Update README.md

README.md

@@ -1,7 +1,9 @@
 # cardiffnlp/twitter-roberta-base-latest-tweet-emoji
 
-This is a [RoBERTa-base](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) model fine-tuned on [tweet-emoji](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
+This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 and finetuned for emoji classification (multiclass classification on 100 emojis) on the _TweetEmoji100_ dataset of [SuperTweetEval](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
+The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m).
 
+## Example
 ```python
 from transformers import pipeline
 text= "I’m tired of being sick.. it’s been four days dawg"
@@ -16,4 +18,16 @@ predictions[:5]
  {'label': '😤', 'score': 0.0710175409913063},
  {'label': '😑', 'score': 0.06665993481874466},
  {'label': '😫', 'score': 0.0662984848022461}]
+```
+
+## Citation Information
+Please cite the [reference paper](https://arxiv.org/abs/2310.14757) if you use this model.
+
+```bibtex
+@inproceedings{antypas2023supertweeteval,
+  title={SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research},
+  author={Dimosthenis Antypas and Asahi Ushio and Francesco Barbieri and Leonardo Neves and Kiamehr Rezaee and Luis Espinosa-Anke and Jiaxin Pei and Jose Camacho-Collados},
+  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
+  year={2023}
+}
 ```