Batch Inference With Dynamic Label Sets Per Sample

via “dynamic label-agnostic text categorization without retraining”

zero-shot-classification model by undefined. 2,37,978 downloads.

Unique: Decouples label definition from model training by reformulating classification as NLI, enabling arbitrary label sets at inference time. Unlike traditional classifiers that require retraining for new labels, this approach treats labels as natural language hypotheses, leveraging the model's learned entailment reasoning.

vs others: Eliminates retraining overhead compared to fine-tuned classifiers when label sets change, and supports arbitrary label descriptions without vocabulary constraints, making it ideal for dynamic or user-defined categorization systems.