Entity Resolution Collection

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🔮Psychic

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“:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.”

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🔮Psychic

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“Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends”

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⚡Thunder

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“Scalable master data management, identity resolution, entity resolution, and deduplication using ML”

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🔮Psychic

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“A powerful and modular toolkit for record linkage and duplicate detection in Python”

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🔮Psychic

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“1 line for thousands of State of The Art NLP models in hundreds of languages The fastest and most accurate way to solve text problems.”

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🔮Psychic

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“On-device Speech-to-Intent engine powered by deep learning”

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🔮Psychic

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“:id: Command line tool for deduplicating CSV files”

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🔮Psychic

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“:id: Examples for using the dedupe library”

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📦Normal

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“A list of free data matching and record linkage software. ”

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📦Normal

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“Recent trends of Entity Linking, Disambiguation, and Representation.”

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🔮Psychic

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“This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).”

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🔮Psychic

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“ReFinED is an efficient and accurate entity linking (EL) system.”

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🔮Psychic

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“Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. This produces a Streamlit app, with MLOps instrumentation.”

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⚡Thunder

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“An open source, high scalability toolkit in Java for Entity Resolution.”

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🔮Psychic

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“🔎 Finds fuzzy matches between CSV files”

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⚡Thunder

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“Entity resolution for Elasticsearch.”

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📦Normal

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“PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.”

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🔮Psychic

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“A convenient way to link, deduplicate, aggregate and cluster data(frames) in Python using deep learning”