# Spacy features - NER manual ?

**URL:** <https://support.prodi.gy/t/spacy-features-ner-manual/3811>\
**Category:** Uncategorized\
**Tags:** ner, spacy, solved\
**Created:** [January 21, 2021, 5:40pm UTC](https://support.prodi.gy/t/spacy-features-ner-manual/3811 "2021-01-21T17:40:02Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**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 22, 2021, 8:55am UTC](https://support.prodi.gy/t/spacy-features-ner-manual/3811/2 "2021-01-22T08:55:33Z")

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Hi! You can always load an existing dataset back in for annotation – just use the [`dataset:` syntax](https://prodi.gy/docs/api-loaders#datasets) instead of the source file (e.g. `dataset:your_previous_dataset`) and then save the results to a new dataset. You'll then see the existing annotations, and you'll be able to add new ones on top of that.

Alternatively, you can also create an entirely new dataset and only annotate the new labels. This can sometimes make sense if you're annotating one label at a time. Prodigy will merge all annotations on the same text when you train or export the data with `data-to-spacy`.

(_In theory_, you could also edit the JSON manually, if you really want to – you can find an example of the format here: [Annotation interfaces · Prodigy · An annotation tool for AI, Machine Learning & NLP](https://prodi.gy/docs/api-interfaces#ner_manual) But it's not necessarily something I'd recommend. It does work, though – just add the spans with their start and end index and the given label.)

> [@Sebastien](#):
>
> Another question : after using data-to-spacy to export the training corpora as a JSON file, how can we use that file to train a NER model in SpaCy ?

The JSON file is the data format used by `spacy train`: [Command Line Interface · spaCy API Documentation](https://spacy.io/api/cli#train)

> [@Sebastien](#):
>
> Also, is there a way to reuse the POS tags in already existing models ?

If you mean the POS tagger and its pretrained weights: Yes, you can just add that to your pipeline. See here for an example: [Add custom NER model from prodigy to spacy pipeline - #2 by ines](https://support.prodi.gy/t/add-custom-ner-model-from-prodigy-to-spacy-pipeline/3798/2)

If you mean the POS tags predicted by the model on your specific data: Yes, you can use spaCy to label the data for you automatically, using the existing model. You could also use an annotation recipe like `pos.correct` and manually review and correct the model's predictions on your data, and then train a POS tagger using the collected annotations.

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_[View the full topic](https://support.prodi.gy/t/spacy-features-ner-manual/3811)._