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  # Dataset Card for eoir_privacy
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  ## Table of Contents
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  ### Dataset Summary
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- ### Supported Tasks and Leaderboards
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  ### Languages
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  ## Dataset Structure
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  "label" : whether to use a pseudonym in filling masks
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- ### Data Fields
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  ### Data Splits
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- train, validation
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  ## Dataset Creation
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  #### Annotation process
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  #### Who are the annotators?
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  ### Personal and Sensitive Information
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  Data may be biased due to its origin in U.S. immigration courts.
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- ### Other Known Limitations
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- ## Additional Information
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- ### Dataset Curators
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  ### Licensing Information
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  ### Citation Information
 
 
 
 
 
 
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  # Dataset Card for eoir_privacy
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  ## Table of Contents
 
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  ### Dataset Summary
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+ This dataset mimics privacy standards for EOIR decisions. It is meant to help learn contextual data sanitization rules to anonymize potentially sensitive contexts in crawled language data.
 
 
 
 
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  ### Languages
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+ English
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  ## Dataset Structure
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  "label" : whether to use a pseudonym in filling masks
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  }
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  ### Data Splits
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+ train 75%, validation 25%
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  ## Dataset Creation
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  #### Annotation process
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+ Annotations (i.e., pseudonymity decisions) were made by the EOIR court. We use regex to identify if a pseudonym was used to refer to the applicant/respondent.
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  #### Who are the annotators?
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+ EOIR judges.
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  ### Personal and Sensitive Information
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  Data may be biased due to its origin in U.S. immigration courts.
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  ### Licensing Information
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+ CC-BY-NC
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  ### Citation Information
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+ ```
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+ @article{hendersonkrass2022pileoflaw,
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+ title={Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
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+ author={Henderson*, Peter and Krass*, Mark and Zheng, Lucia and Guha, Neel and Manning, Christopher and Jurafsky, Dan and Ho, Daniel E},
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+ year={2022}
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+ }
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+ ```