
###  Legal-BERT
Model and tokenizer files for Legal-BERT model from [When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings](https://arxiv.org/abs/2104.08671).

### Training Data
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 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}
	}

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).

{"language": "en", "tags": ["legal"], "pipeline_tag": "fill-mask"}
transformers pytorch tf jax bert legal fill-mask en arxiv:2104.08671 endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #arxiv-2104.08671 #endpoints_compatible #region-us 


###  Legal-BERT
Model and tokenizer files for Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings.

### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (URL 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 the base BERT model (uncased, 110M parameters), 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 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.

@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\\]](URL

###  Legal-BERT
Model and tokenizer files for Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings. ### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (URL 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 the base BERT model (uncased, 110M parameters), 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 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.

@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\\]](URL
TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #arxiv-2104.08671 #endpoints_compatible #region-us 
 ###  Legal-BERT
Model and tokenizer files for Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings. ### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (URL 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 the base BERT model (uncased, 110M parameters), 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 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.

@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\\]](URL
passage: TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #arxiv-2104.08671 #endpoints_compatible #region-us 
###  Legal-BERT
Model and tokenizer files for Legal-BERT model from When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset of 53,000+ Legal Holdings.### Training Data
The pretraining corpus was constructed by ingesting the entire Harvard Law case corpus from 1965 to the present (URL 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 the base BERT model (uncased, 110M parameters), 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).