How to edit existing texts that were added to a dataset using db-in

[ines (Ines Montani)]

Hey, and thanks!

@mihaivinaga

Now, lets say I want to search for a particular text within this dataset to change something, is that something I can do? Probably the main question that I am asking is, can I modify annotations after I've added them to the dataset using db-in? Also the search feature would be cool too.

Datasets in Prodigy are append-only by design – I've written some more about that concept on this thread:

@ines

Datasets in Prodigy are append-only by design: you typically don't want to overwrite existing records, because that means you'd lose a datapoint you've collected. And it'd also make it too easy to erase work. Instead, you can re-annotate and correct the data, and save the results to a new dataset. If you make a mistake, you still have the previous data and can start again.

Prodigy's input and output formats are the same – so you can always export a dataset and load the data back in. For example, if you load a manually-annotated NER dataset back into ner.manual , the entities will be pre-highlighted and you can correct them.

If it's possible to automate some of the changes, that's great, too – for instance, if you removed label X from your label scheme, you can iterate over the "spans" and remove all entries that contain "label": "X" before you send them out for correction again.

If you have conflicting annotations that you want to resolve to one final "master corpus", you can also use the review recipe. It takes one or more datasets with one or more sessions and will group annotations on the same input together. So if annotator A has labelled a span and annotator B hasn't, you can see both and decide what the correct answer is (or even label something entirely different by hand).

@mihaivinaga

Also the search feature would be cool too.

Prodigy gives you direct access to the datasets via its Python API – so you can use that to implement any filtering or search logic you need, over any fields in the JSON records. You could do a simple keyword search over the "text" values, or do something more complex with regular expressions (or even spaCy if you want a more advanced NLP-powered search ).

from prodigy.components.db import connect

db = connect()
examples = db.get_datasset("dataset_name")
for eg in examples:
    # do something here...

examples here is a list of dictionaries representing the individual examples. If you've found examples you want to edit, you could either export them to a file and re-annotate them (if you want to change entity spans or more complex stuff), or edit them in your script, and then save the result (previously correct examples, edited examples) to a new dataset.