# Training a model on both gold and binary data

**URL:** <https://support.prodi.gy/t/training-a-model-on-both-gold-and-binary-data/1502>\
**Category:** Uncategorized\
**Tags:** usage, ner, done\
**Created:** [May 9, 2019, 7:54am UTC](https://support.prodi.gy/t/training-a-model-on-both-gold-and-binary-data/1502 "2019-05-09T07:54:06Z")\
**Posts on this page:** 1\
**Showing post:** 7

<div class="post-metadata">

**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\
**Post date:** [January 31, 2020, 11:15am UTC](https://support.prodi.gy/t/training-a-model-on-both-gold-and-binary-data/1502/7 "2020-01-31T11:15:48Z")

</div>

> [@einarbmag](#):
>
> I'm actually quite confused by the `--binary` flag... isn't all the training data technically "binary", i.e you either accept or reject the annotation?

The `--binary` flag uses a more complex mechanism to update the model that lets you take advantage of both the accepted _and_ rejected suggestions of single entity spans – basically, like the default behaviour of `ner.batch-train`. I've written some more about it on this thread:

> [@Prodigy 1.90 train recipe --ner-missing argument](https://support.prodi.gy/t/prodigy-1-90-train-recipe-ner-missing-argument/2415/4):
>
> In a typical training scenario, you're updating a model with examples and the correct answer – e.g. with a text and the entities in it. In some cases you may also have partial annotations: you know some entities but not all. Prodigy's active learning recipes like ner.teach also let you collect binary yes/no decisions. The data you create here is different again: for some spans, you know that they are entities, because you accepted them. For the ones you rejected, you know that they're not of t…

The `--ner-missing` flag lets you to specify that unannotated tokens should be treated as missing values (and not as explicitly as "not an entity"). This allows you to still train from incomplete annotations, e.g. if you only have annotations for one or two labels.

The new `train` command was designed to harmonise training between Prodigy and spaCy, and use spaCy's regular update mechanism to train from gold-standard data (with and without missing values). This also makes it easier to ensure that results are consistent and reproducible.

---

_[View the full topic](https://support.prodi.gy/t/training-a-model-on-both-gold-and-binary-data/1502)._