dataqa
habitat-lab
Our great sponsors
dataqa | habitat-lab | |
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7 | 3 | |
245 | 1,706 | |
- | 6.2% | |
6.2 | 9.1 | |
almost 2 years ago | 5 days ago | |
JavaScript | Python | |
GNU General Public License v3.0 only | MIT 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.
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.
habitat-lab
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[D] Looking for open source projects to contribute
There are plenty of them out there. I spend a lot of time contributing to open source projects like Habitat-Sim https://github.com/facebookresearch/habitat-sim and Habitat-Lab https://github.com/facebookresearch/habitat-lab which have a ton of open issues and code maintaince stuff that we would welcome contributions of.
- Accelerate PPO training
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Facebook AI Introduces Habitat 2.0: Next-Generation Simulation Platform Provides Faster Training For AI Agents With Tactile Perception
Github: https://github.com/facebookresearch/habitat-lab
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