Fix quality due to ruff release

examples/legacy/pytorch-lightning/run_glue.py

@@ -124,7 +124,7 @@ class GLUETransformer(BaseTransformer):
 
         results = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task, preds, out_label_ids)}
 
-        ret = {k: v for k, v in results.items()}
+        ret = dict(results.items())
         ret["log"] = results
         return ret, preds_list, out_label_list
 

examples/legacy/pytorch-lightning/run_ner.py

@@ -122,7 +122,7 @@ class NERTransformer(BaseTransformer):
         preds = np.argmax(preds, axis=2)
         out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0)
 
-        label_map = {i: label for i, label in enumerate(self.labels)}
+        label_map = dict(enumerate(self.labels))
         out_label_list = [[] for _ in range(out_label_ids.shape[0])]
         preds_list = [[] for _ in range(out_label_ids.shape[0])]
 
@@ -140,7 +140,7 @@ class NERTransformer(BaseTransformer):
             "f1": f1_score(out_label_list, preds_list),
         }
 
-        ret = {k: v for k, v in results.items()}
+        ret = dict(results.items())
         ret["log"] = results
         return ret, preds_list, out_label_list
 

examples/legacy/seq2seq/run_eval_search.py

@@ -34,7 +34,7 @@ task_score_names = {
 
 def parse_search_arg(search):
     groups = search.split()
-    entries = {k: vs for k, vs in (g.split("=") for g in groups)}
+    entries = dict((g.split("=") for g in groups))
     entry_names = list(entries.keys())
     sets = [[f"--{k} {v}" for v in vs.split(":")] for k, vs in entries.items()]
     matrix = [list(x) for x in itertools.product(*sets)]
@@ -105,7 +105,7 @@ def run_search():
     col_widths = {col: len(str(col)) for col in col_names}
     results = []
     for r in matrix:
-        hparams = {k: v for k, v in (x.replace("--", "").split() for x in r)}
+        hparams = dict((x.replace("--", "").split() for x in r))
         args_exp = " ".join(r).split()
         args_exp.extend(["--bs", str(args.bs)])  # in case we need to reduce its size due to CUDA OOM
         sys.argv = args_normal + args_exp

examples/legacy/token-classification/run_ner.py

@@ -158,7 +158,7 @@ def main():
 
     # Prepare CONLL-2003 task
     labels = token_classification_task.get_labels(data_args.labels)
-    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
+    label_map: Dict[int, str] = dict(enumerate(labels))
     num_labels = len(labels)
 
     # Load pretrained model and tokenizer

examples/legacy/token-classification/run_tf_ner.py

@@ -144,7 +144,7 @@ def main():
 
     # Prepare Token Classification task
     labels = token_classification_task.get_labels(data_args.labels)
-    label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}
+    label_map: Dict[int, str] = dict(enumerate(labels))
     num_labels = len(labels)
 
     # Load pretrained model and tokenizer