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README.md

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 <img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
 
-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.
+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 **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
 
 
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