Update app.py

app.py

@@ -255,18 +255,22 @@ with st.columns(1)[0]:
     
         scores = {}
         for index, model_id in enumerate(selected_models):
-            tokenizer = AutoTokenizer.from_pretrained(model_id)
-            model = AutoModelWithLMHead.from_pretrained(model_id)
-            if model_id.startswith("castorini"):
-                tokenizer.model_max_length = 512
-            MLM_MASK_TOKEN = tokenizer.mask_token_id  # [(103, '[MASK]')]
-            MLM_UNK_TOKEN = tokenizer.unk_token_id
-    
-            BATCH_SIZE = 1
-            score = get_sense_score_batched(
-                sentence, tokenizer, model, MLM_MASK_TOKEN, MLM_UNK_TOKEN, BATCH_SIZE
-            )
-            scores[model_id] = score
+            try:
+                tokenizer = AutoTokenizer.from_pretrained(model_id)
+                model = AutoModelWithLMHead.from_pretrained(model_id)
+                if model_id.startswith("castorini"):
+                    tokenizer.model_max_length = 512
+                MLM_MASK_TOKEN = tokenizer.mask_token_id  # [(103, '[MASK]')]
+                MLM_UNK_TOKEN = tokenizer.unk_token_id
+        
+                BATCH_SIZE = 1
+                score = get_sense_score_batched(
+                    sentence, tokenizer, model, MLM_MASK_TOKEN, MLM_UNK_TOKEN, BATCH_SIZE
+                )
+                scores[model_id] = score
+            except:
+                scores[model_id] = 0
+                
             my_bar.progress((index + 1) / len(selected_models))
         scores = sort_dictionary(scores)
         st.write("Our recommendation is:", scores)