Merge pull request #1 from eltociear/patch-1

chore: update auto_model.py

coir/beir/generation/models/auto_model.py

@@ -51,7 +51,7 @@ def start_multi_process_pool(self, target_devices: List[str] = None):
         This method is recommended if you want to encode on multiple GPUs. It is advised
         to start only one process per GPU. This method works together with encode_multi_process
         :param target_devices: PyTorch target devices, e.g. cuda:0, cuda:1... If None, all available CUDA devices will be used
-        :return: Returns a dict with the target processes, an input queue and and output queue.
+        :return: Returns a dict with the target processes, an input queue and output queue.
         """
         if target_devices is None:
             if torch.cuda.is_available():
@@ -158,4 +158,4 @@ def generate_multi_process(self, corpus: List[Dict[str, str]], ques_per_passage:
         results_list = sorted([output_queue.get() for _ in range(last_chunk_id)], key=lambda x: x[0])        
         queries = [result[1] for result in results_list]
 
-        return [item for sublist in queries for item in sublist]
+        return [item for sublist in queries for item in sublist]