[ajratner]

You can always do multi-label as a multi-task learning model (or just a set of binary models), which is something we (and many others) have explored before! A lot of the adjustments for mainline Snorkel have to do with (A) the semantics of the labeling functions (need to be able to express that something is not class A and/or have a general per-class prior) and (B) all the infra to support what is just now a bunch of independent per-label binary tasks, at base

[eggie5]

Snorkel has a label mutual exclusion assumption right?

My core problem is a multi-label problem, but my snorkel data, from the LabelModel is inherently single-label (mutually exclusive). What is the prevailing recommendation to do multi-label w/ Snorkel? Is the below what you are currently recommending?

For a given, k-wise multi-label problem:

1. Generate k binary datasets w/ LabelModel 2. Train k separate binary classifiers for each respective dataset 3. At inference/prediction time pass input though the k classifiers and get scores.

Is this what the current recommendation is? Create a set of binary classifiers?