update utils

src/display/utils.py

@@ -39,16 +39,30 @@ COLUMNS.append(
 )
 
 # Include per-subject accuracy columns based on your subjects
+# Remove unwanted subjects from here
 for task in Tasks:
-    COLUMNS.append(
-        ColumnContent(
-            name=task.value.benchmark,
-            type=float,
-            label=f"{task.value.col_name} (%)",
-            description=f"Accuracy on {task.value.col_name}",
-            displayed_by_default=False,
+    # Exclude unwanted tasks
+    if task.name not in [
+        "History",
+        "Mathematics",
+        "Science",
+        "Geography",
+        "Literature",
+        "Art",
+        "Physics",
+        "Chemistry",
+        "Biology",
+        "Computer_Science",
+    ]:
+        COLUMNS.append(
+            ColumnContent(
+                name=task.value.benchmark,
+                type=float,
+                label=f"{task.value.col_name} (%)",
+                description=f"Accuracy on {task.value.col_name}",
+                displayed_by_default=True,  # Set to True to display by default
+            )
         )
-    )
 
 # Additional columns
 COLUMNS.extend([
@@ -57,126 +71,47 @@ COLUMNS.extend([
         type=str,
         label="Model Type",
         description="Type of the model (e.g., Transformer, RNN, etc.)",
-        displayed_by_default=False,
-    ),
-    ColumnContent(
-        name="architecture",
-        type=str,
-        label="Architecture",
-        description="Model architecture",
-        displayed_by_default=False,
+        displayed_by_default=True,  # Set to True to display by default
     ),
     ColumnContent(
         name="weight_type",
         type=str,
         label="Weight Type",
         description="Type of model weights (e.g., Original, Delta, Adapter)",
-        displayed_by_default=False,
+        displayed_by_default=True,  # Set to True to display by default
     ),
     ColumnContent(
         name="precision",
         type=str,
         label="Precision",
         description="Precision of the model weights (e.g., float16)",
-        displayed_by_default=False,
+        displayed_by_default=True,  # Set to True to display by default
     ),
     ColumnContent(
         name="license",
         type=str,
         label="License",
         description="License of the model",
-        displayed_by_default=False,
-    ),
-    ColumnContent(
-        name="params",
-        type=float,
-        label="Parameters (B)",
-        description="Number of model parameters in billions",
-        displayed_by_default=False,
+        displayed_by_default=True,  # Set to True to display by default
     ),
     ColumnContent(
         name="likes",
         type=int,
         label="Likes",
         description="Number of likes on the Hugging Face Hub",
-        displayed_by_default=False,
+        displayed_by_default=True,  # Set to True to display by default
     ),
     ColumnContent(
         name="still_on_hub",
         type=bool,
         label="Available on the Hub",
         description="Whether the model is still available on the Hugging Face Hub",
-        displayed_by_default=False,
-    ),
-    ColumnContent(
-        name="revision",
-        type=str,
-        label="Model Revision",
-        description="Model revision or commit hash",
-        displayed_by_default=False,
+        displayed_by_default=True,  # Set to True to display by default
     ),
 ])
 
 # Now we can create lists of column names for use in the application
 COLS = [col.name for col in COLUMNS]
-BENCHMARK_COLS = [col.name for col in COLUMNS if col.name not in ["model", "average", "model_type", "architecture", "weight_type", "precision", "license", "params", "likes", "still_on_hub", "revision"]]
-
-# For the queue columns in the submission tab
-@dataclass(frozen=True)
-class EvalQueueColumn:
-    model: str
-    revision: str
-    private: bool
-    precision: str
-    weight_type: str
-    status: str
-
-EVAL_COLS = ["model", "revision", "private", "precision", "weight_type", "status"]
-EVAL_TYPES = [str, str, bool, str, str, str]
-
-## All the model information that we might need
-@dataclass
-class ModelDetails:
-    name: str
-    display_name: str = ""
-    symbol: str = ""  # emoji
-
-class ModelType(Enum):
-    PT = ModelDetails(name="pretrained", symbol="🟢")
-    FT = ModelDetails(name="fine-tuned", symbol="🔶")
-    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
-    RL = ModelDetails(name="RL-tuned", symbol="🟦")
-    Unknown = ModelDetails(name="", symbol="?")
-
-    def to_str(self, separator=" "):
-        return f"{self.value.symbol}{separator}{self.value.name}"
-
-    @staticmethod
-    def from_str(type_str):
-        if "fine-tuned" in type_str or "🔶" in type_str:
-            return ModelType.FT
-        if "pretrained" in type_str or "🟢" in type_str:
-            return ModelType.PT
-        if "RL-tuned" in type_str or "🟦" in type_str:
-            return ModelType.RL
-        if "instruction-tuned" in type_str or "⭕" in type_str:
-            return ModelType.IFT
-        return ModelType.Unknown
-
-class WeightType(Enum):
-    Adapter = "Adapter"
-    Original = "Original"
-    Delta = "Delta"
-
-class Precision(Enum):
-    float16 = "float16"
-    bfloat16 = "bfloat16"
-    Unknown = "Unknown"
-
-    @staticmethod
-    def from_str(precision_str):
-        if precision_str in ["torch.float16", "float16"]:
-            return Precision.float16
-        if precision_str in ["torch.bfloat16", "bfloat16"]:
-            return Precision.bfloat16
-        return Precision.Unknown
+BENCHMARK_COLS = [col.name for col in COLUMNS if col.name not in [
+    "model", "average", "model_type", "weight_type", "precision", "license", "likes", "still_on_hub"
+]]