content: [model]
  package = "bonito.crf"
  
  [labels]
  labels = [ "N", "A", "C", "G", "T",]
  
  [input]
  features = 1
  
  [global_norm]
  state_len = 4
  
  [run_info]
  sample_rate = 5000
  
  [encoder]
  type = "serial"
  [[encoder.sublayers]]
  type = "convolution"
  insize = 1
  size = 16
  bias = true
  winlen = 5
  stride = 1
  padding = 2
  activation = "swish"
  norm = "batchnorm"
  
  [[encoder.sublayers]]
  type = "clamp"
  min = -0.5
  max = 3.5
  
  [[encoder.sublayers]]
  type = "convolution"
  insize = 16
  size = 16
  bias = true
  winlen = 5
  stride = 1
  padding = 2
  activation = "swish"
  norm = "batchnorm"
  
  [[encoder.sublayers]]
  type = "clamp"
  min = -0.5
  max = 3.5
  
  [[encoder.sublayers]]
  type = "convolution"
  insize = 16
  size = 384
  bias = true
  winlen = 19
  stride = 6
  padding = 9
  activation = "swish"
  norm = "batchnorm"
  
  [[encoder.sublayers]]
  type = "clamp"
  min = -0.5
  max = 3.5
  
  [[encoder.sublayers]]
  type = "permute"
  dims = [ 2, 0, 1,]
  
  [[encoder.sublayers]]
  type = "lstm"
  size = 384
  insize = 384
  bias = true
  reverse = 1
  
  [[encoder.sublayers]]
  type = "lstm"
  size = 384
  insize = 384
  bias = true
  reverse = 0
  
  [[encoder.sublayers]]
  type = "lstm"
  size = 384
  insize = 384
  bias = true
  reverse = 1
  
  [[encoder.sublayers]]
  type = "lstm"
  size = 384
  insize = 384
  bias = true
  reverse = 0
  
  [[encoder.sublayers]]
  type = "lstm"
  size = 384
  insize = 384
  bias = true
  reverse = 1
  
  [[encoder.sublayers]]
  type = "linear"
  in_features = 384
  out_features = 128
  
  [[encoder.sublayers]]
  type = "linearcrfencoder"
  insize = 128
  n_base = 4
  state_len = 4
  bias = false
  blank_score = 2.0
  
  [[encoder.sublayers]]
  type = "clamp"
  min = -5.0
  max = 5.0
  
  [qscore]
  scale = 0.95
  bias = -0.2
  