nit: Remove cache dir from tests

tests/test_text_multitask_embeddings.py

@@ -74,7 +74,7 @@ def test_batch_embedding():
         if model_name not in CANONICAL_VECTOR_VALUES.keys():
             continue
 
-        model = TextEmbedding(model_name=model_name, cache_dir="models")
+        model = TextEmbedding(model_name=model_name)
 
         print(f"evaluating {model_name} default task")
 
@@ -105,7 +105,7 @@ def test_single_embedding():
         if model_name not in CANONICAL_VECTOR_VALUES.keys():
             continue
 
-        model = TextEmbedding(model_name=model_name, cache_dir="models")
+        model = TextEmbedding(model_name=model_name)
 
         for task in CANONICAL_VECTOR_VALUES[model_name]:
             print(f"evaluating {model_name} task_id: {task['task_id']}")
@@ -138,7 +138,7 @@ def test_single_embedding_query():
         if model_name not in CANONICAL_VECTOR_VALUES.keys():
             continue
 
-        model = TextEmbedding(model_name=model_name, cache_dir="models")
+        model = TextEmbedding(model_name=model_name)
 
         print(f"evaluating {model_name} query_embed task_id: {task_id}")
 
@@ -170,7 +170,7 @@ def test_single_embedding_passage():
         if model_name not in CANONICAL_VECTOR_VALUES.keys():
             continue
 
-        model = TextEmbedding(model_name=model_name, cache_dir="models")
+        model = TextEmbedding(model_name=model_name)
 
         print(f"evaluating {model_name} passage_embed task_id: {task_id}")
 
@@ -196,7 +196,7 @@ def test_parallel_processing():
     model_name = "jinaai/jina-embeddings-v3"
     dim = 1024
 
-    model = TextEmbedding(model_name=model_name, cache_dir="models")
+    model = TextEmbedding(model_name=model_name)
 
     embeddings = list(model.embed(docs, batch_size=10, parallel=2))
     embeddings = np.stack(embeddings, axis=0)
@@ -221,7 +221,7 @@ def test_parallel_processing():
 )
 def test_lazy_load(model_name):
     is_ci = os.getenv("CI")
-    model = TextEmbedding(model_name=model_name, lazy_load=True, cache_dir="models")
+    model = TextEmbedding(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")
 
     list(model.embed(docs))