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README.md

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 # AraT5-base
-<img src="https://raw.githubusercontent.com/UBC-NLP/araT5/main/AraT5_logo.jpg" alt="drawing" width="30%" height="30%" align="right"/>
+# AraT5: Text-to-Text Transformers for Arabic Language Generation
 
-**AraT5-base** is one of three models described in our [**AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation**
-](https://arxiv.org/abs/2109.12068). In this paper, we introduce three powerful Arabic-specific text-to-text transformer models trained on large Modern Standard Arabic (MSA) and/or Dialectal Arabic (DA) data. **AraT5** is trained on 248GB of text (29B tokens) of MSA and DA, **AraT5-msa** is trained on 70GB of text (7.1B tokens) from MSA data, and **AraT5-tweet** is trained on 178Gb of text (21.9B tokens) from 1.5B Arabic tweets which contains multiple varieties of dialectical Arabic. 
+<img src="AraT5_CR_new.png" alt="AraT5" width="55%" height="45%" align="right"/>
 
-In addition, we provide the three models on two architectures small and base. For all models, we use a learning rate of 0.01, a batch size of 128 sequences, and a maximum sequence length of 512 whereas AraT5-tweet 128 maximum sequence is used. Hence, the original implementation of T5 in the TensorFlow framework is used to train the models. We train the models for 1M steps.8 Training took ∼ 80 days on 1 on Google Cloud TPU with 8 cores (v3.8) from TensorFlow Research Cloud (TFRC).
+This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://arxiv.org/abs/2109.12068). In this is the repository we introduce:
+* Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
+* Introduce **ARGEN**:  A new benchmark for Arabic language generation and evaluation for four Arabic NLP tasks, namely, ```machine  translation```,  ```summarization```,  ```news title   generation```,   ```question   generation```, ,   ```paraphrasing```,   ```transliteration```, and  ```code-switched translation```.
+* Evaluate  ```AraT5``` models on ```ARGEN``` and compare against available language models.
 
+Our models establish new state-of-the-art (SOTA) on  several publicly available datasets.
+Our language models are publicaly available for research (see below).
 
+The rest of this repository provides more information about our new language models, benchmark, and experiments.
+
+---
 # How to use AraT5 models
 Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset 
 ``` bash