Update README.md

README.md

@@ -59,6 +59,29 @@ For fine-tuning to KLEJ tasks we used [Polish RoBERTa](https://github.com/sdadas
 
 Our model achieved 1st place in cyberbullying detection (CBD) task in the [KLEJ leaderboard](https://klejbenchmark.com/leaderboard). Overall, it reached 7th place, just below HerBERT model.
 
+## Citation
+Please cite the following paper:
+```
+@inproceedings{szmyd-etal-2023-trelbert,
+    title = "{T}rel{BERT}: A pre-trained encoder for {P}olish {T}witter",
+    author = "Szmyd, Wojciech  and
+      Kotyla, Alicja  and
+      Zobni{\'o}w, Micha{\l}  and
+      Falkiewicz, Piotr  and
+      Bartczuk, Jakub  and
+      Zygad{\l}o, Artur",
+    booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
+    month = may,
+    year = "2023",
+    address = "Dubrovnik, Croatia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.bsnlp-1.3",
+    pages = "17--24",
+    abstract = "Pre-trained Transformer-based models have become immensely popular amongst NLP practitioners. We present TrelBERT {--} the first Polish language model suited for application in the social media domain. TrelBERT is based on an existing general-domain model and adapted to the language of social media by pre-training it further on a large collection of Twitter data. We demonstrate its usefulness by evaluating it in the downstream task of cyberbullying detection, in which it achieves state-of-the-art results, outperforming larger monolingual models trained on general-domain corpora, as well as multilingual in-domain models, by a large margin. We make the model publicly available. We also release a new dataset for the problem of harmful speech detection.",
+}
+
+```
+
 ## Authors
 
 Jakub Bartczuk, Krzysztof Dziedzic, Piotr Falkiewicz, Alicja Kotyla, Wojciech Szmyd, Michał Zobniów, Artur Zygadło