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I found an alternative with sympy here

As hinted in the comments, the _data_cls attribute could be removed, assuming that it's being used for type hinting purposes. The correct way to annotate a Generic class defined like class MyClass[Generic[T]) is to use MyClass[MyType] in the type annotations.

For example, hopefully the below works in mypy. I only tested in Pycharm and it seems to infer the type well enough at least.

Because sentence_id might be reviewed by different users (role), you can try to use subquery (INNER JOIN between user_accounts & annotated_sentences) with window function + condition aggregate function, getting count by your logic.

if you don't want to see admin count information you can use where filter rows.

A decidedly non-authoritative answer. Curiously, like jnthn in your prior Q, it feels natural to answer your questions in reverse order:

Is there any way to tell Raku "don't rebind this name to a new value, no-really-I-mean-it"?

As far as I can tell so far -- purely by testing variations, not by checking roast or the compiler source -- you can and must declare a sigil free symbol, and it must not be one declared with my \symbol ...:

Great question. I have often thought about this. I don't know of any packages that allow it natively, but it's not terribly difficult to do it yourself, since geom_text accepts angle as an aesthetic mapping.

Say we have the following plot:

With dict[str:int] the hint you are passing is dict whose keys are slices, because x:y is a slice in python.

The dict[str, int] passes the correct key and value hints, previously there also was a typing.Dict but it has been deprecated.

First, spaCy does no transformation of the input - it takes it literally as-is and preserves the format. So you don't lose any information when you provide text to spaCy.

That said, input to spaCy with the pretrained pipelines will work best if it is in natural sentences with no weird punctuation, like a newspaper article, because that's what spaCy's training data looks like.

To that end, you should remove meaningless white space (like newlines, leading and trailing spaces) or formatting characters (maybe a line of ----?), but that's about all the cleanup you have to do. The spaCy training data won't have bullets, so they might get some weird results, but I would leave them in to start. (Also, bullets are obviously printable characters - maybe you mean non-ASCII?)

I have no idea what you mean by "muck with the indexes", but for some older NLP methods it was common to do more extensive preprocessing, like removing stop words and lowercasing everything. Doing that will make things worse with spaCy because it uses the information you are removing for clues, just like a human reader would.

Note that you can train your own models, in which case they'll learn about the kind of text you show them. In that case you can get rid of preprocessing entirely, though for actually meaningless things like newlines / leading and following spaces you might as well remove them anyway.

To address your new info briefly...

Yes, character indexes for NER labels must be updated if you do preprocessing. If they aren't updated they aren't usable.

It looks like you're trying to extract "skills" from a resume. That has many bullet point lists. The spaCy training data is newspaper articles, which don't contain any lists like that, so it's hard to say what the right thing to do is. I don't think the bullets matter much, but you can try removing or not removing them.

What about stuff like lowercasing, stop words, lemmatizing, etc?

I already addressed this, but do not do this. This was historically common practice for NLP models, but for modern neural models, including spaCy, it is actively unhelpful.

You can do calculations within a function for the x and y values to construct the ggplot which extends the circle all the way round and gives labels correct heights.

I've adapted a function to work with other datasets. This takes a dataset in a tidy format, with:

• a 'year' column
• one row per 'event'
• a grouping variable (such as country)

I've used Nobel laurate data from here as an example dataset to show the function in practice. Data setup:

Really big thanks for your question & description of the problem, because it helped me to upgrade from cordova-android 9.1.0 to 10.1.1 :-).

And i think i can solve your problem. This error is thrown, because google services plugin is registered twice (apply plugin: 'com.google.gms.google-services') in gradle files, e.g.: platforms/android/app/build.gradle. You just have to comment/remove one of these imports.

Because cleaning files manually every time is very annoying, i created a bash script (= working on Mac OS), which comments the second import automatically: fix_android.sh