update README with citations for Helsinki models

README.md

@@ -25,6 +25,15 @@ The objective of this project was to showcase our individual learnings about lar
 - Amazon Polly (https://docs.aws.amazon.com/polly/latest/dg/what-is.html)
 - Helsinki-NLP/opus-mt models (https://huggingface.co/Helsinki-NLP)
 
+## Citations
+
+This project utilizes models from the OPUS-MT project. We thank Jörg Tiedemann and Santhosh Thottingal for their work:
+
+- Tiedemann, J., & Thottingal, S. (2020). OPUS-MT – Building open translation services for the World. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (pp. 479–480). European Association for Machine Translation. [https://aclanthology.org/2020.eamt-1.61](https://aclanthology.org/2020.eamt-1.61)
+
+- Tiedemann, J. (2020). The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT. In Proceedings of the Fifth Conference on Machine Translation (pp. 1174–1182). Association for Computational Linguistics. [https://aclanthology.org/2020.wmt-1.139](https://aclanthology.org/2020.wmt-1.139)
+
+
 ## Method
 
 L3-AI Created an assistant to ask your diabetes questions and when needed translate responses to an alternate language.
@@ -80,9 +89,6 @@ Streamlit VS Gradio:
 For L3-AI concept design we centered on diabetes however, we thought in future endeavors expanding to other disease states would enhance the work that was started. 
 The source material limited which sources we could pull from due to API restrictions.  
 
-## Opportunities and Next Steps 
-For L3-AI concept design we centered on diabetes however, we thought in future endeavors expanding to other disease states would enhance the work that was started. 
-
 
 ## Credits
 We would like to thank our pets who kept us company as we worked on coding and this application.