Update README.md

README.md

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 DReAMy is an open-source toolkit to automatically analyse (for now only) textual dream reports. At the moment, annotations are exclusively based on the [Hall & Van de Castle](https://link.springer.com/chapter/10.1007/978-1-4899-0298-6_2) emotions framework, but we are looking forward to expanding our set applications. As for the theoretical backbone, DReAMy and its model are based on a fruitful collaboration between NLP and sleep research. For more details, please refer to the [Bertolini et al., 23](https://arxiv.org/abs/2302.14828) pre-print.
 
-Here you can find the models used by DReAMy that were specifically tuned to analyse dream reports, as well as two datasets containing (non-annotated) dream reports from [DreamBank](https://www.dreambank.net/). Specialised models focus on two main tasks: sentiment analysis and character identification (i.e., NER), with both a multi-label classification and/or generation format.
+Here you can find DReAMy's models, tuned to analyse dream reports, as well as two datasets containing (non-annotated) dream reports from [DreamBank](https://www.dreambank.net/). Specialised models focus on two main tasks: sentiment analysis and character identification (i.e., NER), with both a multi-label classification and/or generation format.
 
-DReAMy can be easily installed via `pip install dreamy` – we do recommend using a virtual env running python 3.9. Please refer  to the git-repo for code, examples and tutorials. If you wish to test/query (a set of) DReAMy's model, try out a Space below.
+DReAMy can be easily installed via `pip install dreamy` – we do recommend using a virtual env running python 3.9. Please refer  to the git-repo for code, examples and tutorials. If you wish to test/query (a set of) DReAMy's model, try out a Space below. 
+
+Lastly, if you use a model in your work, please consider citing the pre-print.
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2302.14828,
+  doi = {10.48550/ARXIV.2302.14828},
+  url = {https://arxiv.org/abs/2302.14828},
+  author = {Bertolini, Lorenzo and Elce, Valentina and Michalak, Adriana and Bernardi, Giulio and Weeds, Julie},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Automatic Scoring of Dream Reports' Emotional Content with Large Language Models},
+  publisher = {arXiv},
+  year = {2023},
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```