dataqa
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dataqa | kaggle-environments | |
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7 | 55 | |
245 | 274 | |
- | 1.8% | |
6.2 | 6.6 | |
almost 2 years ago | about 1 month ago | |
JavaScript | Jupyter Notebook | |
GNU General Public License v3.0 only | Apache License 2.0 |
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.
dataqa
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[D] Looking for open source projects to contribute
Hey, I am the creator and (only contributor today) of open-source https://github.com/dataqa/dataqa, a Python library to explore and annotate documents. It uses weak supervision, is based on spacy, and has a lot of opportunities to add more deep learning and ML functionality. I can guide you through it :-). This would be a great opportunity to be first and lead contributor of an open-source library (outside the creator).
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[P]: Extract and label data from Wikipedia with DataQA
I recently added a new feature to DataQA (https://github.com/dataqa/dataqa) to be able to extract entities from Wikipedia. All you need to do is upload a file with Wikipedia urls:
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Show HN: DataQA – now possible to link entities to large ontologies
The open-source project is here: https://github.com/dataqa/dataqa. I have just released a feature which I have been working on for a while to solve a problem which I've seen a lot in industry: how to map entities found in text to large knowledge base ontologies.
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[P] Using rules to speed up labelling by 2x
The tool I developed and used for this problem: https://github.com/dataqa/dataqa
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The First Rule of Machine Learning: Start Without Machine Learning
I have seen first hand at small and large companies how problems have been tackled with ML without trying a simple rule or heuristic first. And then, further down the line, the system has been compared to a few business rules put together, to find that the difference in performance did not explain the deployment of an ML system in the first place.
It's true that if your rules grow in complexity, this might make it harder to maintain, but the good thing about rules is that they tend to be fully explainable, and they can be encoded by domain experts. So the maintenance of such a system does not need to be done exclusively by an ML engineer anymore.
Here is where I insert my plug: I have developed a tool to create rules to solve NLP problems: https://github.com/dataqa/dataqa
- Show HN: Rules-based labelling tool for NLP
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DataQA: the new Python app to do rules-based text annotation
After working in ML for more than a decade, I became frustrated over time with the lack of tools to create baselines using simple rules and heuristics. It is well known that most business problems out there can achieve decent baselines using only heuristics. This is why I have developed DataQA (https://github.com/dataqa/dataqa), which uses NLP rules to do common NLP annotation tasks, such as multiclass classification or named entity recognition.
kaggle-environments
- Data Science Roadmap with Free Study Material
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Help needed! My first hackathon
If you are interested in Data Science, you may want to look at Kaggle competitions. https://www.kaggle.com/competitions
- What's a statistical / research methodology, that's not usually taught in grad programs, that you think more IO's should be aware about?
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Freaking out about how I’m inexperienced to land an internship and eventually a job
Secondly, if you feel like you do not have enough skills or a lack of practice answering problem statements, there are a lot of good websites where you can find interesting projects. I would recommend starting participating in some Kaggle competitions or download some free Google datasets and start playing with them.
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Capitalism provides half-assed solutions to extinction-level problems caused by capitalism
For reference: Kaggle is a Google product. You can see the list of current competitions here.
- Where can neural networks take me? - Semi-existential crisis
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What Can I Do With My Time as a Substitute for Strategy Computer Games?
You could try Kaggle competitions, or participating in forecasting markets (as you stated) is another option. You don't need any specific skill set to be a forecaster, the rules of the bet are stipulated and from there it's just based on your ability to predict the outcome. You could also try your hand at investing in the stock market, or try and make money betting on sports games. If you're very good at this stuff I'm sure you can make a lot of money doing it. The thing to keep in mind is that generally video games are much much easier than real life
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What is the best advanced professional certification for Data Science/ML/DL/MLOps?
As to the specifics of your projects, that's up to you. Try browsing Kaggle; check out some of the work we have on The Pudding; check out some journalism examples to see what you can try to build on or improve.
- Suggestions for projects on kaggle for cv?
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Hi! Im doing research on AI innovation. Does anybody know any specific platform where I can learn/understand and get case studies or on-going projects that companies are implementing? Thanks for your help!
You might want to look at kaggle competitions.
What are some alternatives?
diffgram - The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.
CKAN - CKAN is an open-source DMS (data management system) for powering data hubs and data portals. CKAN makes it easy to publish, share and use data. It powers catalog.data.gov, open.canada.ca/data, data.humdata.org among many other sites.
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
stable-baselines - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
general
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
docarray - Represent, send, store and search multimodal data
poutyne - A simplified framework and utilities for PyTorch
datasci-ctf - A capture-the-flag exercise based on data analysis challenges
habitat-sim - A flexible, high-performance 3D simulator for Embodied AI research.
dremio-oss - Dremio - the missing link in modern data