chore: Rename PoolingType to PoolingMode

model2vec/distill/distillation.py

@@ -11,7 +11,7 @@
 from transformers.modeling_utils import PreTrainedModel
 from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
 
-from model2vec.distill.inference import PCADimType, PoolingType, create_embeddings, post_process_embeddings
+from model2vec.distill.inference import PCADimType, PoolingMode, create_embeddings, post_process_embeddings
 from model2vec.distill.utils import select_optimal_device
 from model2vec.model import StaticModel
 from model2vec.quantization import DType, quantize_embeddings
@@ -31,7 +31,7 @@ def distill_from_model(
     token_remove_pattern: str | None = r"\[unused\d+\]",
     quantize_to: DType | str = DType.Float16,
     vocabulary_quantization: int | None = None,
-    pooling: PoolingType = PoolingType.MEAN,
+    pooling: PoolingMode = PoolingMode.MEAN,
 ) -> StaticModel:
     """
     Distill a staticmodel from a sentence transformer.
@@ -55,7 +55,7 @@ def distill_from_model(
         If the pattern is so general that it removes all tokens, we throw an error. If the pattern can't be compiled into a valid regex, we also throw an error.
     :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents.
     :param vocabulary_quantization: The number of clusters to use for vocabulary quantization. If this is None, no quantization is performed.
-    :param pooling: The pooling strategy to use for creating embeddings. Can be one of:
+    :param pooling: The pooling mode to use for creating embeddings. Can be one of:
         'mean' (default): mean over all tokens. Robust and works well in most cases.
         'last': use the last token's hidden state (often the [EOS] token). Common for decoder-style models.
         'first': use the first token's hidden state ([CLS] token in BERT-style models).
@@ -209,7 +209,7 @@ def distill(
     trust_remote_code: bool = False,
     quantize_to: DType | str = DType.Float16,
     vocabulary_quantization: int | None = None,
-    pooling: PoolingType = PoolingType.MEAN,
+    pooling: PoolingMode = PoolingMode.MEAN,
 ) -> StaticModel:
     """
     Distill a staticmodel from a sentence transformer.
@@ -232,7 +232,7 @@ def distill(
     :param trust_remote_code: Whether to trust the remote code. If this is False, we will only load components coming from `transformers`. If this is True, we will load all components.
     :param quantize_to: The data type to quantize to. Can be any of the DType enum members or their string equivalents.
     :param vocabulary_quantization: The number of clusters to use for vocabulary quantization. If this is None, no quantization is performed.
-    :param pooling: The pooling strategy to use for creating embeddings. Can be one of:
+    :param pooling: The pooling mode to use for creating embeddings. Can be one of:
         'mean' (default): mean over all tokens. Robust and works well in most cases.
         'last': use the last token's hidden state (often the [EOS] token). Common for decoder-style models.
         'first': use the first token's hidden state ([CLS] token in BERT-style models).

model2vec/distill/inference.py

@@ -23,9 +23,9 @@
 _DEFAULT_BATCH_SIZE = 256
 
 
-class PoolingType(str, Enum):
+class PoolingMode(str, Enum):
     """
-    Pooling strategies for embedding creation.
+    Pooling modes for embedding creation.
 
     - MEAN: masked mean over all tokens.
     - LAST: last non-padding token (often EOS, common in decoder-style models).
@@ -47,7 +47,7 @@ def create_embeddings(
     tokenized: list[list[int]],
     device: str,
     pad_token_id: int,
-    pooling: PoolingType = PoolingType.MEAN,
+    pooling: PoolingMode = PoolingMode.MEAN,
 ) -> np.ndarray:
     """
     Create output embeddings for a bunch of tokens using a pretrained model.
@@ -59,9 +59,9 @@ def create_embeddings(
     :param tokenized: All tokenized tokens.
     :param device: The torch device to use.
     :param pad_token_id: The pad token id. Used to pad sequences.
-    :param pooling: The pooling strategy to use.
+    :param pooling: The pooling mode to use.
     :return: The output embeddings.
-    :raises ValueError: If the pooling strategy is unknown.
+    :raises ValueError: If the pooling mode is unknown.
     """
     model = model.to(device).eval()  # type: ignore  # Transformers error
 
@@ -97,13 +97,13 @@ def create_embeddings(
             # Add token_type_ids for models that support it
             encoded["token_type_ids"] = torch.zeros_like(encoded["input_ids"])
 
-        if pooling == PoolingType.MEAN:
+        if pooling == PoolingMode.MEAN:
             out = _encode_mean_with_model(model, encoded)
-        elif pooling == PoolingType.LAST:
+        elif pooling == PoolingMode.LAST:
             out = _encode_last_with_model(model, encoded)
-        elif pooling == PoolingType.FIRST:
+        elif pooling == PoolingMode.FIRST:
             out = _encode_first_with_model(model, encoded)
-        elif pooling == PoolingType.POOLER:
+        elif pooling == PoolingMode.POOLER:
             out = _encode_pooler_with_model(model, encoded)
         else:
             raise ValueError(f"Unknown pooling: {pooling}")

tests/test_distillation.py

@@ -12,7 +12,7 @@
 from transformers.modeling_utils import PreTrainedModel
 
 from model2vec.distill.distillation import distill, distill_from_model
-from model2vec.distill.inference import PoolingType, create_embeddings, post_process_embeddings
+from model2vec.distill.inference import PoolingMode, create_embeddings, post_process_embeddings
 from model2vec.model import StaticModel
 from model2vec.tokenizer import clean_and_create_vocabulary
 
@@ -260,10 +260,10 @@ def test_clean_and_create_vocabulary(
 @pytest.mark.parametrize(
     "pooling,with_pooler,expected_rows",
     [
-        (PoolingType.MEAN, False, [1.0, 0.0]),  # len=3: mean(0,1,2)=1; len=1: mean(0)=0
-        (PoolingType.LAST, False, [2.0, 0.0]),  # last of 3: 2; last of 1: 0
-        (PoolingType.FIRST, False, [0.0, 0.0]),  # first position: 0
-        (PoolingType.POOLER, True, [7.0, 7.0]),  # pooler_output used
+        (PoolingMode.MEAN, False, [1.0, 0.0]),  # len=3: mean(0,1,2)=1; len=1: mean(0)=0
+        (PoolingMode.LAST, False, [2.0, 0.0]),  # last of 3: 2; last of 1: 0
+        (PoolingMode.FIRST, False, [0.0, 0.0]),  # first position: 0
+        (PoolingMode.POOLER, True, [7.0, 7.0]),  # pooler_output used
     ],
 )
 def test_pooling_strategies(mock_transformer, pooling, with_pooler, expected_rows) -> None:
@@ -292,5 +292,5 @@ def test_pooler_raises_without_pooler_output(mock_transformer) -> None:
             tokenized=tokenized,
             device="cpu",
             pad_token_id=0,
-            pooling=PoolingType.POOLER,
+            pooling=PoolingMode.POOLER,
         )