# How to make more specific selection?

**URL:** <https://support.prodi.gy/t/how-to-make-more-specific-selection/6244>\
**Category:** Uncategorized\
**Tags:** usage, ner\
**Created:** [January 18, 2023, 9:39pm UTC](https://support.prodi.gy/t/how-to-make-more-specific-selection/6244 "2023-01-18T21:39:42Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![mble](https://avatars.discourse-cdn.com/v4/letter/m/edb3f5/32.png) [@mble](https://support.prodi.gy/u/mble)\
**Post date:** [January 18, 2023, 9:39pm UTC](https://support.prodi.gy/t/how-to-make-more-specific-selection/6244/1 "2023-01-18T21:39:42Z")

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Hello,  
I want to make a precise selection. When I try to select for example "Name\abc", or "Name[abc" it selects whole string, instead of only "Name", and I want to be able to correct this, but I do not know how to do that, I do not see anywhere an option to change tagged text.

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**Author:** ![ryanwesslen](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ryanwesslen/32/2969_2.png) [@ryanwesslen](https://support.prodi.gy/u/ryanwesslen)\
**Post date:** [January 18, 2023, 10:10pm UTC](https://support.prodi.gy/t/how-to-make-more-specific-selection/6244/2 "2023-01-18T22:10:54Z")

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hi @mble!

Thanks for your question and welcome to the Prodigy community 👋

> [@mble](#):
>
> When I try to select for example "Name\abc", or "Name[abc" it selects whole string, instead of only "Name", and I want to be able to correct this, but I do not know how to do that, I do not see anywhere an option to change tagged text.

Are you looking for [character-based highlighting](https://prodi.gy/docs/named-entity-recognition#highlight-chars)?

Per the docs, you ca set a `--highlight-chars` flag to allow highlighting individual characters instead of only tokens. This will only store the character offsets of your annotation and won’t add a `"tokens"` property to the saved task.

But it's important to highlight this from the docs:

> When using character-based highlighting, annotation may be slower and there’s no guarantee that the spans you annotate map to actual tokens later on. If your goal is to train a named entity recognizer, you should consider using the **same tokenizer** during annotation, to make sure that your data can be used.

The key point is critical: make sure to use the same tokenizer you're using in annotation as you'd want to use for training. If you're not careful, you could run into tokenization alignment problems.

> [@ner correct with prodigy 1.11.8](https://support.prodi.gy/t/ner-correct-with-prodigy-1-11-8/6131/12):
>
> hi @JulieSarah! So mismatched tokenization can be a big problem that many users don't realize how important it is until it happens. Typically this happens when users have pre-annotated spans/entities that they load into manual recipes. These would be spans/entities they annotated in another tool, formatted the data for Prodigy, and then they use in the manual recipe (e.g., ner.manual) with a different tokenizer (e.g., blank:en or en\_core\_web\_sm). This is a good post that highlights it and pro…

If you can find ways to [modify your tokenizer](https://spacy.io/usage/training/#custom-tokenizer) early on, you may save yourself from headaches down the road due to mismatched tokens.

Hope this helps!