Update README.md

README.md

@@ -13,7 +13,7 @@ Our User Genrated Content (UGC) is comments written on articles collected from 3
 In order to valid the annotation, we search an agreement between raters to emotion in each sentence using krippendorff's alpha [(krippendorff, 1970)](https://journals.sagepub.com/doi/pdf/10.1177/001316447003000105). We left sentences that got alpha > 0.7. Note that while we found a general agreement between raters about emotion like happy, trust and disgust, there are few emotion with general disagreement about them, apparently given the complexity of finding them in the text (e.g. expectation and surprise).
 
 ### Performance
-#### sentiment analysis  				
+#### sentiment analysis  \t\t\t\t
 |              | precision | recall | f1-score |
 |--------------|-----------|--------|----------|
 | natural      | 0.83      | 0.56   | 0.67     |
@@ -29,3 +29,15 @@ We are still working on our model and will edit this page as we progress.<br>
 Note that we have released only sentiment analysis (polarity) at this point, emotion detection will be released later on.<br>
 our git: https://github.com/avichaychriqui/HeBERT
 
+
+## If you used this model please cite us as :
+Chriqui, A., & Yahav, I. (2021). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. arXiv preprint arXiv:2102.01909.
+```
+@article{chriqui2021hebert,
+  title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition},
+  author={Chriqui, Avihay and Yahav, Inbal},
+  journal={arXiv preprint arXiv:2102.01909},
+  year={2021}
+}
+```
+