Update README.md

README.md

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 license: cc-by-4.0
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 # Automatic Translation Alignment of Ancient Greek Texts
-GRC-ALIGNMENT model is an XLM-RoBERTa-based model, trained on 12 million monolingual ancient Greek tokens with Masked Language Model (MLM) training objective. Further, the model is fine-tuned on 45k parallel sentences, mainly in ancient Greek-English, Greek-Latin, and Greek-Georgian.
+GRC-ALIGNMENT model is an XLM-RoBERTa-based model, fine-tuned for automatic multilingual text alignment at the word level. 
+The model is trained on 12 million monolingual ancient Greek tokens with Masked Language Model (MLM) training objective. Further, the model is fine-tuned on 45k parallel sentences, mainly in ancient Greek-English, Greek-Latin, and Greek-Georgian.
 
 ### Multilingual Training Dataset
 |                Languages                |Sentences |                                      Source                                      |
 |:---------------------------------------|:-----------:|:--------------------------------------------------------------------------------|
 | GRC-ENG                                 |      32.500 | Perseus Digital Library (Iliad, Odyssey, Xenophon, New Testament)                |
 | GRC-LAT                                 |       8.200 | [Digital Fragmenta Historicorum Graecorum project](https://www.dfhg-project.org/) |
-| GRC-KAT <br>GRC-ENG <br>GRC-LAT<br>GRC-ITA<br>GRC-POR |       4.000 | [UGARIT Translation Alignment Editor](https://ugarit.ialigner.com/ )             |
+| GRC-KAT <br>GRC-ENG <br>GRC-LAT<br>GRC-ITA<br>GRC-POR |       4.000 | [UGARIT Translation Alignment Editor](https://ugarit.ialigner.com/ )             |
+
+If you use this model, please cite our paper:
+<pre>
+@misc{yousef_palladino_wright_berti_2022,
+ title={Automatic Translation Alignment for Ancient Greek and Latin},
+ url={osf.io/8epsy},
+ DOI={10.31219/osf.io/8epsy},
+ publisher={OSF Preprints},
+ author={Yousef, Tariq and Palladino, Chiara and Wright, David J and Berti, Monica},
+ year={2022},
+ month={Apr}
+}</pre>