feat!: Rename model_name_or_path to model in `TransformersSimilar…

…ityRanker` (#6734)

* rename model parameter in transformers ranker

* fix tests for transformers ranker

* reno

* reno

* typo

haystack/components/rankers/transformers_similarity.py

@@ -37,7 +37,7 @@ class TransformersSimilarityRanker:
 
     def __init__(
         self,
-        model_name_or_path: Union[str, Path] = "cross-encoder/ms-marco-MiniLM-L-6-v2",
+        model: Union[str, Path] = "cross-encoder/ms-marco-MiniLM-L-6-v2",
         device: Optional[str] = "cpu",
         token: Union[bool, str, None] = None,
         top_k: int = 10,
@@ -51,7 +51,7 @@ def __init__(
         """
         Creates an instance of TransformersSimilarityRanker.
 
-        :param model_name_or_path: The name or path of a pre-trained cross-encoder model
+        :param model: The name or path of a pre-trained cross-encoder model
             from the Hugging Face Hub.
         :param device: The torch device (for example, cuda:0, cpu, mps) to which you want to limit model inference.
         :param token: The API token used to download private models from Hugging Face.
@@ -66,18 +66,18 @@ def __init__(
             `sigmoid(logits * calibration_factor)`. This is only used if `scale_score` is set to True.
         :param score_threshold: If provided only returns documents with a score above this threshold.
         :param model_kwargs: Additional keyword arguments passed to `AutoModelForSequenceClassification.from_pretrained`
-            when loading the model specified in `model_name_or_path`. For details on what kwargs you can pass,
+            when loading the model specified in `model`. For details on what kwargs you can pass,
             see the model's documentation.
         """
         torch_and_transformers_import.check()
 
-        self.model_name_or_path = model_name_or_path
+        self.model = model
         if top_k <= 0:
             raise ValueError(f"top_k must be > 0, but got {top_k}")
         self.top_k = top_k
         self.device = device
         self.token = token
-        self.model = None
+        self._model = None
         self.tokenizer = None
         self.meta_fields_to_embed = meta_fields_to_embed or []
         self.embedding_separator = embedding_separator
@@ -94,20 +94,20 @@ def _get_telemetry_data(self) -> Dict[str, Any]:
         """
         Data that is sent to Posthog for usage analytics.
         """
-        return {"model": str(self.model_name_or_path)}
+        return {"model": str(self.model)}
 
     def warm_up(self):
         """
         Warm up the model and tokenizer used for scoring the Documents.
         """
-        if self.model is None:
+        if self._model is None:
             if self.device is None:
                 self.device = get_device()
-            self.model = AutoModelForSequenceClassification.from_pretrained(
-                self.model_name_or_path, token=self.token, **self.model_kwargs
+            self._model = AutoModelForSequenceClassification.from_pretrained(
+                self.model, token=self.token, **self.model_kwargs
             ).to(self.device)
-            self.model.eval()
-            self.tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, token=self.token)
+            self._model.eval()
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model, token=self.token)
 
     def to_dict(self) -> Dict[str, Any]:
         """
@@ -116,7 +116,7 @@ def to_dict(self) -> Dict[str, Any]:
         serialization_dict = default_to_dict(
             self,
             device=self.device,
-            model_name_or_path=self.model_name_or_path,
+            model=self.model,
             token=self.token if not isinstance(self.token, str) else None,  # don't serialize valid tokens
             top_k=self.top_k,
             meta_fields_to_embed=self.meta_fields_to_embed,
@@ -208,7 +208,7 @@ def run(
             score_threshold = self.score_threshold
 
         # If a model path is provided but the model isn't loaded
-        if self.model_name_or_path and not self.model:
+        if self.model and not self._model:
             raise ComponentError(
                 f"The component {self.__class__.__name__} wasn't warmed up. Run 'warm_up()' before calling 'run()'."
             )
@@ -227,7 +227,7 @@ def run(
             self.device
         )
         with torch.inference_mode():
-            similarity_scores = self.model(**features).logits.squeeze(dim=1)  # type: ignore
+            similarity_scores = self._model(**features).logits.squeeze(dim=1)  # type: ignore
 
         if scale_score:
             similarity_scores = torch.sigmoid(similarity_scores * calibration_factor)

releasenotes/notes/rename-model-param-rankers-be3d6163597e0a9b.yaml

@@ -0,0 +1,3 @@
+---
+upgrade:
+    - Rename `model_name_or_path` to `model` in `TransformersSimilarityRanker`.

