snorkel
cleanlab


snorkel | cleanlab | |
---|---|---|
6 | 70 | |
5,828 | 10,169 | |
0.0% | 3.2% | |
5.2 | 9.0 | |
10 months ago | 11 days ago | |
Python | Python | |
Apache License 2.0 | GNU Affero General Public License v3.0 |
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.
snorkel
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Harnessing Weak Supervision to Isolate Sign Language in Crowded News Videos
Hello everyone, we are trying to make a large dataset for Sign Language translation, inspired by BSL-1K [1]. As part of cleaning our collected videos, we use a nice technique for aggregating heuristic labels [2]. We thought it was interesting enough to share with people on here.
[1] https://www.robots.ox.ac.uk/~vgg/research/bsl1k/
[2] https://github.com/snorkel-team/snorkel
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
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Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
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[Discussion] Methods for enhancing high-quality dataset A with low-quality dataset
Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:
cleanlab
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Ask HN: Not a webdev, why are these sites so good?
https://cleanlab.ai/
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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.
What are some alternatives?
argilla - Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
labelflow - The open platform for image labelling
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
refinery - The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
alibi-detect - Algorithms for outlier, adversarial and drift detection
snorkel-tutorials - A collection of tutorials for Snorkel
SSL4MIS - Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

