# No tagger in pre-trained models?
**URL:** https://support.prodi.gy/t/no-tagger-in-pre-trained-models/7162
**Category:** Uncategorized
**Tags:** coref
**Created:** [March 23, 2024, 11:01pm UTC](https://support.prodi.gy/t/no-tagger-in-pre-trained-models/7162 "2024-03-23T23:01:15Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@akimotode](https://support.prodi.gy/u/akimotode)
#### Post date: [March 23, 2024, 11:01pm UTC](https://support.prodi.gy/t/no-tagger-in-pre-trained-models/7162/1 "2024-03-23T23:01:15Z")
I am trying to do Coref tagging.
My CLI is this:
`python -m prodigy coref.manual coref_dataset en_core_web_sm d:\training\spacy_rel\assets\text_fragments.jsonl --label COREF`
The error message I get confused me since I more or less copied this off the documentation. And I can't imagine that the pre-trained model is not complete...
`ValueError: [E155] The pipeline needs to include a morphologizer or tagger+attribute_ruler in order to use Matcher or PhraseMatcher with the attribute POS. Try using 'nlp()' instead of 'nlp.make_doc()' or 'list(nlp.pipe())' instead of 'list(nlp.tokenizer.pipe())'.`
I assume this is a silly mistake on my side, but I can't see it...
In case it is important: I'm running Prodigy 1.14.12 and spaCy 3.7 (incl. the 3.7 models).
Cheers,
Kai
---
### Author:  [@magdaaniol](https://support.prodi.gy/u/magdaaniol)
#### Post date: [March 26, 2024, 8:35am UTC](https://support.prodi.gy/t/no-tagger-in-pre-trained-models/7162/2 "2024-03-26T08:35:19Z")
Hi @akimotode,
Have you modified the spaCy pipeline in any way, e.g. by adding EntityRuler or custom NER component?
If so, you should make sure, the `entity_ruler` and `ner` components are after the `atribute_ruler` so that the `POS` labels produced by `tagger` and `attribute_ruler` are available for `entity_ruler` and `ner`.
You can see your current order like so:
```python
import spacy
nlp = spacy.load("en_core_web_sm")
print(nlp.pipe_names)
```
To change the order, if necessary:
```python
# move the NER component to the end of the pipeline: remove and then reload from the same source in the new position
nlp.remove_pipe("ner")
nlp.add_pipe("ner", source=spacy.load("en_core_web_sm"))
# add entity ruler
nlp.add_pipe("entity_ruler", before="ner")
print(nlp.pipe_names)
# ['tok2vec', 'tagger', 'parser', 'attribute_ruler', 'lemmatizer', 'entity_ruler', 'ner', ...]
```