Update README.md

README.md

@@ -1,12 +1,15 @@
 # AraT5-msa-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-msa-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="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" 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.
 
 
+---
 # How to use AraT5 models
 Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset 
 ``` bash
@@ -29,6 +32,10 @@ In addition, we release the fine-tuned checkpoint of the News Title Generation (
 
 For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
 
+
+
+
+
 # AraT5 Models Checkpoints 
 
 AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
@@ -45,15 +52,19 @@ AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface websit
 
 If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
 ```bibtex
-@inproceedings{araT5-2021,
-    title = "{AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation",
+@inproceedings{nagoudi-2022-arat5,
+    title = "{AraT5: Text-to-Text Transformers for Arabic Language Generation",
     author = "Nagoudi, El Moatez Billah  and
       Elmadany, AbdelRahim  and
       Abdul-Mageed, Muhammad",
-    booktitle = "https://arxiv.org/abs/2109.12068",
-    month = aug,
-    year = "2021"}
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics",
+    month = May,
+    year = "2022",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    }
 ```
 
 ## Acknowledgments
-We gratefully acknowledge support from the Natural Sciences and Engineering Research Council  of Canada, the  Social  Sciences and  Humanities  Research  Council  of  Canada, Canadian  Foundation for  Innovation,  [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We  also  thank  the  [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
+We gratefully acknowledge support from the Natural Sciences and Engineering Research Council  of Canada, the  Social  Sciences and  Humanities  Research  Council  of  Canada, Canadian  Foundation for  Innovation,  [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We  also  thank  the  [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
+ (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.