Update README.md

README.md

@@ -11,7 +11,7 @@ widget:
 
 # bert-base-irish-cased-v1
 
-[gaBERT](https://arxiv.org/abs/2107.12930) is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.
+[gaBERT](https://aclanthology.org/2022.lrec-1.511/) is a BERT-base model trained on 7.9M Irish sentences. For more details, including the hyperparameters and pretraining corpora used please refer to our paper.
 
 ## Model description
 
@@ -36,3 +36,27 @@ The following hyperparameters were used during training:
 - TensorFlow 2.9.1
 - Datasets 2.3.2
 - Tokenizers 0.12.1
+
+### Citation
+If you use this model in your research, please consider citing the following paper:
+```
+@inproceedings{barry-etal-2022-gabert,
+    title = "ga{BERT} {---} an {I}rish Language Model",
+    author = "Barry, James  and
+      Wagner, Joachim  and
+      Cassidy, Lauren  and
+      Cowap, Alan  and
+      Lynn, Teresa  and
+      Walsh, Abigail  and
+      {\'O} Meachair, M{\'\i}che{\'a}l J.  and
+      Foster, Jennifer",
+    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.lrec-1.511",
+    pages = "4774--4788",
+    abstract = "The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.",
+}
+```