Delete configuration_neuroclr.py

configuration_neuroclr.py

@@ -1,59 +0,0 @@
-# classification/configuration_neuroclr.py
-from transformers import PretrainedConfig
-
-class NeuroCLRConfig(PretrainedConfig):
-    model_type = "neuroclr"
-
-    def __init__(
-        self,
-        # Encoder / SSL
-        TSlength: int = 128,
-        nhead: int = 4,
-        nlayer: int = 4,
-        projector_out1: int = 256,
-        projector_out2: int = 128,
-        pooling: str = "flatten",      # input is [B,1,128]
-        normalize_input: bool = True,
-
-        # Classification
-        n_rois: int = 200,
-        num_labels: int = 2,
-
-        # ResNet1D head hyperparams
-        base_filters: int = 256,
-        kernel_size: int = 16,
-        stride: int = 2,
-        groups: int = 32,
-        n_block: int = 48,
-        downsample_gap: int = 6,
-        increasefilter_gap: int = 12,
-        use_bn: bool = True,
-        use_do: bool = True,
-
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        # Encoder
-        self.TSlength = TSlength
-        self.nhead = nhead
-        self.nlayer = nlayer
-        self.projector_out1 = projector_out1
-        self.projector_out2 = projector_out2
-        self.pooling = pooling
-        self.normalize_input = normalize_input
-
-        # Classification
-        self.n_rois = n_rois
-        self.num_labels = num_labels
-
-        # ResNet1D head
-        self.base_filters = base_filters
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.groups = groups
-        self.n_block = n_block
-        self.downsample_gap = downsample_gap
-        self.increasefilter_gap = increasefilter_gap
-        self.use_bn = use_bn
-        self.use_do = use_do