Generalized Entity Matching with Machop

Real-world applications frequently seek to solve a general form of the Entity Matching (EM) problem to find associated entities. Such scenarios, which we call Generalized Entity Matching (GEM), include matching jobs to candidates in job targeting, matching students with courses in online education, matching products with user reviews on e-commercial websites, and beyond. These tasks impose new requirements such as matching data entries with diverse formats or having a flexible and semantics-rich matching definition. Scenarios like these are    

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Haystack: Finding passion in NLP

Big language models are a new trend that is about to launch and influence NLP and search engines. NLP with advances in Deep Learning is getting behind fascinating use cases, like question answering in healthcare and finance. Haystack platform enables you to create flexible search pipelines, starting with document conversions (say, pdf to text), extracting metadata useful for search & filter later, proceeding to semantic indexing and similarity matching. You can combine sparse retrieval with dense search, all    

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