diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 43866e4b3..1366e0b38 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -162,13 +162,14 @@ def train(
                 progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
     except Exception as e:
         if output_path is not None:
+            # We don't want to swallow the traceback if we don't have a
+            # specific error.
             msg.warn(
                 f"Aborting and saving the final best model. "
-                f"Encountered exception: {str(e)}",
-                exits=1,
+                f"Encountered exception: {str(e)}"
             )
-        else:
-            raise e
+            nlp.to_disk(output_path / "model-final")
+        raise e
     finally:
         if output_path is not None:
             final_model_path = output_path / "model-final"
@@ -207,7 +208,7 @@ def create_evaluation_callback(
         scores = nlp.evaluate(dev_examples)
         # Calculate a weighted sum based on score_weights for the main score
         try:
-            weighted_score = sum(scores[s] * weights.get(s, 0.0) for s in weights)
+            weighted_score = sum(scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights)
         except KeyError as e:
             keys = list(scores.keys())
             err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
@@ -377,7 +378,7 @@ def setup_printer(
 
         try:
             scores = [
-                "{0:.2f}".format(float(info["other_scores"][col])) for col in score_cols
+                "{0:.2f}".format(float(info["other_scores"].get(col, 0.0))) for col in score_cols
             ]
         except KeyError as e:
             raise KeyError(
@@ -403,7 +404,7 @@ def update_meta(
 ) -> None:
     nlp.meta["performance"] = {}
     for metric in training["score_weights"]:
-        nlp.meta["performance"][metric] = info["other_scores"][metric]
+        nlp.meta["performance"][metric] = info["other_scores"].get(metric, 0.0)
     for pipe_name in nlp.pipe_names:
         nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]