Multilingual Multi-label Text Classification on HumSet (test) benchmark leaderboard · SOTA2 Research

Multilingual Multi-label Text Classification on HumSet (test)

73.32SectorsSCALEARN

Evaluation Results

Method	Links
SCALEARN2023.10		73.32	53.94	44.14	63.89	44.75	56.01
PROPETL2023.10		73.2	51.58	45.1	61.52	41.98	54.68
SCALEARN++2023.10		73.18	51.41	44.1	63.37	45.43	55.5
SCALEARNUNIFORM++2023.10		73.02	50.84	44.88	62.87	44.45	55.21
FINETUNE2023.10		72.99	51.38	44.84	61.9	43.49	54.92
SCALEARNUNIFORM2023.10		72.56	50.59	44.62	62.66	45.16	55.12
ADAPTERFUSION2023.10		72.53	51.33	43.75	62.31	42.78	54.54
ADAPTER2023.10		72.29	49.31	45.25	62.58	44.36	54.76
LORA2023.10		72.22	52.15	0	61.34	0	37.14
PROPETL-M2023.10		70.56	41.58	35.91	42.2	29.67	43.98
ADAPTER-M2023.10		65.66	37.65	28.51	43.4	27.44	40.53
(IA)32023.10		64.72	38.26	26.77	55.57	31.11	43.29
COMPACTER++2023.10		61.77	8.17	6.37	20.39	15.36	22.41
FINETUNE-M2023.10		59.04	22.95	10.75	29.76	9.65	26.43
ADAPTERSOUP2023.10		52.54	24.07	20.62	31.16	12.84	28.25
HYPERFORMER2023.10		47.74	29.06	22.16	35.92	22.58	31.49
HYPERFORMER++2023.10		0	0	0	0	0	0