text: import torch
  
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  
  def predict(text):
      device = "cuda:0" if torch.cuda.is_available() else "cpu"
  
      tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
  
      model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
  
      input_ids = tokenizer.encode_plus(text, return_tensors="pt")
  
      sequence_classifier = model(**input_ids)
      predict_softmax = torch.softmax(sequence_classifier[0], dim=1)
  
      predicted_idx = predict_softmax.argmax(dim=-1)
  
      #return ("positive" if predicted_idx >=3 else "negative") 
      return str(predicted_idx.item() + 1)
      