diff --git a/haystack/nodes/retriever/_embedding_encoder.py b/haystack/nodes/retriever/_embedding_encoder.py
index 4ed663c4a..45255136b 100644
--- a/haystack/nodes/retriever/_embedding_encoder.py
+++ b/haystack/nodes/retriever/_embedding_encoder.py
@@ -477,7 +477,14 @@ class _CohereEmbeddingEncoder(_BaseEmbeddingEncoder):
         self.api_key = retriever.api_key
         self.batch_size = min(16, retriever.batch_size)
         self.progress_bar = retriever.progress_bar
-        self.model: str = next((m for m in ["small", "medium", "large"] if m in retriever.embedding_model), "large")
+        self.model: str = next(
+            (
+                m
+                for m in ["small", "medium", "large", "multilingual-22-12", "finance-sentiment"]
+                if m in retriever.embedding_model
+            ),
+            "multilingual-22-12",
+        )
         self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
 
     def _ensure_text_limit(self, text: str) -> str:
diff --git a/haystack/nodes/retriever/dense.py b/haystack/nodes/retriever/dense.py
index bd99a7ccd..125fda861 100644
--- a/haystack/nodes/retriever/dense.py
+++ b/haystack/nodes/retriever/dense.py
@@ -1819,7 +1819,7 @@ class EmbeddingRetriever(DenseRetriever):
     def _infer_model_format(model_name_or_path: str, use_auth_token: Optional[Union[str, bool]]) -> str:
         if any(m in model_name_or_path for m in ["ada", "babbage", "davinci", "curie"]):
             return "openai"
-        if model_name_or_path in ["small", "medium", "large"]:
+        if model_name_or_path in ["small", "medium", "large", "multilingual-22-12", "finance-sentiment"]:
             return "cohere"
         # Check if model name is a local directory with sentence transformers config file in it
         if Path(model_name_or_path).exists():