6379 40262.\n"            "Is this your phone number: 734-413-1647?\n"            "Is this your email address: wdavis@example.net"        ),        deanonymizer_matching_strategy=combined_exact_fuzzy_matching_strategy,    ))    Are you Slim Shady? I found your card with number 4916 0387 9536 0861.    Is this your phone number: 313-666-7440?    Is this your email address: wdavis@example.netOf course, there is no perfect method and it is worth experimenting and finding the one best suited to your use case.Future works‚Äãbetter matching and substitution of fake values for real ones - currently the strategy is based on matching full strings and then substituting them. Due to the indeterminism of language models, it may happen that the value in the answer is slightly changed (e.g. John Doe -> John or Main St, New York -> New York) and such a substitution is then no longer possible. Therefore, it is worth adjusting the matching for your needs.PreviousData anonymization with Microsoft PresidioNextMulti-language anonymizationUse caseOverviewQuickstartUsing with LangChain Expression LanguageExtra knowledgeCustom deanonymization strategyFuture worksCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Open In Colab
Open In Colab ->: 6379 40262.\n"            "Is this your phone number: 734-413-1647?\n"            "Is this your email address: wdavis@example.net"        ),        deanonymizer_matching_strategy=combined_exact_fuzzy_matching_strategy,    ))    Are you Slim Shady? I found your card with number 4916 0387 9536 0861.    Is this your phone number: 313-666-7440?    Is this your email address: wdavis@example.netOf course, there is no perfect method and it is worth experimenting and finding the one best suited to your use case.Future works‚Äãbetter matching and substitution of fake values for real ones - currently the strategy is based on matching full strings and then substituting them. Due to the indeterminism of language models, it may happen that the value in the answer is slightly changed (e.g. John Doe -> John or Main St, New York -> New York) and such a substitution is then no longer possible. Therefore, it is worth adjusting the matching for your needs.PreviousData anonymization with Microsoft PresidioNextMulti-language anonymizationUse caseOverviewQuickstartUsing with LangChain Expression LanguageExtra knowledgeCustom deanonymization strategyFuture worksCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.