# How to perform automatically NER annotation based on patterns?

**URL:** <https://support.prodi.gy/t/how-to-perform-automatically-ner-annotation-based-on-patterns/4284>\
**Category:** Uncategorized\
**Tags:** usage, ner, spacy\
**Created:** [June 1, 2021, 6:10am UTC](https://support.prodi.gy/t/how-to-perform-automatically-ner-annotation-based-on-patterns/4284 "2021-06-01T06:10:14Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![marino](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/marino/32/2133_2.png) [@marino](https://support.prodi.gy/u/marino)\
**Post date:** [June 1, 2021, 6:10am UTC](https://support.prodi.gy/t/how-to-perform-automatically-ner-annotation-based-on-patterns/4284/1 "2021-06-01T06:10:14Z")

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I would like to perform (and store in prodigy dataset) NER annotation based on patterns without manual evaluation.

Any how-to suggestions?

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**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\
**Post date:** [June 2, 2021, 1:33am UTC](https://support.prodi.gy/t/how-to-perform-automatically-ner-annotation-based-on-patterns/4284/2 "2021-06-02T01:33:02Z")

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Hi! In that case, you can just go directly via spaCy, for example, using the entity ruler: [https://spacy.io/usage/rule-based-matching#entityruler](https://spacy.io/usage/rule-based-matching#entityruler)

It lets you add your patterns and will add all matches to the `doc.ents`, just like an entity recognizer. You can then use that `nlp` object to process your texts and extract the pattern-based NER annotations. In theory, you don't even have to go through Prodigy at all and you could just export the data and train with spaCy directly. But if you want to mix these annotations in with other annotations you've created manually, you can create data in Prodigy's format pretty easily using the processed `doc`:

```python
doc = nlp("This is a text")
spans = [{"start": ent.start_char, "end": ent.end_char, "label": ent.label} for ent in doc.ents]
example = {"text": doc.text, "spans": spans}

```

You can then add it to a dataset using Prodigy's database API: [https://prodi.gy/docs/api-database#database](https://prodi.gy/docs/api-database#database) Or alternatively, using the `db-in` command. I'd recommend setting up a separate dataset for your automatically generated annotations – if there's a bug in your code or a pattern that you want to improve, you can just remove the dataset and re-add it (which is harder if you've added the data to the same set as your manual annotations).