MODEL

app.py

@@ -50,7 +50,7 @@ if not st.session_state.filled:
             elif s == 'distilbert-base-uncased-finetuned-sst-2-english':
                 pline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
             else:
-                model = AutoModelForSequenceClassification.from_pretrained('Ptato/Modified-Bert-Toxicity-Classification')
+                model = AutoModelForSequenceClassification.from_pretrained('Ptato/Modified-Bert-Toxicity-Classification/my_model')
                 model.eval()
                 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
                 encoding = tokenizer(tweet, return_tensors="pt")
@@ -144,7 +144,7 @@ if submit and tweet:
         elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
             pline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
         else:
-            model = AutoModelForSequenceClassification.from_pretrained('Ptato/Modified-Bert-Toxicity-Classification')
+            model = AutoModelForSequenceClassification.from_pretrained('Ptato/Modified-Bert-Toxicity-Classification/my_model')
             tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
             encoding = tokenizer(tweet, return_tensors="pt")
             encoding = {k: v.to(model.device) for k,v in encoding.items()}