F1-score: 46
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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."
논문
AUC: 0.919Macro F1: 57.1P@5: 62.5Macro Precision: 65
F1: 69.6
Weighted F1: 0.9072Average F1: 0.8882
Precision@1: 40.74
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
F1: 82.8Precision: 88.3Recall: 78.0mAP: 93.7
Precision: 0.249Recall: 0.1138
Micro F1: 71.7
Micro-F1: 79.3Macro-F1: 47.3
P@1: 85.28P@3: 61.12P@5: 41.84nDCG@3: 80.84
Micro F1: 72.8
Micro-F1: 68.555
F1: 69.8Precision: 59.8Recall: 83.7Accuracy: 79.6
F1: 66.83%
F1: 62.1Precision: 69.3Recall: 56.2Accuracy: 80.9