# Re-labeling

**URL:** <https://support.prodi.gy/t/re-labeling/547>\
**Category:** Uncategorized\
**Tags:** usage, ner, solved\
**Created:** [May 17, 2018, 4:57pm UTC](https://support.prodi.gy/t/re-labeling/547 "2018-05-17T16:57:44Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![hannahlindsley](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/hannahlindsley/32/285_2.png) [@hannahlindsley](https://support.prodi.gy/u/hannahlindsley)\
**Post date:** [May 17, 2018, 4:57pm UTC](https://support.prodi.gy/t/re-labeling/547/1 "2018-05-17T16:57:44Z")

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For our use case, we want to start from scratch with streaming a raw dataset from a kafka queue. We’re set up to label custom entities in text without a model in the loop.  
We have a single, common postgres for the db.

We’d like to be able to go back into a dataset and add labels on it; for example, one labeler might be annotating phone numbers, and another labeler might come along 6 months later and want to add an address label to the same data.

Is there currently support for updating an existing dataset that’s built off a streaming queue?

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<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:** [May 17, 2018, 5:42pm UTC](https://support.prodi.gy/t/re-labeling/547/2 "2018-05-17T17:42:00Z")

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Prodigy’s stream and JSON output formats are pretty much identical, so you can always go back and load in an existing dataset. For example:

```bash
prodigy db-out some_dataset > some_dataset.jsonl
prodigy ner.manual other_dataset en_core_web_sm some_dataset.jsonl --label SOME_LABEL

```

The `ner.manual` recipe respects pre-defined entities, so the annotator will see everything that was labelled before, can correct the spans and also add new entities.

While you can _technically_ add the new annotations to the old dataset, I’d still recommend creating a new one when you re-annotate an existing set later. This gives you a cleaner separation and if something goes wrong, you’ll always have a record of the previous set.

Hope this helps! 😊