Updated black version and ran black.

Machine-Learning-for-Medical-Language · Sep 27, 2024 · 99c0a2f · 99c0a2f
1 parent 03d46b3
commit 99c0a2f
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Showing 2 changed files with 10 additions and 10 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
 -   repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 23.7.0
+    rev: 24.8.0
     hooks:
     -   id: black
 -   repo: https://github.com/pycqa/flake8

diff --git a/src/cnlpt/train_system.py b/src/cnlpt/train_system.py
@@ -545,16 +545,16 @@ def main(
             # steps per epoch factors in gradient accumulation steps (as compared to batches_per_epoch above which doesn't)
             steps_per_epoch = int(total_steps // training_args.num_train_epochs)
             training_args.eval_steps = steps_per_epoch // training_args.evals_per_epoch
-            training_args.evaluation_strategy = (
-                training_args.eval_strategy
-            ) = IntervalStrategy.STEPS
+            training_args.evaluation_strategy = training_args.eval_strategy = (
+                IntervalStrategy.STEPS
+            )
             # This will save model per epoch
             # training_args.save_strategy = IntervalStrategy.EPOCH
         elif training_args.do_eval:
             logger.info("Evaluation strategy not specified so evaluating every epoch")
-            training_args.evaluation_strategy = (
-                training_args.eval_strategy
-            ) = IntervalStrategy.EPOCH
+            training_args.evaluation_strategy = training_args.eval_strategy = (
+                IntervalStrategy.EPOCH
+            )
 
     current_prediction_packet = deque()
 
@@ -662,9 +662,9 @@ def compute_metrics_fn(p: EvalPrediction):
                                     "w",
                                 ) as f:
                                     config_dict = model_args.to_dict()
-                                    config_dict[
-                                        "label_dictionary"
-                                    ] = dataset.get_labels()
+                                    config_dict["label_dictionary"] = (
+                                        dataset.get_labels()
+                                    )
                                     config_dict["task_names"] = task_names
                                     json.dump(config_dict, f)
                         for task_ind, task_name in enumerate(metrics):