update

app.py

@@ -881,15 +881,6 @@ def triple_to_string(x):
 
 @spaces.GPU
 def rerank_questions(claim, bm25_qas, topk=3):
-    #
-    # tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
-    # bert_model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2,
-    #                                                            problem_type="single_label_classification")  # Must specify single_label for some reason
-    # best_checkpoint = "averitec/pretrained_models/bert_dual_encoder.ckpt"
-    # device = "cuda:0" if torch.cuda.is_available() else "cpu"
-    # trained_model = DualEncoderModule.load_from_checkpoint(best_checkpoint, tokenizer=tokenizer, model=bert_model).to(
-    #     device)
-
     #
     strs_to_score = []
     values = []
@@ -919,7 +910,7 @@ def rerank_questions(claim, bm25_qas, topk=3):
     return top3_qa_pairs
 
 
-# @spaces.GPU
+@spaces.GPU
 def Googleretriever(query, sources):
     # ----- Generate QA pairs using AVeriTeC
     # step 1: generate questions for the query/claim using Bloom
@@ -1246,6 +1237,7 @@ def log_on_azure(file, logs, azure_share_client):
     file_client.upload_file(logs)
 
 
+@spaces.GPU
 def chat(claim, history, sources):
     evidence = []
     if 'Google' in sources: