def test_hf_e5_base_v2_embeddings_load_from_zip_file(self):
        """A test to ensure the embeddings are generated correctly for the default text model, loading from
        a zip file on s3."""
        model_properties = {
            "url": "https://marqo-ecs-50-audio-test-dataset.s3.amazonaws.com/e5-li.zip",
            "dimensions": 768,
            "type": "hf",
        }
        model = HuggingFaceModel(model_properties, "cpu", {})
        model.load()
        embeddings = model.encode(['query: how much protein should a female eat'])
        difference = np.linalg.norm(embeddings - self.E5_BASE_V2_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}")
A test to ensure the embeddings are generated correctly for the default text model, loading from
        a zip file on s3.
tests/integ_tests/core/inference/embedding_models/test_hugging_face_model.py