def test_sentence_transformers_nli_bert_base_cls_pooling_embeddings(self):
        """A test to ensure the embeddings are generated correctly for the default text model, loading from hf.

        This model uses CLS pooling method.
        """
        model_properties = {
            "name": "sentence-transformers/nli-bert-base-cls-pooling",
            "dimensions": 768,
            "type": "hf",
        }

        model = HuggingFaceModel(model_properties, "cpu", {})
        model.load()
        embeddings = model.encode(['This is an example sentence'])
        difference = np.linalg.norm(embeddings - self.NLI_BERT_BASE_CLS_MODEL_EMBEDDINGS) / len(
            embeddings)
        self.assertLess(difference, 1e-4, f"Text embeddings for model {model_properties} "
                                          f"are not close enough. The average difference is: {difference}")

        difference_for_different_models = np.linalg.norm(embeddings - self.E5_BASE_V2_MODEL_EMBEDDINGS) / len(
            embeddings)

        self.assertGreater(
            difference_for_different_models, 1,
            f"Text embeddings for two different models are too close. "
            f"There is a problem with the test data or bug in the code."
        )
A test to ensure the embeddings are generated correctly for the default text model, loading from hf.

        This model uses CLS pooling method.
        
tests/integ_tests/core/inference/embedding_models/test_hugging_face_model.py