Hey HN!<p>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 can achieve decent baselines using only heuristics. So this is why I have just open-sourced DataQA, a rules-based labelling tool for NLP:<p><pre><code> - Quick labelling: You can create complex rules using regular expressions to help you label your text faster.
- Search engine: DataQA also ships with a search engine (local elasticsearch database) so you can search your documents.
- Easy installation: Only need to install a single python package!
- Easy use: upload your data as csv files.
- Privacy: No data ever leaves your computer.
</code></pre>
I'm hoping to get some feedback, and I'm open to hear about feature requests or ideas for extensions. I will be around to answer questions.
Looks great. I can't try it right now, but looking at the documentation I would suggest an alternative to CSV upload.<p>For larger documents CSV can be annoying. The line breaks needs to be escaped and commas need to be escaped. Pointing the application to a folder containing a corpus of text files is much easier.
It looks like this tool is intended to label _documents_ using rules/heuristics. That seems useful.<p>My desired use case is to label words or phrases (named entity recognition) - specifically for chemicals. It seems like this tool isn't designed for that. Am I understanding correctly?
Hi!
Interesting project, congrats!
How does it compare to <a href="https://calmcode.io/human-learn/introduction.html" rel="nofollow">https://calmcode.io/human-learn/introduction.html</a>
?