Multi Label Text Classification
Samples are assigned a subset of the available label classes, where there are no constraints on how many classes a sample can be assigned. We refer to the set of available label classes as tasks and behind the scenes, Galileo treats assigning each class (a task) as a binary prediction problem - 1 if the given class is assigned, 0 otherwise. Here's an example:
Input: Now I'm wondering on what I've been missing out. Again thank you for this.
Output: Curosity, Gratitude
Input: That is odd.
Output: Disappointment, Disgust