duckling VS ugrep

For the reasons others have said I don't see it replacing 'traditional' scraping soon. But I am looking forward to it replacing current methods of extracting data from the scraped content.

I've been using Duckling [0] for extracting fuzzy dates and times from text. It does a good job but I needed a custom build with extra rules to make that into a great job. And that's just for dates, 1 of 13 dimensions supported. Being able to use an AI that handles them with better accuracy will be fantastic.

Does a specialised model trained to extract times and dates already exist? It's entity tagging but a specialised form (especially when dealing with historical documents where you may need Gregorian and Julian calendars).

[0] https://github.com/facebook/duckling

Ah, sorry: https://github.com/facebook/duckling

To understand what relative distance and direction is indicated from the reference point, I'd look into something like Facebook & Wit.AI's Duckling, and a custom classifier to identify if it's on the reference point ("corner of"), or some distance from ("200 meters southwest"). If you can parse out a distance and direction, then it's all logic to plot the point.

It also powers the backend of Wit.ai which FB owns. Wit's open-source entity parser, duckling, is written entirely in Haskell. https://github.com/facebook/duckling

Our RandomPicForLater intent will have one slot called reminderTime and will be of type @duckling.time. Duckling is a library that extracts entities from text, and it is one of the tools used in JAICP for this purpose. Entity types in Duckling are called dimensions and there's a number of them built in, among them is Time which suits us perfectly since we need to ask users when they want us to schedule a post for and then parse a text input into a datetime object.

I'm working on a pretty interesting project right now and I'm having different results depending on the build tool used: with cabal, the test suite fails but it passes with stack.

Try downloading the v0.2.0.0 release, extracting it somewhere, opening that location in powershell, and running these commands:

Duckling (https://github.com/facebook/duckling) is a library for parsing text into structured data.

If you really want high precision, Duckling is a good project to check out https://github.com/facebook/duckling

I switched from from ripgrep to ugrep and never looked back. It's just as fast, but also comes with fuzzy matching (which is super useful), a TUI (useful for code reviews), and can also search in PDFs, archives, etc.

The optional Google search syntax also very convenient.

https://ugrep.com

Installation and user guide: ugrep.com (no ads, no cookies, just plain HTML in a GitHub page)

Not heard of ugrep? Read the wiki on GitHub.

These ripgrep announcements are getting a bit old, don't you think? Ripgrep hasn't improved or added new features since 2016. There are other fast alternatives with a lot more features, like ugrep and qgrep for example. Ugrep has fuzzy regex pattern search, archive search (even nested archives!), Boolean search queries like Google, interactive query TUI, hexdumps, and is compatible with GNU grep (ripgrep is not).

It's the first time I hear about the ugrep. Would be nice to compare it with ripgrep, since both provide benchmark tables listing their tool at the top :D. For my everyday use speed doesn't matter much, as well as interactive mode seems useless (YMMV). So I'm staying with ripgrep for now.

ugrep has full SSE/AVX support

https://github.com/Genivia/ugrep/blob/a3acf863803a755ff8da8c...