EntityRecognizer.make_best(silver_data) seems to ignore entities in silver data

The docs and https://github.com/explosion/prodigy-recipes/blob/master/ner/ner_silver_to_gold.py#L49
suggest it’ll merge the silver annotations with annotations produced by the model to find the best possible analysis given the constraints.

By way of reproduction, I took some values that occurred while running the ner.silver-to-gold recipe

import spacy
from prodigy.models.ner import EntityRecognizer
nlp = spacy.load("en_core_web_lg")
ner = EntityRecognizer(nlp)
list(ner.make_best(
    [
        {
            "text":text,
             "spans":[{"text":"SECURITY BANK CORP","label":"ORG","start":0,"end":18}],
             "no_missing":True,
             "_input_hash":-124546672,
             "_task_hash":1252032485,
             "answer":"accept"
        }
    ]
))

gives

[{'text': 'SECURITY BANK CORP <SBKC.O> SAYS EARNINGS INCREASE',
  'spans': [{'start': 0,
    'end': 20,
    'text': 'SECURITY BANK CORP <',
    'rank': 0,
    'label': 'ORG',
    'score': 1.0}],
  '_input_hash': -124546672,
  '_task_hash': 1252032485}]

Would you expect this to take my annotation in preference to the one produced by the model, or have I got the wrong idea?

(This is with Prodigy 1.8.3. Possibly related: Binary annotated data missed out in making gold data also suggests the silver annotations don’t show up in the gold data.)

Thanks for any help!

Jamie

That does look wrong! Thanks for the simple example. Looking into it.