Update classifier.py

classifier.py

@@ -1,5 +1,5 @@
 import spaces
-from transformers import pipeline as tpipeline
+from transformers import AutoTokenizer, pipeline
 # from optimum.pipelines import pipeline as opipeline
 from optimum.onnxruntime import ORTModelForSequenceClassification
 
@@ -8,9 +8,10 @@ def classify(tweet, event_model, hftoken, threshold):
     results = {"text": None,  "event": None, "score": None}
 
     model = ORTModelForSequenceClassification.from_pretrained(event_model)
+    tokenizer = AutoTokenizer.from_pretrained(event_model)
     
     # event type prediction with transformers pipeline
-    event_predictor = tpipeline(task="text-classification", model=model,  #model=event_model
+    event_predictor = pipeline(task="text-classification", model=model, tokenizer=tokenizer,  #model=event_model
                                batch_size=512, token=hftoken, device="cpu")
     tokenizer_kwargs = {'padding': True, 'truncation': True, 'max_length': 512}
     prediction = event_predictor(tweet, **tokenizer_kwargs)[0]