Using your UI on imported data for classification and annotation

[ines (Ines Montani)]

@shana

By applying this feature, can i choose to label by multiple choices or is it only one selected out of many? like your example to choose from “happy”,“sad”,“angry”,“neutral”, my labels can overlap like “sunny day”,“very humid”,“cold temprature”, where the weather can be sunny and humid at the same time.

Yes, you can set "choice_style": "multiple" in your recipe config to allow multiple selections. The collected annotations will then contain a list of all accepted option IDs, for example: "accept": ["SUNNY", "HUMID"].

@shana

We are actually not using spaCy and we expect to get output on every NER and text annotation in the form of a json. By combining the two I want to mark the data that helped me get the decision.

Yes, that makes sense! If you're planning on exporting the data and training afterwards, you can focus on using Prodigy as an "annotation workflow builder", to create different interfaces to collect the exact data you need as efficiently as possible. Once you're done annotating, you can export a dataset to a JSONL file:

prodigy db-out your_dataset > your_data.jsonl

@shana

for example, in your example for classifying words of insults, if the word “asshole” is in the text that I classify, and it is the reason I classified the text as an insult, I would like to tag it as indicative

Btw, you might also find this thread interesting, which explores a similar concept: Highlighting the matching words for text classfication