Update app.py

app.py

@@ -15,9 +15,10 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 from transformers import AutoTokenizer, AutoModelForMaskedLM
 tokenizer = AutoTokenizer.from_pretrained("roberta-large")
 model = AutoModelForMaskedLM.from_pretrained("BigSalmon/FormalRobertaa")
+model2 = AutoModelForMaskedLM.from_pretrained("roberta-base")
 
 
-with st.expander('Expand'):
+with st.expander('BigSalmon/FormalRobertaa'):
   with st.form(key='my_form'):
     prompt = st.text_area(label='Enter Text. Put <mask> where you want the model to fill in the blank. You can use more than one at a time.')
     submit_button = st.form_submit_button(label='Submit')
@@ -31,6 +32,25 @@ with st.expander('Expand'):
       with torch.no_grad():
         output = model(token_ids)
       last_hidden_state = output[0].squeeze()
+      for mask_index in masked_pos:
+        mask_hidden_state = last_hidden_state[mask_index]
+        idx = torch.topk(mask_hidden_state, k=100, dim=0)[1]
+        words = [tokenizer.decode(i.item()).strip() for i in idx]
+        st.text_area(label = 'Infill:', value=words)
+ 
+ with st.expander('roberta-large'):
+  with st.form(key='my_form'):
+    prompt = st.text_area(label='Enter Text. Put <mask> where you want the model to fill in the blank. You can use more than one at a time.')
+    submit_button = st.form_submit_button(label='Submit')
+    if submit_button:
+      a_list = []
+      token_ids = tokenizer.encode(prompt, return_tensors='pt')
+      token_ids_tk = tokenizer.tokenize(prompt, return_tensors='pt')
+      masked_position = (token_ids.squeeze() == tokenizer.mask_token_id).nonzero()
+      masked_pos = [mask.item() for mask in masked_position ]
+      with torch.no_grad():
+        output = model2(token_ids)
+      last_hidden_state = output[0].squeeze()
       for mask_index in masked_pos:
         mask_hidden_state = last_hidden_state[mask_index]
         idx = torch.topk(mask_hidden_state, k=100, dim=0)[1]