move ST tests to test_modeling.py since it's a single test

.github/workflows/test_openvino.yml

@@ -22,7 +22,6 @@ jobs:
           [
             "*modeling.py",
             "*modeling_diffusion.py",
-            "*modeling_sentence_transformers.py",
             "*quantization*",
             "*training*",
             "*export*",

tests/openvino/test_modeling.py

@@ -32,6 +32,7 @@
 from huggingface_hub import HfApi
 from parameterized import parameterized
 from PIL import Image
+from sentence_transformers import SentenceTransformer
 from transformers import (
     AutoConfig,
     AutoFeatureExtractor,
@@ -2360,3 +2361,36 @@ def test_functions(self):
 
         del model
         gc.collect()
+
+
+class OVModelForSTFeatureExtractionIntegrationTest(unittest.TestCase):
+    SUPPORTED_ARCHITECTURES = ("st-bert", "st-mpnet")
+
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    def test_compare_to_transformers(self, model_arch):
+        model_id = MODEL_NAMES[model_arch]
+        set_seed(SEED)
+        ov_model = OVSentenceTransformer.from_pretrained(model_id, export=True, ov_config=F32_CONFIG)
+        self.assertIsInstance(ov_model.config, PretrainedConfig)
+        self.assertTrue(hasattr(ov_model, "encode"))
+        st_model = SentenceTransformer(model_id)
+        sentences = ["This is an example sentence", "Each sentence is converted"]
+        st_embeddings = st_model.encode(sentences)
+        ov_embeddings = ov_model.encode(sentences)
+        # Compare tensor outputs
+        self.assertTrue(np.allclose(ov_embeddings, st_embeddings, atol=1e-4))
+        del st_embeddings
+        del ov_model
+        gc.collect()
+
+    @parameterized.expand(SUPPORTED_ARCHITECTURES)
+    def test_sentence_transformers_save_and_infer(self, model_arch):
+        model_id = MODEL_NAMES[model_arch]
+        ov_model = OVSentenceTransformer.from_pretrained(model_id, export=True, ov_config=F32_CONFIG)
+        with TemporaryDirectory() as tmpdirname:
+            model_save_path = os.path.join(tmpdirname, "sentence_transformers_ov_model")
+            ov_model.save_pretrained(model_save_path)
+            model = OVSentenceTransformer.from_pretrained(model_save_path)
+            sentences = ["This is an example sentence", "Each sentence is converted"]
+            model.encode(sentences)
+        gc.collect()

tests/openvino/utils_tests.py

@@ -148,6 +148,8 @@
     "glm4": "katuni4ka/tiny-random-glm4",
     "open-clip": "hf-internal-testing/tiny-open-clip-model",
     "open-clip-ov": "zofinka/tiny-open-clip-model",
+    "st-bert": "sentence-transformers/all-MiniLM-L6-v2",
+    "st-mpnet": "sentence-transformers/all-mpnet-base-v2",
 }