F1: 82.8Precision: 88.3Recall: 78.0mAP: 93.7
Papers with Code · CC BY-SA 4.0
Multi-Label Text Classification
According to Wikipedia "In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to."
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
벤치마크
Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.
데이터셋
20개논문
Micro F1: 71.7
Micro-F1: 79.3Macro-F1: 47.3
Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
P@1: 84.48P@3: 60.72P@5: 41.19nDCG@3: 80.11
P@1: 85.28P@3: 61.12P@5: 41.84nDCG@3: 80.84
Micro F1: 72.8
F1: 69.6
F1: 69.8Precision: 59.8Recall: 83.7Accuracy: 79.6
F1: 62.1Precision: 69.3Recall: 56.2Accuracy: 80.9
라이브러리
Papers with Code 리더보드 row에 연결된 구현 저장소입니다.