Update README.md

README.md

@@ -10,7 +10,7 @@ This model is pretrained from scratch for 2M steps on the MLM and NSP objective,
 The model also uses a custom domain-specific legal vocabulary. The vocabulary set is constructed using [SentencePiece](https://arxiv.org/abs/1808.06226) on a subsample (approx. 13M) of sentences from our pretraining corpus, with the number of tokens fixed to 32,000.
 
 ### Usage
-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.
+Please see the [casehold repository](https://github.com/reglab/casehold) for scripts that support computing pretrain loss and finetuning on Custom Legal-BERT for classification and multiple choice tasks described in the paper: Overruling, Terms of Service, CaseHOLD.
 
 ### Citation
 ```
@@ -27,4 +27,4 @@ Please see the [casehold repository](https://github.com/reglab/casehold) for scr
 }
 ```
 
-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).
+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).