Truncate in EmbeddingSimilarityEvaluator

sentence_transformers/SentenceTransformer.py

@@ -70,7 +70,8 @@ class SentenceTransformer(nn.Sequential):
         This option should only be set to True for repositories you trust and in which you have read the code, as it
         will execute code present on the Hub on your local machine.
     :param token: Hugging Face authentication token to download private models.
-    :param truncate_dim: The dimension to truncate sentence embeddings to. `None` does no truncation.
+    :param truncate_dim: The dimension to truncate sentence embeddings to. `None` does no truncation. Truncation is
+        only applicable during inference when `.encode` is called.
     """
 
     def __init__(

sentence_transformers/evaluation/EmbeddingSimilarityEvaluator.py

@@ -1,3 +1,4 @@
+from contextlib import nullcontext
 from . import SentenceEvaluator, SimilarityFunction
 import logging
 import os
@@ -33,6 +34,7 @@ def __init__(
         show_progress_bar: bool = False,
         write_csv: bool = True,
         precision: Optional[Literal["float32", "int8", "uint8", "binary", "ubinary"]] = None,
+        truncate_dim: Optional[int] = None,
     ):
         """
         Constructs an evaluator based for the dataset
@@ -45,12 +47,15 @@ def __init__(
         :param write_csv: Write results to a CSV file
         :param precision: The precision to use for the embeddings. Can be "float32", "int8", "uint8", "binary", or
             "ubinary". Defaults to None.
+        :param truncate_dim: The dimension to truncate sentence embeddings to. `None` uses the model's current
+            truncation dimension. Defaults to None.
         """
         self.sentences1 = sentences1
         self.sentences2 = sentences2
         self.scores = scores
         self.write_csv = write_csv
         self.precision = precision
+        self.truncate_dim = truncate_dim
 
         assert len(self.sentences1) == len(self.sentences2)
         assert len(self.sentences1) == len(self.scores)
@@ -107,22 +112,23 @@ def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int =
 
         logger.info("EmbeddingSimilarityEvaluator: Evaluating the model on " + self.name + " dataset" + out_txt)
 
-        embeddings1 = model.encode(
-            self.sentences1,
-            batch_size=self.batch_size,
-            show_progress_bar=self.show_progress_bar,
-            convert_to_numpy=True,
-            precision=self.precision,
-            normalize_embeddings=bool(self.precision),
-        )
-        embeddings2 = model.encode(
-            self.sentences2,
-            batch_size=self.batch_size,
-            show_progress_bar=self.show_progress_bar,
-            convert_to_numpy=True,
-            precision=self.precision,
-            normalize_embeddings=bool(self.precision),
-        )
+        with nullcontext() if self.truncate_dim is None else model.truncate_sentence_embeddings(self.truncate_dim):
+            embeddings1 = model.encode(
+                self.sentences1,
+                batch_size=self.batch_size,
+                show_progress_bar=self.show_progress_bar,
+                convert_to_numpy=True,
+                precision=self.precision,
+                normalize_embeddings=bool(self.precision),
+            )
+            embeddings2 = model.encode(
+                self.sentences2,
+                batch_size=self.batch_size,
+                show_progress_bar=self.show_progress_bar,
+                convert_to_numpy=True,
+                precision=self.precision,
+                normalize_embeddings=bool(self.precision),
+            )
         # Binary and ubinary embeddings are packed, so we need to unpack them for the distance metrics
         if self.precision == "binary":
             embeddings1 = (embeddings1 + 128).astype(np.uint8)