test/components/rankers/test_transformers_similarity.py

@@ -17,7 +17,7 @@ def test_to_dict(self):
                 "device": "cpu",
                 "top_k": 10,
                 "token": None,
-                "model_name_or_path": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+                "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
                 "meta_fields_to_embed": [],
                 "embedding_separator": "\n",
                 "scale_score": True,
@@ -29,7 +29,7 @@ def test_to_dict(self):
 
     def test_to_dict_with_custom_init_parameters(self):
         component = TransformersSimilarityRanker(
-            model_name_or_path="my_model",
+            model="my_model",
             device="cuda",
             token="my_token",
             top_k=5,
@@ -43,7 +43,7 @@ def test_to_dict_with_custom_init_parameters(self):
             "type": "haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker",
             "init_parameters": {
                 "device": "cuda",
-                "model_name_or_path": "my_model",
+                "model": "my_model",
                 "token": None,  # we don't serialize valid tokens,
                 "top_k": 5,
                 "meta_fields_to_embed": [],
@@ -71,7 +71,7 @@ def test_to_dict_with_quantization_options(self):
                 "device": "cpu",
                 "top_k": 10,
                 "token": None,
-                "model_name_or_path": "cross-encoder/ms-marco-MiniLM-L-6-v2",
+                "model": "cross-encoder/ms-marco-MiniLM-L-6-v2",
                 "meta_fields_to_embed": [],
                 "embedding_separator": "\n",
                 "scale_score": True,
@@ -91,7 +91,7 @@ def test_from_dict(self):
             "type": "haystack.components.rankers.transformers_similarity.TransformersSimilarityRanker",
             "init_parameters": {
                 "device": "cuda",
-                "model_name_or_path": "my_model",
+                "model": "my_model",
                 "token": None,
                 "top_k": 5,
                 "meta_fields_to_embed": [],
@@ -105,7 +105,7 @@ def test_from_dict(self):
 
         component = TransformersSimilarityRanker.from_dict(data)
         assert component.device == "cuda"
-        assert component.model_name_or_path == "my_model"
+        assert component.model == "my_model"
         assert component.token is None
         assert component.top_k == 5
         assert component.meta_fields_to_embed == []
@@ -122,9 +122,9 @@ def test_embed_meta(self, mocked_sort, mocked_sigmoid):
         mocked_sort.return_value = (None, torch.tensor([0]))
         mocked_sigmoid.return_value = torch.tensor([0])
         embedder = TransformersSimilarityRanker(
-            model_name_or_path="model", meta_fields_to_embed=["meta_field"], embedding_separator="\n"
+            model="model", meta_fields_to_embed=["meta_field"], embedding_separator="\n"
         )
-        embedder.model = MagicMock()
+        embedder._model = MagicMock()
         embedder.tokenizer = MagicMock()
 
         documents = [Document(content=f"document number {i}", meta={"meta_field": f"meta_value {i}"}) for i in range(5)]
@@ -147,9 +147,9 @@ def test_embed_meta(self, mocked_sort, mocked_sigmoid):
     @patch("torch.sort")
     def test_scale_score_false(self, mocked_sort):
         mocked_sort.return_value = (None, torch.tensor([0, 1]))
-        embedder = TransformersSimilarityRanker(model_name_or_path="model", scale_score=False)
-        embedder.model = MagicMock()
-        embedder.model.return_value = SequenceClassifierOutput(
+        embedder = TransformersSimilarityRanker(model="model", scale_score=False)
+        embedder._model = MagicMock()
+        embedder._model.return_value = SequenceClassifierOutput(
             loss=None, logits=torch.FloatTensor([[-10.6859], [-8.9874]]), hidden_states=None, attentions=None
         )
         embedder.tokenizer = MagicMock()
@@ -162,9 +162,9 @@ def test_scale_score_false(self, mocked_sort):
     @patch("torch.sort")
     def test_score_threshold(self, mocked_sort):
         mocked_sort.return_value = (None, torch.tensor([0, 1]))
-        embedder = TransformersSimilarityRanker(model_name_or_path="model", scale_score=False, score_threshold=0.1)
-        embedder.model = MagicMock()
-        embedder.model.return_value = SequenceClassifierOutput(
+        embedder = TransformersSimilarityRanker(model="model", scale_score=False, score_threshold=0.1)
+        embedder._model = MagicMock()
+        embedder._model.return_value = SequenceClassifierOutput(
             loss=None, logits=torch.FloatTensor([[0.955], [0.001]]), hidden_states=None, attentions=None
         )
         embedder.tokenizer = MagicMock()
@@ -201,7 +201,7 @@ def test_run(self, query, docs_before_texts, expected_first_text, scores):
         """
         Test if the component ranks documents correctly.
         """
-        ranker = TransformersSimilarityRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2")
+        ranker = TransformersSimilarityRanker(model="cross-encoder/ms-marco-MiniLM-L-6-v2")
         ranker.warm_up()
         docs_before = [Document(content=text) for text in docs_before_texts]
         output = ranker.run(query=query, documents=docs_before)
@@ -244,7 +244,7 @@ def test_run_top_k(self, query, docs_before_texts, expected_first_text):
         """
         Test if the component ranks documents correctly with a custom top_k.
         """
-        ranker = TransformersSimilarityRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2", top_k=2)
+        ranker = TransformersSimilarityRanker(model="cross-encoder/ms-marco-MiniLM-L-6-v2", top_k=2)
         ranker.warm_up()
         docs_before = [Document(content=text) for text in docs_before_texts]
         output = ranker.run(query=query, documents=docs_before)
@@ -261,7 +261,7 @@ def test_run_single_document(self):
         """
         Test if the component runs with a single document.
         """
-        ranker = TransformersSimilarityRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2", device=None)
+        ranker = TransformersSimilarityRanker(model="cross-encoder/ms-marco-MiniLM-L-6-v2", device=None)
         ranker.warm_up()
         docs_before = [Document(content="Berlin")]
         output = ranker.run(query="City in Germany", documents=docs_before)