diff --git a/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift b/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
index c6430934c44773aa6d36e1df6e2d0a1d3164ab51..34251fe80a43e9255f3c9dc66d6c8c3ec70bcd89 100644
--- a/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
+++ b/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
@@ -30,25 +30,27 @@ final class BERT_test: XCTestCase {
         ]
         //        let docs = ["cat dog", "bee fly"]
         
-        var database_embedding: [[Float]] = []
-        var query_embedding: [Float] = []
-        var embedding_dim: Int = 384
-       
-        var model = MiniLMEmbeddings(model_type: "gte-small")
-       
-        query_embedding = await model.encode(sentence: query[0])!
-       
-        var i = 1
-        //append sentence embedding to database_embedding
-        for string in docs {
-            if let vector = await model.encode(sentence: string) {
-                database_embedding.append(vector)
-                //print(i)
-                i += 1
-            } else {
-                fatalError("Error occurred!")
+        for model in ["gte-small", "all_MiniLM_L6_v2"] {
+            var database_embedding: [[Float]] = []
+            var query_embedding: [Float] = []
+            var embedding_dim: Int = 384
+           
+            var model = MiniLMEmbeddings(model_type: "gte-small")
+           
+            query_embedding = await model.encode(sentence: query[0])!
+           
+            var i = 1
+            //append sentence embedding to database_embedding
+            for string in docs {
+                if let vector = await model.encode(sentence: string) {
+                    database_embedding.append(vector)
+                    //print(i)
+                    i += 1
+                } else {
+                    fatalError("Error occurred!")
+                }
+                
             }
-            
         }
        
 // //        let index = AnnoyIndex<Float>(itemLength: embedding_dim, metric: .euclidean)