empower | Power Assert feature enhancer for assert function/object | Assertion library

Calculated Column in AllIterationTable:

You're going to find a few challenges here, such as body-positivity being in one of your sentences but not being a keyword. There could be many variations you are missing. However you can take an initial stab at it by splitting all of the individual sentences into rows, then using the regex to find the matches. You can stack those back up into lists of matches if you want.

There’s not really a nice way to achieve this currently and it’s maybe not advised. This is primarily because the list of properties that will be copied from an object are fixed at publish-/compile-time in any (nice) method I can see.

I've included the most generic version of this function I can construct below, but it has some limitations. More concretely, the copy function

1. does not copy links that aren't represented by FK properties (i.e. it only copies properties);
2. does not adapt and might break when there are new or removed properties; and
3. is not easily maintained as functionality can change depending on when it is compiled.

I made a little docker image with xeyes in it to test X11 clients with the XQuartz X11 server on my Mac using this as my Dockerfile:

A full page load is where the entire page is downloaded from the server. A page typically consists of several sections: header, footer, navigation, and content. In a classic web application without AJAX, a user clicks on a link to another page, and has to download the full page, even though only the main content is changing. The header, footer, and navigation all get downloaded again even though they don't change.

With AJAX there is the opportunity to only change the parts of the page that will change. When a user clicks on the link, JavaScript loads just the content for that link and inserts it into the current page. The header, footer, and navigation don't need to reload.

This introduces other problems that need attention.

• When AJAX inserts new content into the page, the URL doesn't change. That makes it difficult for users to bookmark or link to specific content. Well written AJAX applications use history.pushState() to update the URL when loading content via AJAX.

• There are then two paths to get to every piece of content. Users can either load the URL containing that content directly, or load the content into some other page by following a link. Web developers need to test and ensure both work.

• Search engines have trouble crawling AJAX powered sites. For best compatibility, you need to employ server side rendering (SSR) or pre-rendering to serve initial content on a page load that doesn't require JavaScript.

  Even for Googlebot (which executes JavaScript) care must be taken to make an AJAX powered site crawlable. Googlebot doesn't simulate user actions like clicking, scrolling, hovering, or moving the mouse.

  • Content needs to appear on page load without any user interaction
  • You must use links for navigation so that Googlebot can find other pages by scanning the document object model (DOM). For users, JavaScript can intercept clicks on those links and prevent a full page load by using return false from the onclick handler or event.preventDefault() in the click handler.

You have to import and wrap your component in a ThemeProvider and then pass in a string (i.e. "secondary," "primary") to the Button's color attribute.

Your are close to your goal, just some little adjustment to do - While iterating check for tag.name as well as its class and break if it is not a

with class containing index2:

You're improvising a lot here with a bunch of potential errors or suboptimalities. Note especially that:

• While (because it's Python) you can always mutate the models however you want for interesting effects, seeding a model with outside word-vectors then continuing training isn't formally- or well-supported by Gensim. As far as I can tell – & I wrote a bunch of this code! – there aren't any good docs/examples of doing it well, or doing the necessary tuning/validation of results, or demonstrating a reliable advantage of this technique. Most examples online are of eager people plowing ahead unaware of the tradeoffs, seeing a trivial indicator of completion or a tiny bit of encouraging results, and then overconfidently showing their work as if this were a well-grounded technique or best-practice. It isn't. Without a deep understanding of the model, & review of the source code, & regular re-checking of your results for sanity/improvement, there will be hidden gotchas. It is especially the case that fresh training on just a subset of all words could pull those words out of compatible coordinate alignment with other words not receiving training.
• The intersect_word2vec_format() feature, and especially the lockf function, are also experimental - one stab at maybe offering a way to mix in other word-vectors, but without any theoretical support. (I also believe intersect_word2vec_format() remains slightly broken in recent (circa 4.1.2) Gensim versions, though there may be a simple workaround.) Still, the lockf functionality may require tricky manual initialization & adaptation to other non-standard steps. To use it, it'd be best to read & understand the Gensim source code where related variables appear.

So, if you really need a larger vocabulary than your initial domain-specific corpus, the safest approach is probably to extend your training corpus with more texts that feature the desired words, as used in similar language contexts. (For example, if you rdomain is scientific discourse, you'd want to extend your corpus with more similar scientific text to learn compatible words – not, say, classic fiction.) Then all words go through the well-characterized simultaneous training process.

That said, if you really want to continue experimenting with this potentially complicated and error-prone improvised approach, your main problems might be:

• using strings as your sentences instead of lists-of-tokens (so the training 'words' wind up actually just being single-characters)
• something related to the intersect_word2vec_format bug; check if .vectors_lockf is the right length, with 1.0 in the all the right slots for word-updates, before training

Separately, other observations:

• min_count=0 is usually a bad idea: these models improve when you discard rare words entirely. (Though, when doing a .build_vocab(…, update=True) vocab-expansion, a bunch of things with the usual neat handling of low-frequency words and frequency-sorted vocabularies become screwy.)
• hs=1 should generally not be set without also disabling the usually-preferred default negative-sampling with negative=0. (Otherwise, you're creating a hybrid franken-model, using both modes on one side of the internal neural network, that share the same input word-vectors: a much slower approach not especially likely to be better than either alone.)
• ns_exponent=0.5 is non-standard, and using non-standard values for the parameter is most-likely to offer benefit in peculiar situations (like training texts that aren't true natural language sentences), and should only be tweaked within a harness for comparing results with alternate values.
• sample=10**-2 is also non-standard, and such a large value might be nearly the same as turning off sample (say with a 0 value) entirely. It's more common to want to make this parameter more-aggressive (smaller than the default), if you have plentiful training data.

In general, while the defaults aren't sacred, you generally should avoid tinkering with them until you have both (a) a good idea of why your corpus/goals might benefit from a different value; & (b) a system for verifying which alterations are helping or hurting, such as a grid-search over many parameter combinations that scores the fitness of resulting models on (some proxy for) your true end task.