Skills | detailed implementation of the TrueSkill algorithm | Learning library

I was able to accomplish what I want with a negative lookahead assertion:

To create a new line variable is a good start. But you should use it in a different way.
Percent expansion doesn't work quite well with newlines in variables, it can be done, but it's quite complex.

But delayed expansion flawlessly works with any characters

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.

Thy the following

You are getting the undefined error because for few of the cases the post_categories array is empty and if u try accessing the 0th element it will throw an error. So add a null check for the array length and for id something like below

I'm not sure if this is what you need, but maybe you can use it as a starting point... This requires C++14

This is a side-effect of using the sub-expression operator $() here (note you would get the same effect using the group-expression operator () as well). The reason this happens is because $() and () function like parentheses in mathematics, and order of operations (technically order of precedence in computing) dictates that inner expressions be worked out before outer expressions. So anything inside the parentheses must complete first before it can be processed by the outer code.

In your case here, the "outer code" is what displays information to the console, hence, why the inner command has to complete before it gets displayed.

If you don't need to evaluate $output later on, the simplest and most performant approach is to pipe your command output directly to Out-Host instead (you probably also want to redirect error output as well):

You can add the (?!.*(\d)(?:-*\1){3}) negative lookahead after ^ (start of string) to add the restriction.

The ^(?!.*(\d)(?:-*\1){3}) pattern matches

• ^ - start of string
• (?!.*(\d)(?:-*\1){3}) - a negative lookahead that fails the match if, immediately to the right of the current location, there is
  • .* - any zero or more chars other than line break chars as many as possible
  • (\d) - Group 1: one digit
  • (?:-*\1){3} - three occurrences of zero or more - chars followed with the same digit as captured in Group 1 (as \1 is an inline backreference to Group 1 value).

See the regex demo.

If you want to combine this pattern with others, just put the lookahead right after ^ (and in case you have other patterns before with capturing groups, you will need to adjust the \1 backreference). E.g. combining it with your second regex, validLength = re.compile(r'^[0-9]{16}$|^[0-9]{4}-[0-9]{4}-[0-9]{4}-[0-9]{4}$'), it will look like

Try this, it worked properly: