update mteb eval

Author: 545999961

FlagEmbedding/evaluation/mteb/runner.py

@@ -66,13 +66,8 @@ def read_results(self, output_folder, tasks):
                         break
 
                     temp_data = data['scores'][split][0]
+                    tasks_results[t_type][task_name] = round(temp_data['main_score'] * 100, 2)
 
-                    if metric == 'ap':
-                        tasks_results[t_type][task_name] = round(temp_data['cos_sim']['ap'] * 100, 2)
-                    elif metric == 'cosine_spearman':
-                        tasks_results[t_type][task_name] = round(temp_data['cos_sim']['spearman'] * 100, 2)
-                    else:
-                        tasks_results[t_type][task_name] = round(temp_data[metric] * 100, 2)
         print(f"tasks_results: {tasks_results}")
         return tasks_results
 

FlagEmbedding/evaluation/mteb/searcher.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 from typing import List, Dict, Optional
 from FlagEmbedding.abc.evaluation import EvalDenseRetriever, EvalReranker
 
@@ -22,7 +24,7 @@ def encode_queries(self, queries: List[str], **kwargs):
         emb = self.embedder.encode_queries(queries)
         if isinstance(emb, dict):
             emb = emb["dense_vecs"]
-        return emb
+        return emb.astype(np.float32)
 
     def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
         if isinstance(corpus[0], dict):
@@ -32,7 +34,7 @@ def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs):
         emb = self.embedder.encode_corpus(input_texts)
         if isinstance(emb, dict):
             emb = emb["dense_vecs"]
-        return emb
+        return emb.astype(np.float32)
 
     def encode(self, corpus: List[Dict[str, str]], **kwargs):
         if isinstance(corpus[0], dict):
@@ -42,7 +44,7 @@ def encode(self, corpus: List[Dict[str, str]], **kwargs):
         emb = self.embedder.encode_queries(input_texts)
         if isinstance(emb, dict):
             emb = emb["dense_vecs"]
-        return emb
+        return emb.astype(np.float32)
 
 class MTEBEvalReranker(EvalReranker):
     def __init__(self, reranker, **kwargs):