stackage-content
duckling
| stackage-content | duckling | |
|---|---|---|
| 3 | 13 | |
| 10 | 4,019 | |
| - | 0.2% | |
| 6.8 | 0.0 | |
| 8 days ago | 3 months ago | |
| HTML | Haskell | |
| MIT License | BSD 3-clause "New" or "Revised" 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.
stackage-content
- Should Stack use Fedora 33 or Debian 10 binaries?
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GHC 9.2.5 is now available
You could try out stackage-content#122 before it gets merged. If LLVM is buggy on 9.2.5, I am sure the GHC devs would like to know.
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Running Duckling on Windows
So save this file to stack-setup-info.yaml and change the sevenzexe-info and sevenzdll-info fields to this:
duckling
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Experimental library for scraping websites using OpenAI's GPT API
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 It's entity tagging but a specialised form (especially when dealing with historical documents where you may need Gregorian and Julian calendars).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
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Automatisiert Kalendereinträge erstellen aus Mails mit Formatlosen Datumsangaben
Ah, sorry: https://github.com/facebook/duckling
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Transforming free-form geospatial directions into addresses - SOTA?
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.
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Programming languages endorsed for server-side use at Meta
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
- Data Cleaning using Machine Learning?
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Unsplash chatbot for Discord, Pt. 2: more ways to bring pictures to Discord
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.
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Dependencies difference between cabal and stack
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.
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Running Duckling on Windows
Try downloading the v0.2.0.0 release, extracting it somewhere, opening that location in powershell, and running these commands:
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[ANN] Duckling v0.2.0.0 released
Duckling (https://github.com/facebook/duckling) is a library for parsing text into structured data.
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Extract name:value relationships from plain text
If you really want high precision, Duckling is a good project to check out https://github.com/facebook/duckling
What are some alternatives?
stack - The Haskell Tool Stack
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
ctparse - Parse natural language time expressions in python
Giveme5W1H - Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?
syntaxdot - Neural syntax annotator, supporting sequence labeling, lemmatization, and dependency parsing.
Kornia - Geometric Computer Vision Library for Spatial AI
BLINK - Entity Linker solution
semantic-source - Parsing, analyzing, and comparing source code across many languages
projects - 🪐 End-to-end NLP workflows from prototype to production
rules - Durable Rules Engine
laserembeddings - LASER multilingual sentence embeddings as a pip package