.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
     hooks:
     -   id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: 'v0.9.9'
+    rev: 'v0.9.10'
     hooks:
         - id: ruff
           args: [--fix, --exit-non-zero-on-fix]

elk/extraction/extraction.py

@@ -1,5 +1,4 @@
 """Functions for extracting the hidden states of a model."""
-
 import os
 from collections import defaultdict
 from dataclasses import InitVar, dataclass, replace

elk/plotting/visualize.py

@@ -78,9 +78,9 @@ def render(
                         y=dataset_data["auroc_estimate"],
                         mode="lines",
                         name=ensemble,
-                        showlegend=(
-                            False if dataset_name != unique_datasets[0] else True
-                        ),
+                        showlegend=False
+                        if dataset_name != unique_datasets[0]
+                        else True,
                         line=dict(color=color_map[ensemble]),
                     ),
                     row=row,

elk/promptsource/templates.py

@@ -215,11 +215,9 @@ def _escape_pipe(cls, example):
         # Replaces any occurrences of the "|||" separator in the example, which
         # which will be replaced back after splitting
         protected_example = {
-            key: (
-                value.replace("|||", cls.pipe_protector)
-                if isinstance(value, str)
-                else value
-            )
+            key: value.replace("|||", cls.pipe_protector)
+            if isinstance(value, str)
+            else value
             for key, value in example.items()
         }
         return protected_example

elk/training/platt_scaling.py

@@ -12,7 +12,8 @@ class PlattMixin(ABC):
     scale: nn.Parameter
 
     @abstractmethod
-    def __call__(self, *args: Any, **kwds: Any) -> Any: ...
+    def __call__(self, *args: Any, **kwds: Any) -> Any:
+        ...
 
     def platt_scale(self, labels: Tensor, hiddens: Tensor, max_iter: int = 100):
         """Fit the scale and bias terms to data with LBFGS.