Update model card

README.md

@@ -5,20 +5,20 @@ Model and tokenizer files for Legal-BERT model from [When Does Pretraining Help?
 The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (https://case.law/). The size of this corpus (37GB) is substantial, representing 3,446,187 legal decisions across all federal and state courts, and is larger than the size of the BookCorpus/Wikipedia corpus originally used to train BERT (15GB).
 
 ### Training Objective
-This model is initialized with [bert-base-uncased](https://huggingface.co/bert-base-uncased) and trained for an additional 1M steps on the MLM and NSP objective, with tokenization and sentence segmentation adapted for legal text (cf. the paper).
+This model is initialized with the base BERT model (uncased, 110M parameters), [bert-base-uncased](https://huggingface.co/bert-base-uncased), and trained for an additional 1M steps on the MLM and NSP objective, with tokenization and sentence segmentation adapted for legal text (cf. the paper).
 
 ### 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.
 
 ### 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)}
-	}
+\t@inproceedings{zhengguha2021,
+\t\ttitle={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
+\t\tauthor={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
+\t\tyear={2021},
+\t\teprint={2104.08671},
+\t\tarchivePrefix={arXiv},
+\t\tprimaryClass={cs.CL},
+\t\tbooktitle={Proceedings of the 18th International Conference on Artificial Intelligence and Law},
+\t\tpublisher={Association for Computing Machinery},
+\t\tnote={(in press)}
+\t}