more outputs

app.py

@@ -50,7 +50,7 @@ class ArxivSearch:
                     self.search_button = gr.Button("Search")
 
             self.output_md = gr.Markdown()
-            
+
             self.query_box.submit(
                 self.search_function,
                 inputs=[self.query_box, self.embedding_dropdown],
@@ -201,7 +201,7 @@ class ArxivSearch:
             fig = go.Figure(data=[trace], layout=layout)
         return fig
     
-    def keyword_match_ranking(self, query, top_n=5):
+    def keyword_match_ranking(self, query, top_n=10):
         query_terms = query.lower().split()
         query_indices = [i for i, term in enumerate(self.feature_names) if term in query_terms]
         if not query_indices:
@@ -215,7 +215,7 @@ class ArxivSearch:
         scores.sort(key=lambda x: x[1], reverse=True)
         return scores[:top_n]
     
-    def word2vec_search(self, query, top_n=5):
+    def word2vec_search(self, query, top_n=10):
         tokens = [word for word in query.split() if word in self.wv_model.key_to_index]
         if not tokens:
             return []
@@ -226,7 +226,7 @@ class ArxivSearch:
         top_indices = sims.argsort()[::-1][:top_n]
         return [(i, sims[i]) for i in top_indices]
 
-    def bert_search(self, query, top_n=5):
+    def bert_search(self, query, top_n=10):
         with torch.no_grad():
             inputs = self.tokenizer(query, return_tensors="pt", truncation=True, padding=True)
             outputs = self.model(**inputs)
@@ -236,7 +236,7 @@ class ArxivSearch:
         top_indices = sims.argsort()[::-1][:top_n]
         return [(i, sims[i]) for i in top_indices]
 
-    def bert_search_2(self, query, top_n=5):
+    def bert_search_2(self, query, top_n=10):
         with torch.no_grad():
             inputs = self.tokenizer(query, return_tensors="pt", truncation=True, padding=True)
             outputs = self.model(**inputs)