diff --git a/src/chonkie/chunker/semantic.py b/src/chonkie/chunker/semantic.py
index 1e1ccf9..64cb35e 100644
--- a/src/chonkie/chunker/semantic.py
+++ b/src/chonkie/chunker/semantic.py
@@ -94,7 +94,7 @@ def __init__(
                 "Cannot specify both similarity_threshold and similarity_percentile"
             )
         if similarity_threshold is None and similarity_percentile is None:
-            similarity_percentile = 0.8
+            similarity_percentile = 80
             raise Warning(
                 "No similarity threshold specified. Defaulting to 80th percentile."
             ) #TODO: Change this to be a non-blocking warning
@@ -211,6 +211,18 @@ def _prepare_sentences(self, text: str) -> List[Sentence]:
             )
         ]
 
+        # Get or compute similarity threshold
+        if self.similarity_threshold is None:
+            # Compute all pairwise similarities
+            all_similarities = [
+                self._get_semantic_similarity(
+                    sentences[i].embedding, sentences[i + 1].embedding
+                )
+                for i in range(len(sentences) - 1)
+            ]
+            self.similarity_threshold = float(
+                np.percentile(all_similarities, self.similarity_percentile)
+            )
         return sentences
 
     def _get_semantic_similarity(
@@ -240,21 +252,6 @@ def _group_sentences(self, sentences: List[Sentence]) -> List[List[Sentence]]:
         if len(sentences) <= self.initial_sentences:
             return [sentences]
 
-        # Get or compute similarity threshold
-        if self.similarity_percentile is not None:
-            # Compute all pairwise similarities
-            all_similarities = [
-                self._get_semantic_similarity(
-                    sentences[i].embedding, sentences[i + 1].embedding
-                )
-                for i in range(len(sentences) - 1)
-            ]
-            similarity_threshold = float(
-                np.percentile(all_similarities, self.similarity_percentile)
-            )
-        else:
-            similarity_threshold = self.similarity_threshold
-
         groups = []
         current_group = sentences[: self.initial_sentences]
         current_embedding = self._compute_group_embedding(current_group)
@@ -265,7 +262,7 @@ def _group_sentences(self, sentences: List[Sentence]) -> List[List[Sentence]]:
                 current_embedding, sentence.embedding
             )
 
-            if similarity >= similarity_threshold:
+            if similarity >= self.similarity_threshold:
                 # Add to current group
                 current_group.append(sentence)
                 # Update mean embedding
diff --git a/tests/chunker/test_sdpm_chunker.py b/tests/chunker/test_sdpm_chunker.py
index f796f50..4675b17 100644
--- a/tests/chunker/test_sdpm_chunker.py
+++ b/tests/chunker/test_sdpm_chunker.py
@@ -14,9 +14,10 @@ def sample_text():
 def embedding_model():
     return SentenceTransformerEmbeddings("all-MiniLM-L6-v2")
 
+
 @pytest.fixture
 def sample_complex_markdown_text():
-    text =  """# Heading 1
+    text = """# Heading 1
     This is a paragraph with some **bold text** and _italic text_. 
     ## Heading 2
     - Bullet point 1
@@ -70,6 +71,25 @@ def test_spdm_chunker_chunking(embedding_model, sample_text):
     assert all([chunk.sentences is not None for chunk in chunks])
 
 
+def test_spdm_chunker_percentile_mode(embedding_model, sample_complex_markdown_text):
+    """Test the SPDMChunker works with percentile-based similarity."""
+    chunker = SDPMChunker(
+        embedding_model=embedding_model,
+        chunk_size=512,
+        similarity_percentile=50,
+    )
+    chunks = chunker.chunk(sample_complex_markdown_text)
+
+    assert len(chunks) > 0
+    assert isinstance(chunks[0], SemanticChunk)
+    assert all([chunk.token_count <= 512 for chunk in chunks])
+    assert all([chunk.token_count > 0 for chunk in chunks])
+    assert all([chunk.text is not None for chunk in chunks])
+    assert all([chunk.start_index is not None for chunk in chunks])
+    assert all([chunk.end_index is not None for chunk in chunks])
+    assert all([chunk.sentences is not None for chunk in chunks])
+
+
 def test_spdm_chunker_empty_text(embedding_model):
     """Test that the SPDMChunker can handle empty text input."""
     chunker = SDPMChunker(