Update app.py

app.py

@@ -12,14 +12,14 @@ selection = "N/A"
 with col1:
     selection = st.radio("Pick one of the four pre-trained models below:",
         key = "modelChoice",
-        options = ["DistilBERT", "FinBERT", "SiEBERT", "Twitter-roBERTa"],
+        options = ["DistilBERT", "Toxicity-Classifier", "SiEBERT", "Twitter-roBERTa"],
     )
 
 pipe = pipeline('sentiment-analysis')
 if selection == "DistilBERT":
     pipe = pipeline(model = "distilbert-base-uncased-finetuned-sst-2-english")
-if selection == "FinBERT":
-    pipe = pipeline(model = "yiyanghkust/finbert-tone")
+if selection == "Toxicity-Classifier":
+    pipe = pipeline(model = "JulianHame/toxicity-classifier")
 if selection == "Twitter-roBERTa":
     pipe = pipeline(model = "cardiffnlp/twitter-roberta-base-sentiment-latest")
 if selection == "SiEBERT":
@@ -27,7 +27,7 @@ if selection == "SiEBERT":
 
 with col2:
     st.caption('DistilBERT - One of the most popular and widely-used language models. Labels text as POSITIVE or NEGATIVE. Developed by Hugging Face.')
-    st.caption('FinBERT - A finance-oriented model trained with analysis reports. Labels text as POSITIVE, NEGATIVE or NEUTRAL. Developed by yiyanghkust.')
+    st.caption('Toxicity-Classifier - A model trained to classify tweets under different toxicity-related categories.')
     st.caption('SiEBERT - A model trained on diverse text sources to improve generalization. Labels text as POSITIVE or NEGATIVE. Developed by siebert.')
     st.caption('Twitter-roBERTa - A model trained on over 124M tweets. Labels text as POSITIVE, NEGATIVE or NEUTRAL. Developed by cardiffnlp.')