# Does prodigy.models.ner.EntityRecognizer constructor modify the underlying nlp model?
**URL:** https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399
**Category:** Uncategorized
**Tags:** solved, usage, done, ner
**Created:** [July 2, 2021, 8:26pm UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399 "2021-07-02T20:26:23Z")
**Posts on this page:** 6
**Page:** 1
### Author:  [@haishao](https://support.prodi.gy/u/haishao)
#### Post date: [July 2, 2021, 8:26pm UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399/1 "2021-07-02T20:26:23Z")
First, I want to thank you for the great work on Prodigy and spaCy. I was able to progress much faster on my NER project since I started using Prodigy and spaCy. Now I have a trained NER model with 13 labels that performs fairly decently, overall F-score \> 90%, but for one label, sometimes the prediction will only include portion of the ground truth span. However, when I tried running the same sample through `ner.teach` it's highlighting the correct span. After some digging, I was able to nail down the following situation:
```python
nlp = spacy.load(model_path) # trained NER model
displacy.render(nlp(sample_text), style="ent")
```
while this shows the incorrect predicted entity (partially highlighted)
```python
from prodigy.models.ner import EntityRecognizer
nlp = spacy.load(model_path) # trained NER model
model = EntityRecognizer(nlp)
displacy.render(nlp(sample_text), style="ent")
```
by wrapping `nlp` with `EntityRecognizer`, the error is gone. I wonder if you could shed some light on this. Thanks!
---
### Author:  [@ines](https://support.prodi.gy/u/ines)
#### Post date: [July 3, 2021, 3:27am UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399/2 "2021-07-03T03:27:49Z")
Hi and thanks for the kind words, that's great to hear 🎉
I'm surprised that using the `EntityRecognizer` annotation model here even worked, because what it does it quite specific to Prodigy and the binary annotation process (we should have probably used a better and more descriptive name for it). The annotation model will use the underlying `nlp` object and the beam parse to suggest entities based on the different **possible analyses** of the text. These aren't necessarily the most confident predictions. So it will generate a stream of binary suggestions of all kinds of entities, potentially conflicting, and their respective confidence scores. Within the recipe, those suggestions can then be filtered and sorted, e.g. to focus on the most uncertain predictions and skip suggestions with a higher confidence, so you can focus on annotating examles that result in the most relevant updates to the weights.
That likely also explains what you were seeing in `ner.teach`: the suggestion here may have been one that the model was less confident about, compared to a different analysis that ended up "winning". For instance, for the sentence `"i like apple"`, the model may come up with 2 different possible analyses: `["O", "O", "O"]` (no entities at all) and `["O", "O", "U-ORG"]` ("apple" is an `ORG`). Given the current weights, analysis 1 might end up with a higher score, so that's the output you will see at runtime. However, when you run `ner.teach`, the second analysis may end up with a score of `0.5 `, so Prodigy will present it to you for annotation. If you accept this suggestion, this is very valuable feedback to the model – in fact, more valuable than if you accepted a very high-confidence prediction. If you annotate enough of these examples, this can move the model into the right direction, so it's more likely to predict the correct analysis in the future for similar examples.
---
### Author:  [@haishao](https://support.prodi.gy/u/haishao)
#### Post date: [July 7, 2021, 6:42pm UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399/3 "2021-07-07T18:42:49Z")
Thanks so much for the detailed explanation (and sorry for not seeing it earlier) and I understand that the Prodigy annotation model may produce different output from the original spacy model as in how `ner.teach` is intended to be used. However, what I am trying to understand is why the same spacy model produces different output after being wrapped by the prodigy `EntityRecognizer`class. I rewrote my code snippet hoping to make it a bit more clear
```python
from prodigy.models.ner import EntityRecognizer
# trained NER model
nlp = spacy.load(model_path)
# same trained NER model, but passed to Prodigy EntityRecognizer class constructor
nlp_wrapped = spacy.load(model_path)
EntityRecognizer(nlp_wrapped)
# feed same text to both model objects
doc = nlp(sample_text)
doc_wrapped = nlp_wrapped(sample_text)
```
And I see differences when comparing attribute values token by token between `doc` and `doc_wrapped`. For example (here `DIMENSION` is a label in my trained NER model, `*_1` fields are from `doc_wrapped` and `*_2` fields are from `doc` (not wrapped))
| i | text | ent\_iob\_1 | ent\_iob\_2 | ent\_type\_1 | ent\_type\_2 | dep\_1 | dep\_2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 6 | 3.4 | B | B | DIMENSION | DIMENSION | nummod | nummod |
| 7 | cm | I | I | DIMENSION | DIMENSION | npadvmod | dobj |
| 8 | x | I | O | DIMENSION | | punct | punct |
| 9 | 4.9 | I | O | DIMENSION | | nummod | nummod |
| 10 | cm | I | O | DIMENSION | | appos | dep |
| 11 | x | I | O | DIMENSION | | punct | punct |
| 12 | 2.2 | I | O | DIMENSION | | nummod | nummod |
| 13 | cm | I | O | DIMENSION | | npadvmod | npadvmod |
And annotations in the training dataset are all like what `doc_wrapped` produces (that's the main reason I started this experiment/investigation). I do realize it's probably more of a spaCy than a Prodigy question, but it is related to the use of `prodigy.models.ner.EntityRecognizer`. I wonder if you could shed more light on what I am seeing. Thanks again!
---
### Author:  [@haishao](https://support.prodi.gy/u/haishao)
#### Post date: [July 7, 2021, 8:53pm UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399/4 "2021-07-07T20:53:08Z")
Adding some more information. It seems `prodigy.models.ner.EntityRecognizer` does add a `sentencizer` to the underlying `nlp`:
```python
nlp_2 = spacy.load(model_path)
print(nlp_2.pipe_names)
EntityRecognizer(nlp_2)
print(nlp_2.pipe_names)
```
outputs
```python
['tagger', 'parser', 'ner']
['sentencizer', 'tagger', 'parser', 'ner']
```
And for the above example `nlp` added a sentence boundary in front of the first "x" (8th token) while `nlp_wrapped` did not.
---
### Author:  [@ines](https://support.prodi.gy/u/ines)
#### Post date: [July 8, 2021, 3:41am UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399/5 "2021-07-08T03:41:14Z")
> [@haishao](#):
>
> However, what I am trying to understand is why the same spacy model produces different output after being wrapped by the prodigy `EntityRecognizer`class.
Oh I understand what you mean now, sorry! And yes, your analysis seem correct: in the current stable version, the `EntityRecognizer` constructor will add a sentencizer for sentence segmentation by default – but this is definitely unideal and it's actually something we've removed for the upcoming version (currently available as a [nightly](https://support.prodi.gy/t/prodigy-nightly-spacy-v3-support-ui-for-overlapping-spans-improved-feeds-more/3861)). It usually doesn't matter that much, because the `nlp` object in the annotation model is updated and later discarded anyways – but there are cases where the sentence segmentation can impact the model's predictions, because the entity recognizer isn't allowed to predict entities across sentence boundaries.
---
### Author:  [@haishao](https://support.prodi.gy/u/haishao)
#### Post date: [July 8, 2021, 4:09am UTC](https://support.prodi.gy/t/does-prodigy-models-ner-entityrecognizer-constructor-modify-the-underlying-nlp-model/4399/6 "2021-07-08T04:09:19Z")
That totally makes sense now. Thanks again!