SlowGuess/ACL_2020_OCR huggingface dataset:
Multi-class	List-wise
AAP	TwADR-L	SMM4H-17	AAP	TwADR-L	SMM4H-17	MCN
learning_rate	1e-4	5e-5	5e-5	5e-5	5e-5	3e-5	3e-5
num_train_epochs	30	30	40	10	10	20	30
per_gpu_train_batch_size	32	16	32	16	16	16	8
save_steps	487	301	166	976	301	333	250
warmup_steps	1463	903	664	976	301	666	750
list size (k)	-	-	-	10	20	10	30
m1	-	-	-	0.0	0.0	0.0	0.1
m2	-	-	-	0.2	0.2	0.2	0.2
λ	-	-	-	0.6	0.4	0.4	0.4
μ	-	-	-	0.6	0.4	0.4	0.8
Table A1: Hyper-parameters for BERT-based multi-class and list-wise classifiers. AAP=AskAPatient. Terms with underscores are hyper-parameters in huggingface's pytorch implementation of BERT.
Table A1 shows the hyper-parameters for our models. We use huggingface's pytorch implementation of BERT. We tune the hyperparameters via grid search, and select the best BERT hyper-parameters based on the performance on the dev set.
To keep the size of the candidate list equal to  $k$  for every mention, we apply the following rules: if the list does not contain the gold concept and is already of length  $k$ , we inject the correct one and remove an incorrect candidate; if the list is not length of  $k$ , we inject the gold concept and the most frequent concepts in the training set to reach  $k$ .
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