Multi Label Phi Classification

via “multi-label-phi-classification”

token-classification model by undefined. 14,64,632 downloads.

Unique: Trained on radiology-specific PHI annotations, capturing entity type distributions and patterns unique to imaging reports (e.g., frequent institution names, date formats in imaging protocols). Uses PubMedBERT's biomedical vocabulary to better recognize medical entity types.

vs others: Provides entity-type granularity that generic NER models lack, enabling selective redaction strategies, while maintaining higher accuracy on clinical PHI types compared to general-purpose entity classifiers.

via “multi-label-classification-via-independent-scoring”

zero-shot-classification model by undefined. 2,25,548 downloads.

Unique: Leverages NLI entailment scoring to enable multi-label classification without task-specific fine-tuning; each label treated as independent hypothesis allows flexible label combinations vs. single-label softmax approaches

vs others: More flexible than single-label zero-shot classifiers; avoids label correlation assumptions that multi-label neural networks require, enabling dynamic label sets at inference time

via “multi-label classification with confidence thresholding”

zero-shot-classification model by undefined. 56,557 downloads.

Unique: Produces continuous similarity scores for all candidate labels simultaneously, enabling threshold-based multi-label assignment without architectural changes, unlike single-label classifiers that require ensemble or post-processing hacks

vs others: More flexible than hard single-label classifiers and requires no additional model training or ensemble logic, while maintaining the zero-shot capability across arbitrary label sets

via “multi-label classification with hypothesis ranking”

zero-shot-classification model by undefined. 70,019 downloads.

Unique: Applies BART's entailment scoring independently to each label, avoiding the computational overhead of traditional multi-label classifiers that require label-interaction modeling. This design trades label correlation awareness for simplicity and zero-shot adaptability.

vs others: Simpler and faster than multi-label neural classifiers (e.g., sigmoid-output models) for dynamic label sets, but sacrifices label dependency modeling that specialized multi-label methods (e.g., label-powerset, structured prediction) provide.