bug fix in evaulation

evaluation/evaluate_with_religous_50000.py

@@ -20,31 +20,40 @@ import numpy as np
 
 
 class CustomModel:
-    def __init__(self, model):
+    def __init__(self):
         self.session = requests.Session()
-        self.model = model
 
 
-    def get_simplexity_query2vec_results(self, sentences, embedding_url, model, template):
-        params = {}
-        params["model"] = model
-        params["template"] = template
+    def get_embedding(self, sentece, prompt_name):
+        embedding_url = "http://127.0.0.1:5010/embed"
         headers = {"accept": "application/json"}
+        headers["Content-Type"] = "application/json"
+
         data = {}
+        data["inputs"] = sentece
+        data["normalize"] = True
+        data["prompt_name"] = prompt_name
+        data["truncate"] = False
+        data["truncation_direction"] = "Right"
+
+        response = self.session.post(embedding_url, headers=headers, data=json.dumps(data), timeout=600)
+
+        return response.json()
+
+
+    def get_simplexity_query2vec_results(self, sentences, template):
         
         if len(sentences) < 2000:
             my_range = range
         else:
             my_range = tqdm.trange
         
-        batch_size = 1024
+        batch_size = 64
         vec = []
         for i in my_range(0, len(sentences), batch_size):
             start_idx = i
             stop_idx = min(i+batch_size, len(sentences))
-            data["queries"] = sentences[start_idx:stop_idx]
-            response = self.session.post(embedding_url, headers=headers, params=params, data=json.dumps(data), timeout=600)
-            new_vec = response.json()
+            new_vec = self.get_embedding(sentences[start_idx:stop_idx], template)
             vec += new_vec
         return vec
 
@@ -57,8 +66,6 @@ class CustomModel:
         **kwargs,
     ) -> np.ndarray:
         
-        embedding_url = "http://127.0.0.1:5015/embedding"
-        
         if prompt_type == None:
             template = "document"
         elif prompt_type == PromptType.query:
@@ -75,7 +82,7 @@ class CustomModel:
             # embeddings = self.get_simplexity_query2vec_results(batch["text"], embedding_url, model, template)
 
             # all_embeddings += embeddings
-        all_embeddings = self.get_simplexity_query2vec_results(sentences, embedding_url, self.model, template)
+        all_embeddings = self.get_simplexity_query2vec_results(sentences, template)
 
 
         return numpy.array(all_embeddings)
@@ -102,12 +109,11 @@ def recall_at_k(index, query_embeddings, ground_truth, all_docs, k=10):
     return hits / len(ground_truth)
 
 def evaluate():
-    model_name = "Qwen3-Embedding-0.6B"
     # model_name = "KaLM-embedding-multilingual-mini-instruct-v2.5"
     # model_name = "KaLM-Embedding-Gemma3-12B-2511"
     # model_name = "llama-embed-nemotron-8b"
     # model_name = "embeddinggemma-300m"
-    model = CustomModel(model_name)
+    model = CustomModel()
 
     file_path = os.path.dirname(__file__)
     

evaluation/evaluation.py

@@ -96,6 +96,8 @@ def is_dataset_cached(dataset_name):
 def evaluate():
     model = CustomModel()
 
+    model_name = "Qwen3-Embedding-0.6B"
+
     file_path = os.path.dirname(__file__)
     # model = mteb.get_model(model_name)
     # model = SentenceTransformer(model_name)