BLINK
Entity Linker solution (by facebookresearch)
duckling
Language, engine, and tooling for expressing, testing, and evaluating composable language rules on input strings. (by facebook)
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BLINK | duckling | |
---|---|---|
2 | 13 | |
1,114 | 4,015 | |
- | 0.6% | |
0.0 | 0.0 | |
7 months ago | 2 months ago | |
Python | Haskell | |
MIT License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
BLINK
Posts with mentions or reviews of BLINK.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-02-01.
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How to write a Scoring Script
I recently used a machine learning model from Github to predict different sets of documents. But what I am trying to do now is create a scoring script, which takes the same testing datasets but with different models. The output should have precision,recall, f1, etc. (something like that)
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SpaCy v3.0 Released (Python Natural Language Processing)
there is also BLINK by Facebook
https://github.com/facebookresearch/BLINK
duckling
Posts with mentions or reviews of duckling.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-25.
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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?
When comparing BLINK and duckling you can also consider the following projects:
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python