SlowGuess/NAACL_2022_OCR huggingface dataset:
Baseline training methods are BERT-based in-scope classifier (MLE) (Larson et al., 2019; Devlin et al., 2019), Large Margin Cosine Loss (LMCL) (Zeng et al., 2021a), Domain Regularization Module (DRM) (Shen et al., 2021), entropy regularization (Reg.) (Zheng et al., 2020), and BERT-binary classifier (Binary) (Devlin et al., 2019). Post-processing methods are not applicable for Binary training since it models OOS detection as a binary classification problem. Baseline post-processing methods are Maximum Likelihood Estimate (MLE) (Gangal et al., 2020; Zhang et al., 2020), softmax temperature (Temp) (Liang et al., 2018; Lin and Xu, 2019b), standard deviation (Stdev), and entropy (Ent) (Shen et al., 2021).