Hi, I'm using the
textcat.teach recipe for some straight forward annotation of only two labels: biased-language and neutral-language, like shown in the screenshot.
I started prodigy using:
prodigy textcat.teach wordchoice en_core_web_lg prod.jsonl --label NEUTRAL-LANG,BIASED-LANG
As far as I understand the documentation, in
textcat.teach prodigy uses the model in the loop to already choose one label, which will be shown at the top (e.g., in the screenshot, prodigy predicted
neutral-lang). Further, human annotators have to accept the task if the shown label is correct in their opinion, and have to reject if the shown label is incorrect.
I got two questions:
- Is that correct?
- In terms of manual labeling, I find that considering both the predicted label as well as the text in order to determine which button to press (accept, if the label matches the text, or else reject) puts a higher cognitive load than, for instance, always pressing accept for one label (e.g., biased-language) and always reject for the other (e.g., neutral-language). How would I do that (e.g., accept=biased-language, reject=neutral-language) and still use
textcat.teachincluding its active learning part?