Update README.md

README.md

@@ -18,18 +18,15 @@ This model is initialized with the base BERT model (uncased, 110M parameters), [
 Please see the [casehold repository](https://github.com/reglab/casehold) for scripts that support computing pretrain loss and finetuning on Legal-BERT for classification and multiple choice tasks described in the paper: Overruling, Terms of Service, CaseHOLD.
 
 ### Citation
-```
-@inproceedings{zhengguha2021,
-		title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
-		author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
-		year={2021},
-		eprint={2104.08671},
-		archivePrefix={arXiv},
-		primaryClass={cs.CL},
-		booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law},
-		publisher={Association for Computing Machinery},
-		note={(in press)}
-}
-```
+	@inproceedings{zhengguha2021,
+			title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
+			author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
+			year={2021},
+			eprint={2104.08671},
+			archivePrefix={arXiv},
+			primaryClass={cs.CL},
+			booktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law},
+			publisher={Association for Computing Machinery}
+	}
 
 Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset. In *Proceedings of the 18th International Conference on Artificial Intelligence and Law (ICAIL '21)*, June 21-25, 2021,  São Paulo, Brazil. ACM Inc., New York, NY, (in press). arXiv: [2104.08671 \\[cs.CL\\]](https://arxiv.org/abs/2104.08671).