chunk_content: r-earlier figure , the Lithuanian Brewers ' Association reporting citing the results from its members .",
      return_tensors="pt",
  )
  Put the model on a GPU and generate the predicted text sentiment:
  
  
  	Copied
  model.to(device)
  
  with torch.no_grad():
      inputs = {k: v.to(device) for k, v in inputs.items()}
      outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
      print(tokenizer.batch_decode(outputs.detach().cpu().numpy(