SlowGuess/NAACL_2022_OCR huggingface dataset:
Statistics	DWIE	DocRED
# Train	602	3053
# Dev	98	1000
# Test	99	1000
# Relations	65	96
# Relation facts	19493	56354
Avg.# mentions per Ent.	1.98	1.34
Table 3: Statistics of the two datasets.
Hyper-parameter	DWIE	DocRED
Batch size	4	8
Learning rate	3e-5	5e-5
Epoch	40	60
Gradient clipping	1	1
Warmup ratio	0.1	0.1
Table 4: Hyper-parameter settings for our experiments on the two datasets.
In this appendix, we introduce more details of our experimental settings. We implemented our RS-MAN with PyTorch and trained it with an NVIDIA GeForce RTX 3090 GPU. In addition, we adopted AdamW (Loshchilov and Hutter, 2018) as our optimizer and used learning rate linear schedule with warming up based on Huggingface's Transformers (Wolf et al., 2019). The hyper-parameter settings
of our experiments on the two datasets are listed in Table 4, which were decided through our tuning studies.