -11,8 +11,11 @@ widget:
 ## Model description
 
 **CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants.
+We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three.
+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)."*
-We release eight models with different sizes and variants as follows:
+
+This model card describes **CAMeLBERT-CA** (`bert-base-camelbert-ca`), a model pre-trained on the CA (classical Arabic) dataset.
 
 ||Model|Variant|Size|#Word|
 |-|-|:-:|-:|-:|
@@ -25,8 +28,6 @@ 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 **CAMeLBERT-CA** (`bert-base-camelbert-ca`), a model pre-trained on the CA dataset.
-
 ## Intended uses
 You can use the released model for either masked language modeling or next sentence prediction.
 However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification.
@@ -83,7 +84,7 @@ output = model(encoded_input)
 ```
 
 ## Training data
-- CA
+- CA (classical Arabic)
   - [OpenITI (Version 2020.1.2)](https://zenodo.org/record/3891466#.YEX4-F0zbzc)
 
 ## Training procedure