update

app.py

@@ -68,15 +68,18 @@ input_text = st.text_input(
 )
 
 # desirability model
-#model_path = '/nlp/nlp/models/finetuned/twitter-xlm-roberta-base-regressive-desirability-ft-4'
-model_path = 'magnolia-psychometrics/item-desirability'
-auth_token = os.environ.get("item-desirability") or True
+# remote or local?
+if os.environ.get("item-desirability"):
+    model_path = 'magnolia-psychometrics/item-desirability'
+else:
+    model_path = '/nlp/nlp/models/finetuned/twitter-xlm-roberta-base-regressive-desirability-ft-4'   
 
+auth_token = os.environ.get("item-desirability") or True
 
 if 'tokenizer' not in globals():
     tokenizer = AutoTokenizer.from_pretrained(
         pretrained_model_name_or_path=model_path,
-        # use_fast=True,
+        use_fast=True,
         use_auth_token=auth_token
     )
 
@@ -91,7 +94,7 @@ if 'model' not in globals():
 # sentiment classifier
 if 'classifier' not in globals():
     sentiment_model = 'cardiffnlp/twitter-xlm-roberta-base-sentiment'
-    classifier = pipeline("sentiment-analysis", model=sentiment_model, tokenizer=sentiment_model, top_k=3)
+    classifier = pipeline("sentiment-analysis", model=sentiment_model, tokenizer=sentiment_model, use_fast=False, top_k=3)
 
 classifier_output = classifier(input_text)
 classifier_output_dict = {x['label']: x['score'] for x in classifier_output[0]}