diff --git a/src/jmteb/embedders/data_parallel_sbert_embedder.py b/src/jmteb/embedders/data_parallel_sbert_embedder.py
index 4cde996..d1b0b11 100644
--- a/src/jmteb/embedders/data_parallel_sbert_embedder.py
+++ b/src/jmteb/embedders/data_parallel_sbert_embedder.py
@@ -85,13 +85,11 @@ def encode(
             # Some models (e.g. INSTRUCTOR, GRIT) require removing the prompt before pooling
             # Tracking the prompt length allow us to remove the prompt during pooling
             tokenized_prompt = self.sbert.tokenize([prompt])
-            if "input_ids" in tokenized_prompt:
-                extra_features["prompt_length"] = tokenized_prompt["input_ids"].shape[-1] - 1
 
-        # When `include_prompt` is False in Pooling, prompt_length is unnecessary and should be removed.
-        # This prevents problems arising from DataParallel
-        if self.include_prompt_for_pooling():
-            _ = extra_features.pop("prompt_length")
+            # When `include_prompt` is True in Pooling, prompt_length is unnecessary and should be removed.
+            # This prevents problems arising from DataParallel
+            if "input_ids" in tokenized_prompt and not self.include_prompt_for_pooling():
+                extra_features["prompt_length"] = tokenized_prompt["input_ids"].shape[-1] - 1
 
         all_embeddings = []
         length_sorted_idx = np.argsort([-self.sbert._text_length(sen) for sen in sentences])