dataqa

Labelling platform for text using weak supervision. (by dataqa)

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dataqa reviews and mentions

Posts with mentions or reviews of dataqa. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-09.
  • [D] Looking for open source projects to contribute
    15 projects | /r/MachineLearning | 9 Jan 2022
    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).
  • [P]: Extract and label data from Wikipedia with DataQA
    1 project | /r/u_dataqa_ai | 2 Dec 2021
    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:
  • Show HN: DataQA – now possible to link entities to large ontologies
    1 project | news.ycombinator.com | 25 Oct 2021
    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.
  • [P] Using rules to speed up labelling by 2x
    1 project | /r/MachineLearning | 1 Oct 2021
    The tool I developed and used for this problem: https://github.com/dataqa/dataqa
  • The First Rule of Machine Learning: Start Without Machine Learning
    1 project | news.ycombinator.com | 22 Sep 2021
    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
    1 project | news.ycombinator.com | 22 Sep 2021
  • DataQA: the new Python app to do rules-based text annotation
    1 project | /r/Python | 13 Sep 2021
    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.
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dataqa/dataqa is an open source project licensed under GNU General Public License v3.0 only which is an OSI approved license.

The primary programming language of dataqa is JavaScript.


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