diff --git a/demos/embeddings/ovms_mteb.py b/demos/embeddings/ovms_mteb.py
index 80c7b5678a..b08019f5ec 100644
--- a/demos/embeddings/ovms_mteb.py
+++ b/demos/embeddings/ovms_mteb.py
@@ -14,25 +14,38 @@
 # limitations under the License.
 #
 
-from __future__ import annotations
-
 import argparse
 
 import mteb
 from mteb.models.model_implementations.openai_models import OpenAIModel
 from openai import OpenAI
 
-parser = argparse.ArgumentParser(description='Run MTEB benchmark against OVMS embeddings endpoint')
+parser = argparse.ArgumentParser(
+    description="Run MTEB benchmark against OVMS embeddings endpoint")
 parser.add_argument('--service_url', required=False, default='http://localhost:8000/v3/embeddings',
-                    help='Specify url to embeddings endpoint. default:http://localhost:8000/v3/embeddings', dest='service_url')
-parser.add_argument('--model_name', default='Alibaba-NLP/gte-large-en-v1.5', help='Model name to query. default: Alibaba-NLP/gte-large-en-v1.5',
+                    help='Specify url to embeddings endpoint.',
+                    dest='service_url')
+parser.add_argument('--model_name', default='Alibaba-NLP/gte-large-en-v1.5',
+                    help='Model name to query. default: Alibaba-NLP/gte-large-en-v1.5',
                     dest='model_name')
-parser.add_argument('--dataset', default='Banking77Classification', help='Dataset to benchmark. default: Banking77Classification',
+parser.add_argument('--dataset', default='Banking77Classification',
+                    help='Dataset to benchmark. default: Banking77Classification',
                     dest='dataset')
-parser.add_argument('--embed_dim', type=int, default=None, help='Embedding dimension. Auto-detected if not provided.',
+parser.add_argument('--results_dir', default='results', help='Results directory. default: results',
+                    dest='results_dir')
+parser.add_argument('--verbosity', choices=['0', '1', '2', '3'], default='3', )
+parser.add_argument('--overwrite_results', action='store_true', default=True, )
+parser.add_argument('--eval_splits', nargs='*', default=None,
+                    help='Evaluation splits to use, e.g. --eval_splits test dev. If not set, '
+                         'all splits defined in the task are used.',
+                    dest='eval_splits')
+parser.add_argument('--embed_dim', type=int, default=None,
+                    help='Embedding dimension. Auto-detected if not provided.',
                     dest='embed_dim')
-parser.add_argument('--max_tokens', type=int, default=99999, help='Max input tokens for truncation. default: 99999',
+parser.add_argument('--max_tokens', type=int, default=99999,
+                    help='Max input tokens for truncation. default: 99999',
                     dest='max_tokens')
+
 args = vars(parser.parse_args())
 
 client = OpenAI(base_url=args['service_url'], api_key="unused")
@@ -48,10 +61,17 @@
     embed_dim=embed_dim,
     client=client,
 )
-
-tasks = mteb.get_task(args['dataset'])
+tasks = mteb.get_task(args['dataset'], eval_splits=args["eval_splits"])
 evaluation = mteb.MTEB(tasks=[tasks])
-evaluation.run(model, verbosity=3, overwrite_results=True, output_folder='results')
+results = evaluation.run(model=model,
+                         verbosity=int(args['verbosity']),
+                         overwrite_results=args['overwrite_results'],
+                         output_folder=args["results_dir"]
+                         )
+for task_results in results:
+    # Banking77Classification: 0.8495 mean accuracy
+    print(f"{task_results.task_name}: {task_results.get_score():.4f} mean {task_results.task.metadata.main_score}")
+
 # For full leaderboard tests set run:
 # benchmark = mteb.get_benchmark("MTEB(eng)")
 # evaluation = mteb.MTEB(tasks=benchmark)