style: ran linting

src/seb/benchmark.py

@@ -212,13 +212,6 @@ def evaluate_models(
         for model in pbar:
             pbar.set_description(f"Running {model.meta.name}")
             results.append(
-                self.evaluate_model(
-                    model,
-                    use_cache=use_cache,
-                    run_model=run_model,
-                    raise_errors=raise_errors,
-                    cache_dir=cache_dir,
-                    verbose=verbose
-                ),
+                self.evaluate_model(model, use_cache=use_cache, run_model=run_model, raise_errors=raise_errors, cache_dir=cache_dir, verbose=verbose),
             )
         return results

src/seb/registered_tasks/mteb_tasks.py

@@ -103,7 +103,6 @@ def description(self) -> dict[str, Any]:
         }
 
 
-
 class SwednSummarizationSTS(AbsTaskSTS):
     def load_data(self, **kwargs: dict):  # noqa: ARG002
         """
@@ -249,9 +248,7 @@ def dataset_transform(self) -> None:
                     n += 1
                 cor_n = text2id[art]
 
-                self.relevant_docs[split][str(q_n)] = {
-                    str(text2id[art]): 1, str(text2id[summ]): 1
-                }  # only two correct matches
+                self.relevant_docs[split][str(q_n)] = {str(text2id[art]): 1, str(text2id[summ]): 1}  # only two correct matches
 
 
 class NorwegianCourtsBitextMining(AbsTaskBitextMining):
@@ -291,4 +288,3 @@ def dataset_transform(self) -> None:
         # Convert to standard format
         self.dataset = self.dataset.rename_column("nb", "sentence1")
         self.dataset = self.dataset.rename_column("nn", "sentence2")
-

src/seb/registered_tasks/swedish.py

@@ -57,6 +57,7 @@ def create_swedn_retrieval() -> Task:
     task.domain = ["non-fiction", "news"]
     return task
 
+
 @tasks.register("SwednClustering")
 def create_swedn_clustering() -> Task:
     from seb.registered_tasks.mteb_tasks_clustering import SwednClustering