readme: # This model predicts the sentiment('Negative'/'Positive') for the input sentence. It is fine-tuned mt5-small
  
  The present model supports 6 languages -
  1) English
  2) Hindi
  3) German
  4) Korean
  5) Japanese
  6) Portuguese
  
  Here is how to use this model
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  import torch
  model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/mt5-multilingual-sentiment")
  tokenizer = AutoTokenizer.from_pretrained("Chirayu/mt5-multilingual-sentiment")
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model = model.to(device)
  
  def get_sentiment(text, num_beams=2,max_length=512, repetition_penalty=2.5, length_penalty=1, early_stopping=True,top_p=.95, top_k=50, num_return_sequences=1):
    
    input_ids = tokenizer.encode(
      text, return_tensors="pt", add_special_tokens=True
    )
    
    input_ids = input_ids.to(device)
    generated_ids = model.generate(
        input_ids=input_ids,
       
        num_beams=num_beams,
        max_length=max_length,
        repetition_penalty=repetition_penalty,
        length_penalty=length_penalty,
        early_stopping=early_stopping,
        top_p=top_p,
        top_k=top_k,
        num_return_sequences=num_return_sequences,
    )
    sentiment = [tokenizer.decode(generated_id,skip_special_tokens=True,clean_up_tokenization_spaces=True,) for generated_id in generated_ids]
    return sentiment
  ```