Delete configuration_sybil.py

configuration_sybil.py

@@ -1,63 +0,0 @@
-"""Sybil model configuration"""
-
-from transformers import PretrainedConfig
-from typing import Optional, List, Dict
-import json
-
-
-class SybilConfig(PretrainedConfig):
-    """
-    This is the configuration class to store the configuration of a [`SybilForRiskPrediction`].
-    It is used to instantiate a Sybil model according to the specified arguments, defining the model architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs.
-
-    Args:
-        hidden_dim (`int`, *optional*, defaults to 512):
-            Dimensionality of the hidden representations.
-        dropout (`float`, *optional*, defaults to 0.0):
-            The dropout probability for all fully connected layers.
-        max_followup (`int`, *optional*, defaults to 6):
-            Maximum number of years for risk prediction.
-        num_images (`int`, *optional*, defaults to 208):
-            Number of CT scan slices to process.
-        img_size (`List[int]`, *optional*, defaults to `[512, 512]`):
-            Size of input images after preprocessing.
-        voxel_spacing (`List[float]`, *optional*, defaults to `[0.703125, 0.703125, 2.5]`):
-            Target voxel spacing for CT scans (row, column, slice thickness).
-        censoring_distribution (`str`, *optional*, defaults to "weibull"):
-            Distribution used for censoring in survival analysis.
-        ensemble_size (`int`, *optional*, defaults to 5):
-            Number of models in the ensemble.
-        calibrator_data (`Dict`, *optional*):
-            Calibration data for risk score adjustment.
-    """
-
-    model_type = "sybil"
-
-    def __init__(
-        self,
-        hidden_dim: int = 512,
-        dropout: float = 0.0,
-        max_followup: int = 6,
-        num_images: int = 208,
-        img_size: List[int] = None,
-        voxel_spacing: List[float] = None,
-        censoring_distribution: str = "weibull",
-        ensemble_size: int = 5,
-        calibrator_data: Optional[Dict] = None,
-        initializer_range: float = 0.02,
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.hidden_dim = hidden_dim
-        self.dropout = dropout
-        self.max_followup = max_followup
-        self.num_images = num_images
-        self.img_size = img_size if img_size is not None else [512, 512]
-        self.voxel_spacing = voxel_spacing if voxel_spacing is not None else [0.703125, 0.703125, 2.5]
-        self.censoring_distribution = censoring_distribution
-        self.ensemble_size = ensemble_size
-        self.calibrator_data = calibrator_data
-        self.initializer_range = initializer_range