feat: Add optional embedding normalization to StaticModel loading (#164)

Author: davidberenstein1957

model2vec/model.py

@@ -35,7 +35,7 @@ def __init__(
         :param vectors: The vectors to use.
         :param tokenizer: The Transformers tokenizer to use.
         :param config: Any metadata config.
-        :param normalize: Whether to normalize.
+        :param normalize: Whether to normalize the embeddings.
         :param base_model_name: The used base model name. Used for creating a model card.
         :param language: The language of the model. Used for creating a model card.
         :raises: ValueError if the number of tokens does not match the number of vectors.
@@ -149,6 +149,7 @@ def from_pretrained(
         cls: type[StaticModel],
         path: PathLike,
         token: str | None = None,
+        normalize: bool | None = None,
     ) -> StaticModel:
         """
         Load a StaticModel from a local path or huggingface hub path.
@@ -157,21 +158,28 @@ def from_pretrained(
 
         :param path: The path to load your static model from.
         :param token: The huggingface token to use.
+        :param normalize: Whether to normalize the embeddings.
         :return: A StaticModel
         """
         from model2vec.hf_utils import load_pretrained
 
         embeddings, tokenizer, config, metadata = load_pretrained(path, token=token, from_sentence_transformers=False)
 
         return cls(
-            embeddings, tokenizer, config, base_model_name=metadata.get("base_model"), language=metadata.get("language")
+            embeddings,
+            tokenizer,
+            config,
+            normalize=normalize,
+            base_model_name=metadata.get("base_model"),
+            language=metadata.get("language"),
         )
 
     @classmethod
     def from_sentence_transformers(
         cls: type[StaticModel],
         path: PathLike,
         token: str | None = None,
+        normalize: bool | None = None,
     ) -> StaticModel:
         """
         Load a StaticModel trained with sentence transformers from a local path or huggingface hub path.
@@ -180,13 +188,14 @@ def from_sentence_transformers(
 
         :param path: The path to load your static model from.
         :param token: The huggingface token to use.
+        :param normalize: Whether to normalize the embeddings.
         :return: A StaticModel
         """
         from model2vec.hf_utils import load_pretrained
 
         embeddings, tokenizer, config, _ = load_pretrained(path, token=token, from_sentence_transformers=True)
 
-        return cls(embeddings, tokenizer, config, base_model_name=None, language=None)
+        return cls(embeddings, tokenizer, config, normalize=normalize, base_model_name=None, language=None)
 
     def encode_as_sequence(
         self,