Update app.py

app.py

@@ -23,31 +23,24 @@ st.sidebar.markdown(
 
 # uncomment the options below to test out the app with a variety of classification models.
 models = {
-    # "textattack/distilbert-base-uncased-rotten-tomatoes": "",
-    # "textattack/bert-base-uncased-rotten-tomatoes": "",
-    # "textattack/roberta-base-rotten-tomatoes": "",
-    # "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
-    # "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
+     "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
+     "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
     "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.",
-    # "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.",
+     "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.",
     "sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.",
     "MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.",
     # # "MoritzLaurer/covid-policy-roberta-21": "(Under active development ) RoBERTA model finetuned to identify COVID policy measure classes ",
-    # "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam",
+     "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam",
 }
 model_name = st.sidebar.selectbox(
     "Choose a classification model", list(models.keys())
 )
 model, tokenizer = load_model(model_name)
 
-
-print ("Model loaded")
-if model_name.startswith("textattack/"):
-    model.config.id2label = {0: "NEGATIVE (0) ", 1: "POSITIVE (1)"}
 model.eval()
-print ("Model Evaluated")
+
 cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)
-print ("Model Explained")
+
 if cls_explainer.accepts_position_ids:
     emb_type_name = st.sidebar.selectbox(
         "Choose embedding type for attribution.", ["word", "position"]