Update README.md

README.md

@@ -21,8 +21,6 @@ widget:
 ---
 
 
-# TurkishBERTweet
-  
 #### Table of contents
 1. [Introduction](#introduction)
 2. [Main results](#results)
@@ -36,7 +34,7 @@ widget:
         - [HateSpeech Detection](#hs_lora)
 
 4. [Citation](#citation)
-# <a name="introduction"></a> TurkishBERTweet in the shadow of Large Language Models
+# <a name="introduction"></a> TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis
 
 
 # <a name="results"></a> Main Results
@@ -48,13 +46,13 @@ widget:
 # <a name="trainedModels"></a> Model
 Model | #params | Arch. | Max length | Pre-training data
 ---|---|---|---|---
-`VRLLab/TurkishBERTweet` | 163M | base | 128 | 894M Turkish Tweets (uncased)
+[`VRLLab/TurkishBERTweet`](https://huggingface.co/VRLLab/TurkishBERTweet) | 163M | base | 128 | 894M Turkish Tweets (uncased)
 
 # <a name="loraAdapter"></a> Lora Adapters
 Model | train f1 | dev f1 | test f1 | Dataset Size
 ---|---|---|---|---
-`VRLLab/TurkishBERTweet-Lora-SA` | 0.799 | 0.687 | 0.692 | 42,476 Turkish Tweets  
-`VRLLab/TurkishBERTweet-Lora-HS` | 0.915 | 0.796 | 0.831 | 4,683 Turkish Tweets  
+[`VRLLab/TurkishBERTweet-Lora-SA`](https://huggingface.co/VRLLab/TurkishBERTweet-Lora-SA) | 0.799 | 0.687 | 0.692 | 42,476 Turkish Tweets  
+[`VRLLab/TurkishBERTweet-Lora-HS`](https://huggingface.co/VRLLab/TurkishBERTweet-Lora-HS) | 0.915 | 0.796 | 0.831 | 4,683 Turkish Tweets  
 # <a name="usage2"></a> Example usage
 
 
@@ -203,13 +201,14 @@ Yes : kasmayin artik ya kac kere tanik olduk bu azgin tehlikeli “multecilerin
 # <a name="citation"></a> Citation
 ```bibtex
 @article{najafi2022TurkishBERTweet,
-    title={TurkishBERTweet in the shadow of Large Language Models},
+    title={TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis},
     author={Najafi, Ali and Varol, Onur},
-    journal={arXiv preprint },
+    journal={arXiv preprint 2311.18063},
     year={2023}
 }
 ```
 
+
 ## Acknowledgments
 We thank [Fatih Amasyali](https://avesis.yildiz.edu.tr/amasyali) for providing access to Tweet Sentiment datasets from Kemik group.
 This material is based upon work supported by the Google Cloud Research Credits program with the award GCP19980904. We also thank TUBITAK (121C220 and 222N311) for funding this project.