Multi-label text classification (MLTC), also known as multi-output text classification is a variant of the text classification problem, where multiple labels are assigned to each sample. It is a generalization of multiclass text classification, where a single label is assigned to each sample. 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

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  • HuggingFace
  • PyTorch
  • TensorFlow
  • Keras