typo

app.py

@@ -10,10 +10,12 @@ model_checkpoint = "jsylee/scibert_scivocab_uncased-finetuned-ner"
 model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, 
                                                         num_labels=5,
                                                         id2label={0: 'O', 1: 'DRUG', 2: 'DRUG', 3: 'ADVERSE EFFECT', 4: 'ADVERSE EFFECT'} # for grouping BIO tags back together
-                                                        ).to("cpu")
+                                                        )                                                        
 tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
 
-model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer, device=0, grouped_entities=True)
+model.to("cpu")
+
+model_pipeline = pipeline(task="ner", model=model, tokenizer=tokenizer, device=-1, grouped_entities=True)
 
 def extract_entities(sentence):
     """ Extract drug and reaction entities, and show using displaCy's NER visualizer.