Update app.py

app.py

@@ -17,26 +17,22 @@ Pfizer, aşının güvenli ve etkili olduğunun klinik olarak da kanıtlanması
 st.set_page_config(layout="wide")
 
 st.title("Demo for Turkish NER Models")
-st.write("For details of models: 'https://huggingface.co/akdeniz27/")
-st.write("Please refer 'https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html' for entity grouping with aggregation_strategy parameter.")
 
 model_list = ['akdeniz27/bert-base-turkish-cased-ner',
              'akdeniz27/convbert-base-turkish-cased-ner',
              'akdeniz27/xlm-roberta-base-turkish-ner',
-             'akdeniz27/mDeBERTa-v3-base-turkish-ner',
              'xlm-roberta-large-finetuned-conll03-english']
 
 st.sidebar.header("Select NER Model")
 model_checkpoint = st.sidebar.radio("", model_list)
 
-st.sidebar.write("")
-st.sidebar.write("")
+st.sidebar.write("For details of models: 'https://huggingface.co/akdeniz27/")
 st.sidebar.write("")
 
 xlm_agg_strategy_info = "'aggregation_strategy' can be selected as 'simple' or 'none' for 'xlm-roberta' because of the RoBERTa model's tokenization approach."
 
 st.sidebar.header("Select Aggregation Strategy Type")
-if model_checkpoint == "akdeniz27/xlm-roberta-base-turkish-ner" or model_checkpoint == "akdeniz27/mDeBERTa-v3-base-turkish-ner":
+if model_checkpoint == "akdeniz27/xlm-roberta-base-turkish-ner":
     aggregation = st.sidebar.radio("", ('simple', 'none'))
     st.sidebar.write(xlm_agg_strategy_info)
 elif model_checkpoint == "xlm-roberta-large-finetuned-conll03-english":
@@ -46,6 +42,8 @@ elif model_checkpoint == "xlm-roberta-large-finetuned-conll03-english":
     st.sidebar.write("This English NER model is included just to show the zero-shot transfer learning capability of XLM-Roberta.")
 else:
     aggregation = st.sidebar.radio("", ('first', 'simple', 'average', 'max', 'none'))
+    
+st.write("Please refer 'https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html' for entity grouping with aggregation_strategy parameter.")
 
 st.subheader("Select Text Input Method")
 input_method = st.radio("", ('Select from Examples', 'Write or Paste New Text'))
@@ -59,12 +57,8 @@ elif input_method == "Write or Paste New Text":
 
 @st.cache(allow_output_mutation=True)
 def setModel(model_checkpoint, aggregation):
-    if model_checkpoint == "akdeniz27/mDeBERTa-v3-base-turkish-ner":
-        model = DebertaV2Model.from_pretrained(model_checkpoint)
-        tokenizer = DebertaV2Tokenizer.from_pretrained(model_checkpoint)
-    else:
-        model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)
-        tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+    model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)
+    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
     return pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy=aggregation)
 
 @st.cache(allow_output_mutation=True)