Trying to resolve the token issue

final.py

@@ -13,7 +13,8 @@ nlp = spacy.load("en_core_web_sm")
 
 # Load the trained ML model
 model_path = "./results/checkpoint-753"  # Replace with the actual path to your model
-tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
+# tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
+tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small', use_fast=False)
 model = AutoModelForSequenceClassification.from_pretrained(model_path)
 model.eval()
 

st.py

@@ -3,9 +3,15 @@ import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 # Load the model and tokenizer
+# @st.cache_resource
+# def load_model():
+#     tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
+#     model = AutoModelForSequenceClassification.from_pretrained("./results/checkpoint-753")
+#     model.eval()
+#     return tokenizer, model
 @st.cache_resource
 def load_model():
-    tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small')
+    tokenizer = AutoTokenizer.from_pretrained('microsoft/deberta-v3-small', use_fast=False)
     model = AutoModelForSequenceClassification.from_pretrained("./results/checkpoint-753")
     model.eval()
     return tokenizer, model