added citation

README.md

@@ -65,7 +65,7 @@ Follow the guide linked [here](https://towardsdatascience.com/fine-tuning-gpt2-o
 
 ## Finetuning using our code with TF 1.15.4:
 
-- Create the Training TFRecords:
+Create the Training TFRecords:
 ```bash
 python create_pretraining_data.py
  --input_file=<RAW TEXT FILE with documents/article sperated by an empty line>
@@ -73,7 +73,7 @@ python create_pretraining_data.py
  --tokenizer_dir=<Directory with the GPT2 Tokenizer files>
  ```
 
- - Finetuning:
+ Finetuning:
  ```bash
  python3 run_pretraining.py \
  --input_file="gs://<GS_BUCKET>/pretraining_data/*" \
@@ -137,13 +137,18 @@ For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly fi
 # If you used this model please cite us as :
 
 ```
-@misc{antoun2020aragpt2,
-      title={AraGPT2: Pre-Trained Transformer for Arabic Language Generation},
-      author={Wissam Antoun and Fady Baly and Hazem Hajj},
-      year={2020},
-      eprint={2012.15520},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@inproceedings{antoun-etal-2021-aragpt2,
+    title = "{A}ra{GPT}2: Pre-Trained Transformer for {A}rabic Language Generation",
+    author = "Antoun, Wissam  and
+      Baly, Fady  and
+      Hajj, Hazem",
+    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
+    month = apr,
+    year = "2021",
+    address = "Kyiv, Ukraine (Virtual)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2021.wanlp-1.21",
+    pages = "196--207",
 }
 ```