Table 3.

Runs submitted during the n2c2 competition week and their scores as reported by contest organizers

Subtask	Run	System	Precision	Recall	F1
A	1	Seq2seq	0.9093	0.8925	0.9008
	2	Class-Ensemble	0.8360	0.8692	0.8522
	3	Class-RoBERTa	0.8213	0.8580	0.8392
B	1	Seq2seq	0.8108	0.7400	0.7738
	2	Class-RoBERTa	0.6921	0.7256	0.7085
	3	Class-Ensemble	0.6916	0.7170	0.7041
C	1	Seq2seq (SHAC${}_{\mathrm{M}}\to$ SHACW)	0.8906	0.8867	0.8886
	2	Seq2seq (SHACM + SHACW)	0.8800	0.8804	0.8802
	3	Class-RoBERTa (SHACM + SHACW)	0.7423	0.8468	0.7911

Note: Subtasks A, B, and C correspond to extraction, generalization, and transfer learning. “Class-Ensemble” is the ensemble of the classification-based approaches while “Class-RoBERTa” is the classification approach that used RoBERTa alone. In the transfer learning subtask C, “SHAC${}_{\mathrm{M}}\to$ SHAC_W” means that a model was first fine-tuned on SHAC_M and then fine-tuned on SHAC_W. “SHAC_M + SHAC_W” means that a model was fine-tuned on both SHAC_M and SHAC_W together. The highest scores are bolded.

SHAC: Social History Annotation Corpus.