feat: Added a toggle to disable stemmer in bm25 (#416)

* feat: Added a toggle to disable stemmer in bm25

* refactor: Refactored how to disable stemming in bm25

* refactor: Refactored the way of disabling stemmer in bm25

* new: Added english fallback if language = None

* tests: Added test case for disable stemmer

* fix: Fix language to be only string

* tests: Updated bm25 toggle stemmer tests

* refactor: fix stopwords type

* fix: fix param propagation in parallel embed in bm25

---------

Co-authored-by: George Panchuk <[email protected]>

fastembed/sparse/bm25.py

@@ -92,6 +92,8 @@ class Bm25(SparseTextEmbeddingBase):
         b (float, optional): The b parameter in the BM25 formula. Defines the importance of the document length.
             Defaults to 0.75.
         avg_len (float, optional): The average length of the documents in the corpus. Defaults to 256.0.
+        language (str): Specifies the language for the stemmer.
+        disable_stemmer (bool): Disable the stemmer.
     Raises:
         ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
     """
@@ -105,6 +107,7 @@ def __init__(
         avg_len: float = 256.0,
         language: str = "english",
         token_max_length: int = 40,
+        disable_stemmer: bool = False,
         **kwargs,
     ):
         super().__init__(model_name, cache_dir, **kwargs)
@@ -127,9 +130,15 @@ def __init__(
 
         self.token_max_length = token_max_length
         self.punctuation = set(get_all_punctuation())
-        self.stopwords = set(self._load_stopwords(self._model_dir, self.language))
+        self.disable_stemmer = disable_stemmer
+
+        if disable_stemmer:
+            self.stopwords = set()
+            self.stemmer = None
+        else:
+            self.stopwords = set(self._load_stopwords(self._model_dir, self.language))
+            self.stemmer = SnowballStemmer(language)
 
-        self.stemmer = SnowballStemmer(language)
         self.tokenizer = SimpleTokenizer
 
     @classmethod
@@ -182,6 +191,9 @@ def _embed_documents(
                 "k": self.k,
                 "b": self.b,
                 "avg_len": self.avg_len,
+                "language": self.language,
+                "token_max_length": self.token_max_length,
+                "disable_stemmer": self.disable_stemmer,
             }
             pool = ParallelWorkerPool(
                 num_workers=parallel or 1,
@@ -225,16 +237,18 @@ def embed(
     def _stem(self, tokens: list[str]) -> list[str]:
         stemmed_tokens = []
         for token in tokens:
+            lower_token = token.lower()
+
             if token in self.punctuation:
                 continue
 
-            if token.lower() in self.stopwords:
+            if lower_token in self.stopwords:
                 continue
 
             if len(token) > self.token_max_length:
                 continue
 
-            stemmed_token = self.stemmer.stem_word(token.lower())
+            stemmed_token = self.stemmer.stem_word(lower_token) if self.stemmer else lower_token
 
             if stemmed_token:
                 stemmed_tokens.append(stemmed_token)

tests/test_sparse_embeddings.py

@@ -151,6 +151,27 @@ def test_stem_case_insensitive_stopwords(bm25_instance):
     assert result == expected, f"Expected {expected}, but got {result}"
 
 
+@pytest.mark.parametrize("disable_stemmer", [True, False])
+def test_disable_stemmer_behavior(disable_stemmer):
+    # Setup
+    model = Bm25("Qdrant/bm25", language="english", disable_stemmer=disable_stemmer)
+    model.stopwords = {"the", "is", "a"}
+    model.punctuation = {".", ",", "!"}
+
+    # Test data
+    tokens = ["The", "quick", "brown", "fox", "is", "a", "test", "sentence", ".", "!"]
+
+    # Execute
+    result = model._stem(tokens)
+
+    # Assert
+    if disable_stemmer:
+        expected = ["quick", "brown", "fox", "test", "sentence"]  # no stemming, lower case only
+    else:
+        expected = ["quick", "brown", "fox", "test", "sentenc"]
+    assert result == expected, f"Expected {expected}, but got {result}"
+
+
 @pytest.mark.parametrize(
     "model_name",
     ["prithivida/Splade_PP_en_v1"],