Final fix for word2vec + svm input formatting

utils/util_modeler.py

@@ -114,12 +114,15 @@ def fit_transform(
             np.ndarray: Word2Vec embeddings for the input text.
         """
 
+        embedding = []
+
         # Initialize an array to store Word2Vec embeddings for the input text
         words = self.tokenizer.tokenize(text)  # Tokenize the document
         word_vectors = [self.model[word] if word in self.model else np.zeros(self.model.vector_size) for word in words]
         document_embedding = np.mean(word_vectors, axis=0)  # Calculate the mean of word embeddings for the document
+        embedding.append(document_embedding)
 
-        return document_embedding
+        return np.array(embedding)
 
 
 class TPSampler: