Update reference

README.md

@@ -10,8 +10,6 @@ language:
 
 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2022-154m) for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model.
 
-## Following metrics are achieved 
-
 | **Dataset**   |   **Accuracy** |   **Macro-F1** | **Weighted-F1** |
 |:----------|-----------:|-----------:|--------------:|
 | hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter      |     0.5831 |     0.5646 |        0.548  |
@@ -46,14 +44,20 @@ model.predict('I love everybody :)')
 ```
 
 
-### Model based on:
+### Reference paper - Model based on:
 ```
-@misc{antypas2023robust,
-      title={Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation}, 
-      author={Dimosthenis Antypas and Jose Camacho-Collados},
-      year={2023},
-      eprint={2307.01680},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@inproceedings{antypas-camacho-collados-2023-robust,
+    title = "Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation",
+    author = "Antypas, Dimosthenis  and
+      Camacho-Collados, Jose",
+    booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.woah-1.25",
+    pages = "231--242",
+    abstract = "The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from these dataset-specific biases. In this paper, we perform a large-scale cross-dataset comparison where we fine-tune language models on different hate speech detection datasets. This analysis shows how some datasets are more generalizable than others when used as training data. Crucially, our experiments show how combining hate speech detection datasets can contribute to the development of robust hate speech detection models. This robustness holds even when controlling by data size and compared with the best individual datasets.",
 }
+
 ```