cleanlab
OpenRefine
cleanlab | OpenRefine | |
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69 | 45 | |
8,673 | 10,498 | |
6.0% | 0.8% | |
9.4 | 9.7 | |
3 days ago | 5 days ago | |
Python | Java | |
GNU Affero General Public License v3.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
cleanlab
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[Research] Detecting Annotation Errors in Semantic Segmentation Data
We have feely open-sourced our new method for improving segmentation data, published a paper on the research behind it, and released a 5-min code tutorial. You can also read more in the blog if you'd like.
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[R] Automated Quality Assurance for Object Detection Datasets
We’ve open-sourced one line of code to find errors in any object detection dataset via Cleanlab Object Detection, which can utilize any existing object detection model you’ve trained.
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[Research] Detecting Errors in Numerical Data via any Regression Model
If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data.
- Detecting Errors in Numerical Data via Any Regression Model
- cleanlab v2.5 now supports all major ML tasks (adds regression, object detection, and image segmentation)
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Automated Data Quality at Scale
Sharing some context here: in grad school, I spent months writing custom data analysis code and training ML models to find errors in large-scale datasets like ImageNet, work that eventually resulted in this paper (https://arxiv.org/abs/2103.14749) and demo (https://labelerrors.com/).
Since then, I’ve been interested in building tools to automate this sort of analysis. We’ve finally gotten to the point where a web app can do automatically in a couple of hours what I spent months doing in Jupyter notebooks back in 2019—2020. It was really neat to see the software we built automatically produce the same figures and tables that are in our papers.
The blog post shared here is results-focused, talking about some of the data and dataset-level issues that a tool using data-centric AI algorithms can automatically find in ImageNet, which we used as a case study. Happy to answer any questions about the post or data-centric AI in general here!
P.S. all of our core algorithms are open-source, in case any of you are interested in checking out the code: https://github.com/cleanlab/cleanlab
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Enhancing Product Analytics and E-commerce Business
Cleanlab Studio offers a user-friendly interface that allows you to visualize and review the identified issues in your dataset. You can easily explore the detected errors and make corrections with confidence. It's a hassle-free solution that can save you valuable time and improve your overall e-commerce operations. If you'd like more details you can check this article out.
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Databricks users can now automatically correct data and improve ML models
I thought this community might find it very useful that Databricks has partnered with Cleanlab to bring automated data correction and ML model improvement for both structured and unstructured datasets to all Databricks users.
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[R] Automated Checks for Violations of Independent and Identically Distributed (IID) Assumption
I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset.
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[P] Datalab: A Linter for ML Datasets
I recently published a blog introducing Datalab and an open-source Python implementation that is easy-to-use for all data types (image, text, tabular, audio, etc). For data scientists, I’ve made a quick Jupyter tutorial to run Datalab on your own data.
OpenRefine
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Ask HN: What Underrated Open Source Project Deserves More Recognition?
"OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data." https://openrefine.org/
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What you need to know about the future of Mozilla Hubs
Yes, let's hope! The strategy has worked out sometimes - Google shut down 'Google Refine' 10 years ago, it got turned into 'Open Refine', last update 2 months ago. https://github.com/OpenRefine/OpenRefine
It's a hugely useful tool if you're working with messy Excel-scale data, i.e., most biologists or social scientists.
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OpenRefine
It seems to be pure JS with jQuery: https://github.com/OpenRefine/OpenRefine/blob/master/main/we...
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java string equals returns false, even for identical strings
EDIT: trim() does not remove unicode 0x200b (unicode character for zero width space). https://github.com/OpenRefine/OpenRefine/issues/5105 is worth a read.
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UIUC MCS - CS 513 Review - Theory and Practice of Data Cleaning
There were six homework assignments. In order they were Regular Expressions, OpenRefine, Datalog, SQL, Provenance, and Python. None of these assignments took more than two to three hours to complete. They all were basic implementation and programming assignments with autograders.
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"We have great datasets"
Open Refine will get you about 70% there. It's FOSS
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Is there any tools to streamline data cleaning process?
I’ve heard good things about https://openrefine.org/
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What is the best approach to removing duplicate person records if the only identifier is person firstname middle name and last name? These names are entered in varying ways to the DB, thus they are free-fromatted.
It's not suited to SQL, use Open Refine or python fuzzywuzzy.
What are some alternatives?
alibi-detect - Algorithms for outlier, adversarial and drift detection
CQEngine - Ultra-fast SQL-like queries on Java collections
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
visidata - A terminal spreadsheet multitool for discovering and arranging data
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
LightAdmin - [PoC] Pluggable CRUD UI library for Java web applications
labelflow - The open platform for image labelling
Smooks - Extensible data integration Java framework for building XML and non-XML fragment-based applications
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Jimfs - An in-memory file system for Java 7+
SSL4MIS - Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
JBake - Java based open source static site/blog generator for developers & designers.