Adding InferenceClient.get_recommended_model

src/huggingface_hub/inference/_client.py

@@ -1870,6 +1870,11 @@ def _resolve_url(self, model: Optional[str] = None, task: Optional[str] = None)
                     "You must specify at least a model (repo_id or URL) or a task, either when instantiating"
                     " `InferenceClient` or when making a request."
                 )
+            logger.info(
+                f"Using recommended model {model} for task {task}. Note that it is"
+                f" encouraged to explicitly set `model='{model}'` as the recommended"
+                " models list might get updated without prior notice."
+            )
             model = _get_recommended_model(task)
 
         # Compute InferenceAPI url
@@ -1881,6 +1886,24 @@ def _resolve_url(self, model: Optional[str] = None, task: Optional[str] = None)
             else f"{INFERENCE_ENDPOINT}/models/{model}"
         )
 
+    @staticmethod
+    def get_recommended_model(task) -> str:
+        """
+        Get the model Hugging Face recommends for the input task.
+
+        Args:
+            task (`str`):
+                The Hugging Face task to get which model Hugging Face recommends.
+                All available tasks can be found [here](https://huggingface.co/tasks).
+
+        Returns:
+            `str`: Name of the model recommended for the input task.
+
+        Raises:
+            `ValueError`: If Hugging Face has no recommendation for the input task.
+        """
+        return _get_recommended_model(task)
+
     def get_model_status(self, model: Optional[str] = None) -> ModelStatus:
         """
         Get the status of a model hosted on the Inference API.

src/huggingface_hub/inference/_common.py

@@ -147,10 +147,6 @@ def _get_recommended_model(task: str) -> str:
             f"Task {task} has no recommended task. Please specify a model explicitly. Visit"
             " https://huggingface.co/tasks for more info."
         )
-    logger.info(
-        f"Using recommended model {model} for task {task}. Note that it is encouraged to explicitly set"
-        f" `model='{model}'` as the recommended models list might get updated without prior notice."
-    )
     return model
 
 

src/huggingface_hub/inference/_generated/_async_client.py

@@ -1898,6 +1898,11 @@ def _resolve_url(self, model: Optional[str] = None, task: Optional[str] = None)
                     "You must specify at least a model (repo_id or URL) or a task, either when instantiating"
                     " `InferenceClient` or when making a request."
                 )
+            logger.info(
+                f"Using recommended model {model} for task {task}. Note that it is"
+                f" encouraged to explicitly set `model='{model}'` as the recommended"
+                " models list might get updated without prior notice."
+            )
             model = _get_recommended_model(task)
 
         # Compute InferenceAPI url
@@ -1909,6 +1914,24 @@ def _resolve_url(self, model: Optional[str] = None, task: Optional[str] = None)
             else f"{INFERENCE_ENDPOINT}/models/{model}"
         )
 
+    @staticmethod
+    def get_recommended_model(task) -> str:
+        """
+        Get the model Hugging Face recommends for the input task.
+
+        Args:
+            task (`str`):
+                The Hugging Face task to get which model Hugging Face recommends.
+                All available tasks can be found [here](https://huggingface.co/tasks).
+
+        Returns:
+            `str`: Name of the model recommended for the input task.
+
+        Raises:
+            `ValueError`: If Hugging Face has no recommendation for the input task.
+        """
+        return _get_recommended_model(task)
+
     async def get_model_status(self, model: Optional[str] = None) -> ModelStatus:
         """
         Get the status of a model hosted on the Inference API.

tests/test_inference_client.py

@@ -155,6 +155,14 @@ def test_fill_mask(self) -> None:
             {"score", "sequence", "token", "token_str"},
         )
 
+    def test_get_recommended_model_has_recommendation(self) -> None:
+        assert self.client.get_recommended_model("feature-extraction") == "facebook/bart-base"
+        assert self.client.get_recommended_model("translation") == "t5-small"
+
+    def test_get_recommended_model_no_recommendation(self) -> None:
+        with pytest.raises(ValueError):
+            self.client.get_recommended_model("text-generation")
+
     def test_image_classification(self) -> None:
         output = self.client.image_classification(self.image_file)
         self.assertIsInstance(output, list)