snorkel
A system for quickly generating training data with weak supervision
Science Score: 46.0%
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○CITATION.cff file
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○.zenodo.json file
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✓DOI references
Found 1 DOI reference(s) in README -
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13 of 83 committers (15.7%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (16.5%) to scientific vocabulary
Keywords
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Repository
A system for quickly generating training data with weak supervision
Basic Info
- Host: GitHub
- Owner: snorkel-team
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://snorkel.org
- Size: 279 MB
Statistics
- Stars: 5,880
- Watchers: 165
- Forks: 857
- Open Issues: 16
- Releases: 16
Topics
Metadata Files
README.md
Programmatically Build and Manage Training Data
Announcement
The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI application development platform based on the core ideas behind Snorkel—you can check it out here or join us in building it!
The Snorkel project started at Stanford in 2015 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to programmatically label, build, and manage training data.
To say that the Snorkel project succeeded and expanded beyond what we had ever expected would be an understatement. The basic goals of a research repo like Snorkel are to provide a minimum viable framework for testing and validating hypotheses. Four years later, we’ve been fortunate to do not just this, but to develop and deploy early versions of Snorkel in partnership with some of the world’s leading organizations like Google, Intel, Stanford Medicine, and many more; author over sixty peer-reviewed publications on our findings around Snorkel and related innovations in weak supervision modeling, data augmentation, multi-task learning, and more; be included in courses at top-tier universities; support production deployments in systems that you’ve likely used in the last few hours; and work with an amazing community of researchers and practitioners from industry, medicine, government, academia, and beyond.
However, we realized increasingly–from conversations with users in weekly office hours, workshops, online discussions, and industry partners–that the Snorkel project was just the very first step. The ideas behind Snorkel change not just how you label training data, but so much of the entire lifecycle and pipeline of building, deploying, and managing ML: how users inject their knowledge; how models are constructed, trained, inspected, versioned, and monitored; how entire pipelines are developed iteratively; and how the full set of stakeholders in any ML deployment, from subject matter experts to ML engineers, are incorporated into the process.
Over the last year, we have been building the platform to support this broader vision: Snorkel Flow, an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring, monitoring and analysis, and more, all of which integrate in a way that is greater than the sum of its parts–and that we believe makes ML truly faster, more flexible, and more practical than ever before.
Moving forward, we will be focusing our efforts on Snorkel Flow. We are extremely grateful for all of you that have contributed to the Snorkel project, and are excited for you to check out our next chapter here.
Quick Links
- Snorkel website
- Snorkel tutorials
- Snorkel documentation
- Snorkel community forum
- Snorkel mailing list
- Snorkel Twitter
Getting Started
The quickest way to familiarize yourself with the Snorkel library is to walk through the Get Started page on the Snorkel website, followed by the full-length tutorials in the Snorkel tutorials repository. These tutorials demonstrate a variety of tasks, domains, labeling techniques, and integrations that can serve as templates as you apply Snorkel to your own applications.
Installation
Snorkel requires Python 3.11 or later. To install Snorkel, we recommend using pip:
bash
pip install snorkel
or conda:
bash
conda install snorkel -c conda-forge
For information on installing from source and contributing to Snorkel, see our contributing guidelines.
Details on installing with conda
The following example commands give some more color on installing with `conda`. These commands assume that your `conda` installation is Python 3.11, and that you want to use a virtual environment called `snorkel-env`. ```bash # [OPTIONAL] Activate a virtual environment called "snorkel" conda create --yes -n snorkel-env python=3.11 conda activate snorkel-env # We specify PyTorch here to ensure compatibility, but it may not be necessary. conda install pytorch==1.1.0 -c pytorch conda install snorkel==0.9.0 -c conda-forge ```
A quick note for Windows users
If you're using Windows, we highly recommend using Docker (you can find an example in our [tutorials repo](https://github.com/snorkel-team/snorkel-tutorials/blob/master/Dockerfile)) or the [Linux subsystem](https://docs.microsoft.com/en-us/windows/wsl/faq). We've done limited testing on Windows, so if you want to contribute instructions or improvements, feel free to open a PR!
Discussion
Issues
We use GitHub Issues for posting bugs and feature requests — anything code-related. Just make sure you search for related issues first and use our Issues templates. We may ask for contributions if a prompt fix doesn't fit into the immediate roadmap of the core development team.
Contributions
We welcome contributions from the Snorkel community! This is likely the fastest way to get a change you'd like to see into the library.
Small contributions can be made directly in a pull request (PR).
If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion.
For ideas about what to work on, we've labeled specific issues as help wanted.
To set up a development environment for contributing back to Snorkel, see our contributing guidelines. All PRs must pass the continuous integration tests and receive approval from a member of the Snorkel development team before they will be merged.
Community Forum
For broader Q&A, discussions about using Snorkel, tutorial requests, etc., use the Snorkel community forum hosted on Spectrum. We hope this will be a venue for you to interact with other Snorkel users — please don't be shy about posting!
Announcements
To stay up-to-date on Snorkel-related announcements (e.g. version releases, upcoming workshops), subscribe to the Snorkel mailing list. We promise to respect your inboxes — communication will be sparse!
Follow us on Twitter @SnorkelAI.
