Protein Engineering

ube4b · Linear

reference_ube4b_lr_plm_contiguousf0_20261104-175856

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10tie_share_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50tie_share_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100tie_share_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pcttie_share_at_1pctcaptured_at_1pct
train2.85040.60420.59840.31734241.00000.53473.00001.00006.25659.18450.30000.00000.90401.00004.74899.18450.30000.00000.83080.99004.40169.18450.39000.00000.84600.98123.99999.18450.50230.00000.8667
validation3.35650.24110.25380.0542458.0000-0.04322381.00000.5000-0.43991.03880.00000.00000.21590.3600-0.80153.46860.04000.00000.18920.3300-0.84483.46860.08000.00000.20420.4231-0.61483.46860.03850.00000.2078
test2.27130.20060.17260.00031218.00000.00181055.00000.5000-0.01193.14150.00000.00000.15530.5200-0.20563.14150.02000.00000.14590.4100-0.35893.14150.02000.00000.12580.5349-0.07073.14150.02330.00000.1734

Loss per epoch

training validation

Config

name: reference_ube4b_lr_plm_contiguousf0
task: dms
data:
  dataset: ube4b
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  split_scheme: contiguous
  split_fold: 0
model:
  name: lr
train:
  epochs: 300
  batch_size: 64
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7