Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesAdaptersDebuggingDeploymentEvaluationFallbacksLangSmithRun LLMs locallyModel comparisonPrivacyData anonymization with Microsoft PresidioReversible anonymizationMulti-language anonymizationQA with private data protectionPydantic compatibilitySafetyMoreGuidesPrivacyData anonymization with Microsoft PresidioMulti-language anonymizationOn this pageMulti-language anonymizationMulti-language data anonymization with Microsoft PresidioUse case‚ÄãMulti-language support in data pseudonymization is essential due to differences in language structures and cultural contexts. Different languages may have varying formats for personal identifiers. For example, the structure of names, locations and dates can differ greatly between languages and regions. Furthermore, non-alphanumeric characters, accents, and the direction of writing can impact pseudonymization processes. Without multi-language support, data could remain identifiable or be misinterpreted, compromising data privacy and accuracy. Hence, it enables effective and precise pseudonymization suited for global operations.Overview‚ÄãPII detection in Microsoft Presidio relies on several components - in addition to the usual pattern matching (e.g. using regex), the analyser uses a model for Named Entity Recognition (NER) to extract entities such as:PERSONLOCATIONDATE_TIMENRPORGANIZATION[Source]To handle NER in specific languages, we utilize unique models from the spaCy library, recognized for its extensive selection covering multiple languages and sizes. However, it's not restrictive, allowing for integration of alternative frameworks such as Stanza or transformers when
Open In Colab
Open In Colab ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalChainsMemoryAgentsCallbacksModulesSecurityGuidesAdaptersDebuggingDeploymentEvaluationFallbacksLangSmithRun LLMs locallyModel comparisonPrivacyData anonymization with Microsoft PresidioReversible anonymizationMulti-language anonymizationQA with private data protectionPydantic compatibilitySafetyMoreGuidesPrivacyData anonymization with Microsoft PresidioMulti-language anonymizationOn this pageMulti-language anonymizationMulti-language data anonymization with Microsoft PresidioUse case‚ÄãMulti-language support in data pseudonymization is essential due to differences in language structures and cultural contexts. Different languages may have varying formats for personal identifiers. For example, the structure of names, locations and dates can differ greatly between languages and regions. Furthermore, non-alphanumeric characters, accents, and the direction of writing can impact pseudonymization processes. Without multi-language support, data could remain identifiable or be misinterpreted, compromising data privacy and accuracy. Hence, it enables effective and precise pseudonymization suited for global operations.Overview‚ÄãPII detection in Microsoft Presidio relies on several components - in addition to the usual pattern matching (e.g. using regex), the analyser uses a model for Named Entity Recognition (NER) to extract entities such as:PERSONLOCATIONDATE_TIMENRPORGANIZATION[Source]To handle NER in specific languages, we utilize unique models from the spaCy library, recognized for its extensive selection covering multiple languages and sizes. However, it's not restrictive, allowing for integration of alternative frameworks such as Stanza or transformers when