diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_benchmark.sh b/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_benchmark.sh
index 379ce7e981d..f75c0e7fb23 100644
--- a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_benchmark.sh
+++ b/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_benchmark.sh
@@ -57,7 +57,7 @@ function run_benchmark {
     if [[ ${mode} == "accuracy" ]]; then
         mode_cmd=" --accuracy_only"
     elif [[ ${mode} == "benchmark" ]]; then
-        mode_cmd=" --benchmark "
+        mode_cmd=" --benchmark --max_eval_samples 200 "
     else
         echo "Error: No such mode: ${mode}"
         exit 1
diff --git a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_translation.py b/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_translation.py
index 66be454ac0f..f7dbe387ce8 100644
--- a/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_translation.py
+++ b/examples/pytorch/nlp/huggingface_models/translation/quantization/ptq_dynamic/eager/run_translation.py
@@ -443,8 +443,6 @@ def preprocess_function(examples):
         if "train" not in raw_datasets:
             raise ValueError("--do_train requires a train dataset")
         train_dataset = raw_datasets["train"]
-        if data_args.max_train_samples is not None:
-            train_dataset = train_dataset.select(range(data_args.max_train_samples))
         with training_args.main_process_first(desc="train dataset map pre-processing"):
             train_dataset = train_dataset.map(
                 preprocess_function,
@@ -454,14 +452,14 @@ def preprocess_function(examples):
                 load_from_cache_file=not data_args.overwrite_cache,
                 desc="Running tokenizer on train dataset",
             )
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
 
     if training_args.do_eval:
         max_target_length = data_args.val_max_target_length
         if "validation" not in raw_datasets:
             raise ValueError("--do_eval requires a validation dataset")
         eval_dataset = raw_datasets["validation"]
-        if data_args.max_eval_samples is not None:
-            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
         with training_args.main_process_first(desc="validation dataset map pre-processing"):
             eval_dataset = eval_dataset.map(
                 preprocess_function,
@@ -471,14 +469,14 @@ def preprocess_function(examples):
                 load_from_cache_file=not data_args.overwrite_cache,
                 desc="Running tokenizer on validation dataset",
             )
+        if data_args.max_eval_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
 
     if training_args.do_predict:
         max_target_length = data_args.val_max_target_length
         if "test" not in raw_datasets:
             raise ValueError("--do_predict requires a test dataset")
         predict_dataset = raw_datasets["test"]
-        if data_args.max_predict_samples is not None:
-            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
         with training_args.main_process_first(desc="prediction dataset map pre-processing"):
             predict_dataset = predict_dataset.map(
                 preprocess_function,
@@ -488,6 +486,8 @@ def preprocess_function(examples):
                 load_from_cache_file=not data_args.overwrite_cache,
                 desc="Running tokenizer on prediction dataset",
             )
+        if data_args.max_predict_samples is not None:
+            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
 
     # Data collator
     label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id