Update app.py

app.py

@@ -20,7 +20,7 @@ model_list = ['akdeniz27/bert-base-turkish-cased-ner',
              'akdeniz27/convbert-base-turkish-cased-ner',
              'akdeniz27/xlm-roberta-base-turkish-ner',
              'xlm-roberta-large-finetuned-conll03-english',
-             'tner/tner-xlm-roberta-base-ontonotes5']
+             'asahi417/tner-xlm-roberta-base-ontonotes5']
 
 st.sidebar.header("Select NER Model")
 model_checkpoint = st.sidebar.radio("", model_list)
@@ -30,7 +30,7 @@ st.sidebar.write("")
 
 if model_checkpoint == "akdeniz27/xlm-roberta-base-turkish-ner":
     aggregation = "simple"
-elif model_checkpoint == "xlm-roberta-large-finetuned-conll03-english" or model_checkpoint == "tner/tner-xlm-roberta-base-ontonotes5":
+elif model_checkpoint == "xlm-roberta-large-finetuned-conll03-english" or model_checkpoint == "asahi417/tner-xlm-roberta-base-ontonotes5":
     aggregation = "simple"
     st.sidebar.write("")
     st.sidebar.write("The selected NER model is included just to show the zero-shot transfer learning capability of XLM-Roberta pretrained language model.")
@@ -101,7 +101,7 @@ if Run_Button and input_text != "":
     spacy_entity_list = ["PERSON", "NORP", "FAC", "ORG", "GPE", "LOC", "PRODUCT", "EVENT", "WORK_OF_ART", "LAW", "LANGUAGE", "DATE", "TIME", "PERCENT", "MONEY", "QUANTITY", "ORDINAL", "CARDINAL", "MISC"]
     
     for ent in spacy_display["ents"]:
-        if model_checkpoint == "tner/tner-xlm-roberta-base-ontonotes5":
+        if model_checkpoint == "asahi417/tner-xlm-roberta-base-ontonotes5":
             ent["label"] = spacy_entity_list[tner_entity_list.index(ent["label"])]
         else:
             if ent["label"] == "PER": ent["label"] = "PERSON"