|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Libpostal||3,810||2 months ago||309||mit||C|
|A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data.|
|Dedupe||3,708||39||10||7 months ago||174||February 17, 2023||73||mit||Python|
|:id: A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.|
|Splink||870||2||2 days ago||119||November 14, 2023||167||mit||Python|
|Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends|
|Recordlinkage||808||9||3||4 months ago||23||July 20, 2023||57||bsd-3-clause||Python|
|A powerful and modular toolkit for record linkage and duplicate detection in Python|
|Csvdedupe||395||4 years ago||21||other||Python|
|:id: Command line tool for deduplicating CSV files|
|Data Matching Software||329||12 days ago||8|
|A list of free data matching and record linkage software.|
|Dedupe Examples||306||2 years ago||7||mit||Python|
|:id: Examples for using the dedupe library|
|Spark Lucenerdd||127||2 days ago||39||June 02, 2021||34||apache-2.0||Scala|
|Spark RDD with Lucene's query and entity linkage capabilities|
|Entity Embed||98||2 years ago||6||July 16, 2021||mit||Jupyter Notebook|
|PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.|
dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data.
dedupe will help you:
dedupe takes in human training data and comes up with the best rules for your dataset to quickly and automatically find similar records, even with very large databases.
If you or your organization would like professional assistance in working with the dedupe library, Dedupe.io LLC offers consulting services. Read more about pricing and available services here.
A cloud service powered by the dedupe library for de-duplicating and finding matches in your data. It provides a step-by-step wizard for uploading your data, setting up a model, training, clustering and reviewing the results.
If you only want to use dedupe, install it this way:
pip install dedupe
Once you have virtualenvwrapper set up,
mkvirtualenv dedupe git clone https://github.com/dedupeio/dedupe.git cd dedupe pip install -e . --config-settings editable_mode=compat pip install -r requirements.txt
If these tests pass, then everything should have been installed correctly!
Afterwards, whenever you want to work on dedupe,
Unit tests of core dedupe functions
python -m pip install -e ./benchmarks python benchmarks/benchmarks/canonical.py
Using Record Linkage
python -m pip install -e ./benchmarks python benchmarks/benchmarks/canonical_matching.py
Dedupe is based on Mikhail Yuryevich Bilenko's Ph.D. dissertation: Learnable Similarity Functions and their Application to Record Linkage and Clustering.
If something is not behaving intuitively, it is a bug, and should be reported. Report it here
Copyright (c) 2022 Forest Gregg and Derek Eder. Released under the MIT License.
Third-party copyright in this distribution is noted where applicable.
If you use Dedupe in an academic work, please give this citation:
Forest Gregg and Derek Eder. 2022. Dedupe. dedupeio/dedupe.