dedupe
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dedupe | orange | |
---|---|---|
9 | 27 | |
3,973 | 4,611 | |
1.1% | 1.9% | |
7.1 | 9.6 | |
about 1 month ago | 4 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dedupe
- Using deep learning for Fuzzy Matching
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String distance based network for fuzzy matching?
I think this problem is known as data deduplication, in particular, entity deduplication. I googled a bit and it seems approaches vary from manual deduplication to some sort of active learning (if I am not mistaken). I am also curios if pre-trained transformer-based cross encoders can provide any good results (they are trained on sentences I think, but may be worth a try). Another problem here is how to measure progress (compare different approaches)?
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What's the toughest DE problem you faced in your work career?
I've had a good experience in the past with the dedupe package for these type activities. Unsure if it works for out-of-core type situations though, as my data set fit easily into memory.
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Model detects duplicate records
Data deduplication is a super common problem, so it's useful experience to work on it. It's generally useful for companies, but I don't think it could be sold as a product unless is solving a very complicated, domain-specific de-duping problem. Otherwise, there are generic, open source de-duping tools such as: dedupe. It sounds like your model is similar to that.
- [D] Suggestions for large-scale company name standardization?
- Entity Resolution with Magniv
- How to do fuzzy matching in Redshift? A Python UDF, for example?
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[OC] Media bias? US Sunday news shows book Republicans more than Democrats: Three of the five top Sunday news shows, altogether watched by almost 8 million people weekly, featured Republican partisans more often than Democrats in episodes aired this year through Oct. 31.
Tools used: Python to scrape guest lists, dedupeio to better identify guests, Google Sheets to store and analyze the data, and Datawrapper to make the charts.
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Does there exist a python package that clears the dataset/columns in terms of exact and similar duplicates?
Try https://github.com/dedupeio/dedupe
orange
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Hierarchical Clustering
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
- Orange Data Mining
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The Graph of Wikipedia [video]
For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.
[1] https://orangedatamining.com/
[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Taxonomy Management?
First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.
- Orange: Open-source machine learning and data visualization
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
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Why don't more people use Altair for python Visualizations instead of Plotly?
You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.
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Advice on Transitioning to Data Science/ML/AI without Coding Experience
You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.
- Has anybody used Orange?
What are some alternatives?
splink - Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends
glue - Linked Data Visualizations Across Multiple Files
imgdupes - Identifying and removing near-duplicate images using perceptual hashing.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
bees - Best-Effort Extent-Same, a btrfs dedupe agent
RDKit - The official sources for the RDKit library
pyDenStream - Implementation of the DenStream algorithm in Python.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
hazelcast-python-client - Hazelcast Python Client
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
relevanceai - Home of the AI workforce - Multi-agent system, AI agents & tools
NumPy - The fundamental package for scientific computing with Python.