Table II.

F1-scores of six NER models on five medical dataset. The best performance in 5-shot settings and 1-shot settings has been highlighted in bold and underlined.

Models	Training Size	N2C2 2018	I2B2 2014	MIMIC III	BioNLP 2016	SMM4H 2021
SANER (traditional NER model)	Whole training data	80.63	90.62	66.57	81.78	44.56
	10% training data	79.27	80.67	46.68	70.50	23.4
	5-shot	10.27	36.38	21.25	23.14	0.00
	1-shot	7.92	31.14	7.07	4.32	0.00
BERT + Classifier (traditional NER model)	Whole training data	59.47	76.47	65.71	81.23	47.30
	10% training data	42.32	34.69	30.29	58.44	25.83
	5-shot	3.27	0.00	0.00	1.71	0.00
	1-shot	0.00	0.21	0.57	0.15	0.00
BERT + CRF (traditional NER model)	Whole training data	82.79	80.63	59.58	77.62	45.45
	10% training data	64.09	27.84	20.5	61.35	2.74
	5-shot	0.00	0.00	0.00	0.00	0.00
	1-shot	0.00	0.00	0.00	0.00	0.00
StructShot (few-shot model)	5-shot	25.44	20.30	3.18	0.03	0.00
	1-shot	17.59	20.26	0.63	0.00	0.00
NNShot (few-shot model)	5-shot	25.29	19.73	19.71	28.88	0.00
	1-shot	16.70	16.35	15.37	6.42	0.00
FewShot-Tagging (few-shot model)	5-shot	0.94	0.27	0.60	3.32	0.32
	1-shot	4.59	0.14	5.17	6.81	0.35