Is there a way to show a score together with the labels for each entity occurrence in the ner_manual view?

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Thank you! Your solution of appending the score in parenthesis is very good.
Now I have got another issue actually: I tried to adapt the make_tasks function with the logic in this post (Accessing probabilities in NER) to calculate the scores.
Here is the code:

    def make_tasks(nlp, stream):
    """Add a 'spans' key to each example, with predicted entities."""
    texts = ((eg["text"], eg) for eg in stream)
    for doc, eg in nlp.pipe(texts, as_tuples=True):

        task = copy.deepcopy(eg)
        spans = []
        (beams, _) = nlp.entity.beam_parse([doc], beam_width=16, beam_density=0.0001)

        for score, ents in nlp.entity.moves.get_beam_parses(beams[0]):
            for ent in doc.ents:
                if labels and ent.label_ not in labels:
                    continue

                spans.append(
                    {
                        "token_start": ent.start,
                        "token_end": ent.end - 1,
                        "start": ent.start_char,
                        "end": ent.end_char,
                        "text": ent.text,
                        "label": "{} ({})".format(ent.label_, score),
                        "source": spacy_model,
                        "input_hash": eg[INPUT_HASH_ATTR],
                    }
                )
        task["spans"] = spans
        task = set_hashes(task)
        yield task

However, I notice that the model returns always score = 1.0 for all the entities.
I tried some of the models trained by me, plus I tried to run the recipe with the standard “en_core_web_lg-2.0.0” and “en_core_web_sm” models on an english text. Result is always score=1.0.

Am i doing anything wrong in calculating the scores? Or is this expected?
If it is wrong, what can i do to get the model score of each entity prediction?

Thanks
Kasra