Update README.md

README.md

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  - text: "ومن أشعاره الفصيحة ما يأتي:\nدع الأيام تفعل ما تشاء ** وطب نفسا إذا حكم القضاء\nولا تجزع لنازلة الليالي **  فما لحوادث الدنيا بقاء\nوكن رجلا على الأهوال جلدا ** وشيمتك"
 
 ---
+# Jasmine-350M
+# JASMINE: Arabic GPT Models for Few-Shot Learning
+
+
+This is the repository accompanying our EMNLP2023 paper [JASMINE: Arabic GPT Models for Few-Shot Learning](https://aclanthology.org/2023.emnlp-main.1040/). JASMINE is a suite of powerful Arabic autoregressive Transformer language models ranging in size between 300 million-6.7 billion parameters pretrained on a large and diverse dataset ( 235 GB of text). 
+
+# BibTex
+
+If you use Jasmine models  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{billah-nagoudi-etal-2023-jasmine,
+    title = "{JASMINE}: {A}rabic {GPT} Models for Few-Shot Learning",
+    author = "Billah Nagoudi, El Moatez  and
+      Abdul-Mageed, Muhammad  and
+      Elmadany, AbdelRahim  and
+      Inciarte, Alcides  and
+      Islam Khondaker, Md Tawkat",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.1040",
+    doi = "10.18653/v1/2023.emnlp-main.1040",
+    pages = "16721--16744",
+    abstract = "Scholarship on generative pretraining (GPT) remains acutely Anglocentric, leaving serious gaps in our understanding of the whole class of autoregressive models. For example, we have little knowledge about the potential of these models and their societal impacts in diverse linguistic and cultural settings. We alleviate this issue for Arabic, a wide collection of languages and dialectal varieties with more than 400 million population, by introducing JASMINE. JASMINE is a suite of powerful Arabic autoregressive Transformer language models ranging in size between 300 million-6.7 billion parameters pretrained on a large and diverse dataset ( 235 GB of text). We also carefully design and release a comprehensive benchmark for both automated and human evaluation of Arabic autoregressive models, with coverage of potential social biases, harms, and toxicity. Using our novel benchmark, we evaluate JASMINE extensively showing powerful performance intrinsically as well as in few-shot learning on a wide range of NLP tasks. We aim to responsibly release our models and evaluation benchmark with interested researchers, along with code for experimenting with them.",
+}
+
+## 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.