Zero Shot Text Classification With Dynamic Label Inference

via “zero-shot and few-shot learning via embedding similarity”

fill-mask model by undefined. 6,06,75,227 downloads.

Unique: Leverages pre-trained bidirectional context to generate semantically rich embeddings that generalize to unseen classes without task-specific fine-tuning; enables rapid prototyping and dynamic category addition

vs others: More practical than true zero-shot methods (e.g., natural language inference) because it uses simple cosine similarity, and more data-efficient than supervised fine-tuning for low-resource scenarios