duckling VS cinder

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 It's entity tagging but a specialised form (especially when dealing with historical documents where you may need Gregorian and Julian calendars).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

Meta is actually contributing directly to upstream cpython. If you really wanted to, the internal fork is also open source: https://github.com/facebookincubator/cinder

Just one example: last year Meta open-sourced Cinder, which powers Instagram and provides sizeable speedups compared to CPython.

I used to work on the Cinder JIT and can help document any passes you find interesting or confusing.

You might enjoy Cinder then. It's based on CPython so it is nearly 100% compatible.

https://github.com/facebookincubator/cinder/

Disclaimer: I used to work on it.

I think Cinder's Static Python, which also performs runtime type checking, is more ambitious. Though it's not production ready yet.

Here's a fork that implements that https://github.com/facebookincubator/cinder - it might be nice to one day get that up streamed but obviously it'll be controversial and it certainly needs more time to bake. Hopefully at some point we can make it a pip installable extension though.

The other thing that was interesting to me, was the potential of type annotations to help make for a faster, safer experience on the compiler end of things. One example is seen in Meta’s Cinder project, on the docs it explains how typing can be used to reduce the number of steps for the compiler ([cinder/static_python.rst at cinder/3.8 · facebookincubator/cinder · GitHub](https://github.com/facebookincubator/cinder/blob/cinder/3.8/CinderDoc/static_python.rst)), making it more effective.