jigsaw | Simple jigsaw puzzle game using JS and canvas | Game Engine library

I am working on the bert-base-mutilingual-uncased model but when I try to set the TOKENIZER in the config it throws an OSError.

I want to know if it is valid to define a css variable that starts with a number like this,

I'm working on DOM Playground, where I'm editing the page's style strictly using Javascript (I know its inefficient – its for an assignment). DOM Playground

For the most part, I'm almost done. I just need to add list elements in the unordered list that is commented (I'll share the code below). There is an array called resources, which holds five objects with the following properties – title, href, and innerHTML.

I'm trying to create a forEach function, that runs through the resources list, and inserts a list item (li) with the same href, title, and innerHTML as the objects in the array. So for example, resources[0] =

I'm just trying to get the basic Hello World (project Trial0) application running using:

• Eclipse Version: 2020-12 (4.18.0)
• With SWT 4.18 (Linux, GTK)
• with the Java Compiler set to 15

Following the (kinda ancient, it's about JDK 1.4 and SWT 3.1) description in Developing SWT applications using Eclipse, I have imported the SWT project into Eclipse:

1. Go to https://download.eclipse.org/eclipse/downloads/index.html#Stable_Builds
2. Click on "4.18" to reach https://download.eclipse.org/eclipse/downloads/drops4/R-4.18-202012021800/
3. Scroll down until you reach "SWT Binary and Source" and download the zip swt-4.18-gtk-linux-x86_64.zip.
4. The zip includes 'swt.jar' (which includes the '.so' files). Launch Eclipse's "File > Import > General > Existing Projects into Workspace", the select the above zip as "Archive File"

I immediately hit a modularization/Jigsaw snag in a project that uses the imported SWT project. The compiler apparently is not allowed to see the SWT classes, which are not modularized:

"The package org.eclipse.swt.widgets is not accessible"

In this code:

I'm using the Online Retail dataset from the UCI Machine Learning Repository in pandas, and I'm setting a multi-index consisting in CustomerID as first level, and InvoiceNo as second level. Here's the code:

so I'm trying to install outline VPN on my debian machine.

The code I'm using to install is

I am migrating a codebase to Java 11 and JPMS / Jigsaw and am having some trouble with mocking.

This is the test I am trying to run.

I have a simple 2-column layout with 3 sections. Depending on a media query, I want to change the order of them - for this I am using flex order.

This works fine, except I get my narrow sidebar section starting at the end of the first section, or similar to this. Is there a way I can get them to position more like jigsaw pieces?

Fiddle example of issue: https://jsfiddle.net/an7m3yvs/

HTML:

So far I have used Keras Tensorflow to model image processing, NLP, time series prediction. Usually in case of having labels with multiple entries, so multiple categories the task was always to just predict to which class the sample belongs. So for example the list of possible classes was [car, human, airplane, flower, building]. So the final prediction was to which class the sample belongs - giving probabilities for each class. Usually in terms of a very confident prediction one class had a very high probability and the others very low.

Now I came across this Kaggle challenge: Toxic Comment Classification Challenge and in specific this implementation. I thought that this is a multi-label classification problem, as one sample can belong to different classes. And indeed, when I check the final prediction:

I can see that the first sample prediction has a very high probability for both toxic and obscene. With my knowledge so far when I applied a standard model to predict a class I would have predicted the probability to which of this class the sample belongs. So either class 1 or 2 or .... so I would have had - in case of a confident prediciton - a high probability for class toxic and low for the others - or in case of unconfident prediciton - 0.4x for toxic, 0.4x for obscene and small probability for the rest.

Now I was suprised of how the implementation was done resp. I do not understand the following: How is a multi-label classification done (in opposite to the "usual" model)?

When checking the code I see the following model: