chore(ml): removed vit-b check and st warning (#4422)

machine-learning/app/models/clip.py

@@ -16,13 +16,6 @@ from ..config import log
 from ..schemas import ModelType
 from .base import InferenceModel
 
-_ST_TO_JINA_MODEL_NAME = {
-    "clip-ViT-B-16": "ViT-B-16::openai",
-    "clip-ViT-B-32": "ViT-B-32::openai",
-    "clip-ViT-B-32-multilingual-v1": "M-CLIP/XLM-Roberta-Large-Vit-B-32",
-    "clip-ViT-L-14": "ViT-L-14::openai",
-}
-
 
 class CLIPEncoder(InferenceModel):
     _model_type = ModelType.CLIP
@@ -36,11 +29,10 @@ class CLIPEncoder(InferenceModel):
     ) -> None:
         if mode is not None and mode not in ("text", "vision"):
             raise ValueError(f"Mode must be 'text', 'vision', or omitted; got '{mode}'")
-        if "vit-b" not in model_name.lower():
-            raise ValueError(f"Only ViT-B models are currently supported; got '{model_name}'")
+        if model_name not in _MODELS:
+            raise ValueError(f"Unknown model name {model_name}.")
         self.mode = mode
-        jina_model_name = self._get_jina_model_name(model_name)
-        super().__init__(jina_model_name, cache_dir, **model_kwargs)
+        super().__init__(model_name, cache_dir, **model_kwargs)
 
     def _download(self) -> None:
         models: tuple[tuple[str, str], tuple[str, str]] = _MODELS[self.model_name]
@@ -104,20 +96,6 @@ class CLIPEncoder(InferenceModel):
 
         return outputs[0][0].tolist()
 
-    def _get_jina_model_name(self, model_name: str) -> str:
-        if model_name in _MODELS:
-            return model_name
-        elif model_name in _ST_TO_JINA_MODEL_NAME:
-            log.warn(
-                (
-                    f"Sentence-Transformer models like '{model_name}' are not supported."
-                    f"Using '{_ST_TO_JINA_MODEL_NAME[model_name]}' instead as it is the best match for '{model_name}'."
-                ),
-            )
-            return _ST_TO_JINA_MODEL_NAME[model_name]
-        else:
-            raise ValueError(f"Unknown model name {model_name}.")
-
     def _download_model(self, model_name: str, model_md5: str) -> bool:
         # downloading logic is adapted from clip-server's CLIPOnnxModel class
         download_model(