Hyperparameters for GLUE:
 - Learning rate: 5e-5
 - Batch size: 64
 - Max epochs: 10
 - Patience: 10 (for CoLA, MRPC, RTE, BoolQ, MultiRC, and WSC), 100 (for MNLI, QQP, QNLI, and SST-2)
 - Random seed: 12
 - Weight decay: 0.1
 - Warmup ratio: 0.1
 - Learning rate scheduler: cosine
 - Eval strategy: epoch (for CoLA, MRPC, RTE, BoolQ, MultiRC, and WSC), steps (for MNLI, QQP, QNLI, and SST-2)
 - Eval every: 1 (for CoLA, MRPC, RTE, BoolQ, MultiRC, and WSC), 200 (for SST-2 and QNLI), 500 (for MNLI and QQP)

Hyperparameters for MSGS:
 - Learning rate: 5e-5 (for CR, SC, RP, MV_RTP, and SC_LC), 1.5e-5 (for LC), 1e-5 (for SC_RP), 8e-6 (for MV_LC), 5e-6 (for MV), 5e-7 (CR_LC)
 - Batch size: 32
 - Max epochs: 10 (for CR, SC, RP, MV_RTP, SC_LC, SC_RP, MV, and CR_LC), 3 (for LC), 5 (for MV_LC)
 - Patience: 10 (for CR, SC, RP, MV_RTP, SC_LC, SC_RP, MV, and CR_LC), 3 (for LC), 5 (for MV_LC)
 - Random seed: 12
 - Weight decay: 0.1
 - Warmup ratio: 0.1
 - Learning rate scheduler: cosine
 - Eval strategy: epoch
 - Eval every: 1