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--- |
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license: mit |
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--- |
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# Model description |
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LegalBert is a BERT-base-cased model fine-tuned on a subset of the `case.law` corpus. Further details can be found in this paper: |
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[A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering](http://ceur-ws.org/Vol-2645/paper5.pdf) |
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Nils Holzenberger, Andrew Blair-Stanek and Benjamin Van Durme |
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*Proceedings of the 2020 Natural Legal Language Processing (NLLP) Workshop, 24 August 2020* |
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# Usage |
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``` |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained("jhu-clsp/LegalBert") |
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tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/LegalBert") |
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``` |
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# Citation |
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``` |
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@inproceedings{holzenberger20dataset, |
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author = {Nils Holzenberger and |
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Andrew Blair{-}Stanek and |
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Benjamin Van Durme}, |
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title = {A Dataset for Statutory Reasoning in Tax Law Entailment and Question |
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Answering}, |
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booktitle = {Proceedings of the Natural Legal Language Processing Workshop 2020 |
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co-located with the 26th {ACM} {SIGKDD} International Conference on |
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Knowledge Discovery {\&} Data Mining {(KDD} 2020), Virtual Workshop, |
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August 24, 2020}, |
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series = {{CEUR} Workshop Proceedings}, |
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volume = {2645}, |
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pages = {31--38}, |
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publisher = {CEUR-WS.org}, |
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year = {2020}, |
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url = {http://ceur-ws.org/Vol-2645/paper5.pdf}, |
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} |
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``` |
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