Add reference paper

README.md

@@ -6,7 +6,8 @@ language:
 ---
 # Twitter-roBERTa-base for Irony Detection
 
-This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
+This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. 
+This model has integrated in to the [TweetNLP Python library](https://github.com/cardiffnlp/tweetnlp/).
 
 - Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
 - Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
@@ -84,4 +85,22 @@ Output:
 ```
 1) irony 0.914
 2) non_irony 0.086
+```
+
+### Reference
+```
+@inproceedings{barbieri-etal-2022-xlm,
+    title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond",
+    author = "Barbieri, Francesco  and
+      Espinosa Anke, Luis  and
+      Camacho-Collados, Jose",
+    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-1.27",
+    pages = "258--266"
+}
+
 ```