Owner
- Name: Snorkel Team
- Login: snorkel-team
- Kind: organization
- Email: hello@snorkel.org
- Website: https://snorkel.org
- Repositories: 3
- Profile: https://github.com/snorkel-team
GitHub Events
Total
- Watch event: 129
- Fork event: 8
Last Year
- Watch event: 129
- Fork event: 8
Committers
Last synced: almost 3 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Alex Ratner | a****r@g****m | 694 |
| Henry Ehrenberg | h****g@o****m | 507 |
| Stephen Bach | s****h@g****m | 421 |
| Braden Hancock | h****n@g****m | 126 |
| Vincent Chen | v****n@u****m | 56 |
| Bryan He | b****e@s****u | 50 |
| Jaeho Shin | n****j@c****u | 38 |
| Jason Alan Fries | j****s@s****u | 37 |
| Jason Alan Fries | f****n@g****m | 28 |
| Paroma Varma | v****a@g****m | 27 |
| Catalin Voss | c****n@c****u | 23 |
| jasontlam | j****m@l****a | 22 |
| Vincent Chen | v****n@y****m | 19 |
| thodrek | t****k@s****u | 18 |
| Daniel Himmelstein | d****n@g****m | 15 |
| senwu | s****u@s****u | 13 |
| Manas Joglekar | b****a@g****m | 11 |
| David Nicholson | d****9@g****m | 7 |
| Felix Sonntag | f****g@o****m | 7 |
| Hang Yao | h****o@h****m | 7 |
| Humza Iqbal | h****9@g****m | 7 |
| Peter M. Landwehr | p****h@c****u | 5 |
| Páidí Creed | p****d@g****m | 5 |
| Shawn Roberts | r****3@g****m | 5 |
| Xiao Ling | x****g@l****o | 5 |
| regoldman | r****n@g****m | 4 |
| rsmith49 | r****h@s****i | 4 |
| Namit Chaturvedi | n****v@n****z | 4 |
| Luke Hsiao | l****o@u****m | 3 |
| dependabot[bot] | 4****]@u****m | 3 |
| and 53 more... | ||
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 60
- Total pull requests: 56
- Average time to close issues: 4 months
- Average time to close pull requests: 2 months
- Total issue authors: 50
- Total pull request authors: 26
- Average comments per issue: 4.22
- Average comments per pull request: 2.07
- Merged pull requests: 40
- Bot issues: 0
- Bot pull requests: 3
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- rjurney (4)
- yinxiangshi (3)
- hardianlawi (3)
- moscow25 (2)
- anjani-dhrangadhariya (2)
- pratikchhapolika (2)
- meidata (1)
- datduong (1)
- dmracek (1)
- ndenStanford (1)
- nsankar (1)
- gionanide (1)
- e-hossam96 (1)
- JayThibs (1)
- arfeen93 (1)
Pull Request Authors
- humzaiqbal (10)
- bhancock8 (9)
- rsmith49 (6)
- zexuan-zhou (3)
- dependabot[bot] (3)
- fpoms (3)
- asottile (2)
- kamelCased (2)
- anerirana (2)
- marekmodry (2)
- hardianlawi (2)
- jaiwiwjwjwisn (2)
- minhtuev (1)
- run3134 (1)
- zehua99 (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- pypi 37,236 last-month
-
Total dependent packages: 9
(may contain duplicates) -
Total dependent repositories: 69
(may contain duplicates) - Total versions: 36
- Total maintainers: 5
pypi.org: snorkel
A system for quickly generating training data with weak supervision
- Homepage: https://github.com/snorkel-team/snorkel
- Documentation: https://snorkel.readthedocs.io/
- License: Apache License 2.0
-
Latest release: 0.10.0
published almost 2 years ago
Rankings
Maintainers (5)
proxy.golang.org: github.com/snorkel-team/snorkel
- Documentation: https://pkg.go.dev/github.com/snorkel-team/snorkel#section-documentation
- License: apache-2.0
-
Latest release: v0.10.0
published almost 2 years ago
Rankings
conda-forge.org: snorkel
Snorkel is a system for programmatically building and managing training datasets to rapidly and flexibly fuel machine learning models. Users write programmatic operations to label, transform, and structure training datasets for machine learning, without needing to hand label any training data. Snorkel then uses modern, theoretically-grounded modeling techniques to clean and integrate the resulting training data.
- Homepage: https://snorkel.org
- License: Apache-2.0
-
Latest release: 0.9.9
published over 3 years ago
Rankings
Dependencies
- actions/stale v1 composite
- sphinx ==2.1.2
- sphinx_autodoc_typehints ==1.7.0
- sphinx_rtd_theme ==0.4.3
- pyspark ==3.2.2
- black >=22.3
- blis >=0.3.0
- dask >=2020.12.0
- dill >=0.3.0
- distributed >=2020.12.0
- flake8 >=3.7.0
- isort >=4.3.0
- munkres >=1.0.6
- mypy ==0.760
- networkx >=2.2
- numpy >=1.16.5
- pandas >=1.0.0
- protobuf >=3.19.5
- pydocstyle >=4.0.0
- pytest >=5.0.0,<6.0.0
- pytest-cov >=2.7.0
- pytest-doctestplus >=0.3.0
- scikit-learn >=0.20.2
- scipy >=1.2.0
- spacy >=2.1.0
- tensorboard >=2.9.1
- torch >=1.2.0
- tox >=3.13.0
- tqdm >=4.33.0
- munkres >=1.0.6
- networkx >=2.2
- numpy >=1.16.5
- pandas >=1.0.0
- scikit-learn >=0.20.2
- scipy >=1.2.0
- tensorboard >=2.9.1
- torch >=1.2.0
- tqdm >=4.33.0