diff --git a/examples/sentence_transformer/training/ms_marco/train_bi-encoder_mnrl.py b/examples/sentence_transformer/training/ms_marco/train_bi-encoder_mnrl.py
index 5e7652f22..3df16fc0e 100644
--- a/examples/sentence_transformer/training/ms_marco/train_bi-encoder_mnrl.py
+++ b/examples/sentence_transformer/training/ms_marco/train_bi-encoder_mnrl.py
@@ -14,7 +14,7 @@
 With a distilbert-base-uncased model, it should achieve a performance of about 33.79 MRR@10 on the MSMARCO Passages Dev-Corpus
 
 Running this script:
-python train_bi-encoder-v3.py
+python train_bi-encoder_mnrl.py --model_name distilbert-base-uncased
 """
 
 import argparse
@@ -28,9 +28,11 @@
 from datetime import datetime
 
 import tqdm
-from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import Dataset
 
 from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
+from sentence_transformers.trainer import SentenceTransformerTrainer
+from sentence_transformers.training_args import SentenceTransformerTrainingArguments
 
 #### Just some code to print debug information to stdout
 logging.basicConfig(
@@ -243,21 +245,34 @@ def __len__(self):
         return len(self.queries)
 
 
-# For training the SentenceTransformer model, we need a dataset, a dataloader, and a loss used for training.
+# For training the SentenceTransformer model, we need a dataset and a loss function
 train_dataset = MSMARCODataset(train_queries, corpus=corpus)
-train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
 train_loss = losses.MultipleNegativesRankingLoss(model=model)
 
-# Train the model
-model.fit(
-    train_objectives=[(train_dataloader, train_loss)],
-    epochs=num_epochs,
+# Define the training arguments
+args_training = SentenceTransformerTrainingArguments(
+    output_dir=model_save_path,
+    num_train_epochs=num_epochs,
+    per_device_train_batch_size=train_batch_size,
     warmup_steps=args.warmup_steps,
-    use_amp=True,
-    checkpoint_path=model_save_path,
-    checkpoint_save_steps=len(train_dataloader),
-    optimizer_params={"lr": args.lr},
+    learning_rate=args.lr,
+    fp16=True,  
+    bf16=False,  
+    save_strategy="steps",
+    save_steps=len(train_dataset) // train_batch_size,  
+    save_total_limit=2,
+    logging_steps=100,
 )
 
+# Create the trainer & start training
+trainer = SentenceTransformerTrainer(
+    model=model,
+    args=args_training,
+    train_dataset=train_dataset,
+    loss=train_loss,
+)
+trainer.train()
+
 # Save the model
-model.save(model_save_path)
+final_output_dir = f"{model_save_path}/final"
+model.save(final_output_dir)