ORTModelForFeatureExtraction always exports as transformers models

optimum/onnxruntime/modeling_ort.py

@@ -1002,6 +1002,60 @@ def forward(
             # converts output to namedtuple for pipelines post-processing
             return BaseModelOutput(last_hidden_state=last_hidden_state)
 
+    @classmethod
+    def _export(
+        cls,
+        model_id: str,
+        config: "PretrainedConfig",
+        use_auth_token: Optional[Union[bool, str]] = None,
+        revision: Optional[str] = None,
+        force_download: bool = False,
+        cache_dir: Optional[str] = None,
+        subfolder: str = "",
+        local_files_only: bool = False,
+        trust_remote_code: bool = False,
+        provider: str = "CPUExecutionProvider",
+        session_options: Optional[ort.SessionOptions] = None,
+        provider_options: Optional[Dict[str, Any]] = None,
+        use_io_binding: Optional[bool] = None,
+        task: Optional[str] = None,
+    ) -> "ORTModel":
+        if task is None:
+            task = cls._auto_model_to_task(cls.auto_model_class)
+
+        save_dir = TemporaryDirectory()
+        save_dir_path = Path(save_dir.name)
+
+        # ORTModelForFeatureExtraction works with Transformers type of models, thus even sentence-transformers models are loaded as such.
+        main_export(
+            model_name_or_path=model_id,
+            output=save_dir_path,
+            task=task,
+            do_validation=False,
+            no_post_process=True,
+            subfolder=subfolder,
+            revision=revision,
+            cache_dir=cache_dir,
+            use_auth_token=use_auth_token,
+            local_files_only=local_files_only,
+            force_download=force_download,
+            trust_remote_code=trust_remote_code,
+            library_name="transformers",
+        )
+
+        config.save_pretrained(save_dir_path)
+        maybe_save_preprocessors(model_id, save_dir_path, src_subfolder=subfolder)
+
+        return cls._from_pretrained(
+            save_dir_path,
+            config,
+            use_io_binding=use_io_binding,
+            model_save_dir=save_dir,
+            provider=provider,
+            session_options=session_options,
+            provider_options=provider_options,
+        )
+
 
 MASKED_LM_EXAMPLE = r"""
     Example of feature extraction:

tests/onnxruntime/test_modeling.py

@@ -1227,7 +1227,7 @@ def test_trust_remote_code(self):
 class ORTModelForQuestionAnsweringIntegrationTest(ORTModelTestMixin):
     SUPPORTED_ARCHITECTURES = [
         "albert",
-        "bart", 
+        "bart",
         "bert",
         # "big_bird",
         # "bigbird_pegasus",
@@ -1592,7 +1592,7 @@ def test_compare_to_io_binding(self, model_arch):
 class ORTModelForSequenceClassificationIntegrationTest(ORTModelTestMixin):
     SUPPORTED_ARCHITECTURES = [
         "albert",
-        "bart", 
+        "bart",
         "bert",
         # "big_bird",
         # "bigbird_pegasus",