# NER and POS Tagging Annotation using One Prodigy User Interface

**URL:** <https://support.prodi.gy/t/ner-and-pos-tagging-annotation-using-one-prodigy-user-interface/7489>\
**Category:** Uncategorized\
**Created:** [January 30, 2025, 6:31am UTC](https://support.prodi.gy/t/ner-and-pos-tagging-annotation-using-one-prodigy-user-interface/7489 "2025-01-30T06:31:02Z")\
**Posts on this page:** 3\
**Page:** 1

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**Author:** ![sigitpurnomo](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/sigitpurnomo/32/2107_2.png) [@sigitpurnomo](https://support.prodi.gy/u/sigitpurnomo)\
**Post date:** [January 30, 2025, 6:31am UTC](https://support.prodi.gy/t/ner-and-pos-tagging-annotation-using-one-prodigy-user-interface/7489/1 "2025-01-30T06:31:02Z")

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Hi all,  
I am trying to annotate a corpus for the NER and POS tagging using Prodigy with a custom recipe to make the process be done in one Prodigy user interface. When I ran the Prodigy, the Prodigy UI was displayed correctly. However, the problem was that the annotation results affected each other when I tried to annotate the text, as shown in the attached image. Can you help ensure Prodigy treats them as different tasks? Here is my custom recipe script.

```python
import spacy
import prodigy
from prodigy.components.loaders import JSONL
from prodigy.util import split_string

@prodigy.recipe(
    "pos_ner_annotation",
    dataset=("Dataset name", "positional", None, str),
    source=("Input data (JSONL)", "positional", None, str),
    spacy_model=("spaCy model", "option", "m", str, "en_core_web_sm"),
    pos_labels=("Comma-separated POS labels", "option", "p", str, ""),
    ner_labels=("Comma-separated NER labels", "option", "n", str, "")
)
def pos_ner_annotation(dataset, source, spacy_model="blank:id", pos_labels="", ner_labels=""):
    """
    A Prodigy recipe that allows simultaneous POS tagging and NER annotation in the same UI,
    ensuring that both are completely independent.
    """
    nlp = spacy.load(spacy_model)

    # Load the dataset
    stream = JSONL(source)

    # Process label inputs
    pos_labels = split_string(pos_labels) if pos_labels else list(nlp.get_pipe("tagger").labels)
    ner_labels = split_string(ner_labels) if ner_labels else list(nlp.get_pipe("ner").labels)

    def get_annotations():
        for eg in stream:
            doc = nlp(eg["text"])

            # Token-level POS annotation (must be a list)
            pos_tokens = list([
                {
                    "text": tok.text,
                    "id": i,
                    "start": tok.idx,
                    "end": tok.idx + len(tok.text),
                    "tag": tok.pos_
                }
                for i, tok in enumerate(doc)
            ])

            # Span-level NER annotation (must be a list)
            ner_spans = list([
                {"start": ent.start_char, "end": ent.end_char, "label": ent.label_}
                for ent in doc.ents
                if ent.label_ in ner_labels
            ])

            yield {
                "text": eg["text"],
                "tokens": pos_tokens, # Ensures POS uses a list
                "spans": ner_spans, # Ensures NER uses a list
                "meta": {"task": "POS + NER", "model": spacy_model},
                "_input_hash": hash(eg["text"] + "POS_NER"),
                "_task_hash": hash(eg["text"] + "POS_NER")
            }

    return {
        "dataset": dataset,
        "view_id": "blocks",
        "stream": get_annotations(),
        "config": {
            "blocks": [
                {"view_id": "pos_manual", "labels": pos_labels}, # POS tagging
                {"view_id": "ner_manual", "labels": ner_labels} # NER annotation
            ],
            "exclude_by": "task",
            "show_tok_ranks": True,
            "tokens": True,
        },
    }

```

 ![Prodigy](https://us1.discourse-cdn.com/flex020/uploads/prodigy/original/2X/2/2e3861120b4f110b82e2f4eaf18325294079dde3.png)

Thank you.

---

<div class="post-metadata">

**Author:** ![magdaaniol](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/magdaaniol/32/2787_2.png) [@magdaaniol](https://support.prodi.gy/u/magdaaniol)\
**Post date:** [January 30, 2025, 10:27am UTC](https://support.prodi.gy/t/ner-and-pos-tagging-annotation-using-one-prodigy-user-interface/7489/2 "2025-01-30T10:27:09Z")

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Hi @sigitpurnomo,

Thanks for detailed descriptions!

The problem here is that `pos_manual` UI uses `ner_manual` UI under the hood and the they both refer to the same text object.

The best way to combine `ner_manual` and `pos_manual` UIs and keep them independent would be via [`pages`](https://prodi.gy/docs/api-interfaces#pages) UI.  
I've written a very similar recipe in this [post](https://support.prodi.gy/t/working-with-annotating-teams-in-prodigy/7488/2). Could you check if the solution there could work for you?  
Also, let me know if you need help adapting it. I suppose you would just need to add a function for creating the NER page instead of the choice page:

```python
def create_ner_page(text: str, tokens: List[Dict],labels: List[str]) -> Dict[str, Any]:
    """Create a single page configuration with the given parameters."""
    page = {
        "text": text,
        "tokens": tokens,
        "view_id": "ner_manual",
        "config": {
            "labels": labels
        }
    }
    return set_hashes(page)

```

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<div class="post-metadata">

**Author:** ![sigitpurnomo](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/sigitpurnomo/32/2107_2.png) [@sigitpurnomo](https://support.prodi.gy/u/sigitpurnomo)\
**Post date:** [January 31, 2025, 12:09am UTC](https://support.prodi.gy/t/ner-and-pos-tagging-annotation-using-one-prodigy-user-interface/7489/3 "2025-01-31T00:09:49Z")

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Hi @magdaaniol,

Thank you for your suggestion. It works. Appreciated it.