readme: # load model
  
  ```
  from transformers import (
      AutoTokenizer,
      AutoConfig,
      AutoModelForSeq2SeqLM
      )
  
  model_path = "T5-large-esnli-impli-figurative"
  
  tokenizer = AutoTokenizer.from_pretrained(model_path)
  config = AutoConfig.from_pretrained(model_path)
  model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  premise = "I just caught a guy picking up used chewing gum and he put it in his mouth."
  hypothesis = "it was such a pleasant sight to see a guy picking up used chewing gum; and he put it in his mouth"
  prepared_input = f"figurative hypothesis: {hypothesis} premise: {premise}"
  features = tokenizer(prepared_input, max_length=128, padding="max_length", truncation=True, return_tensors="pt")
  
  model.eval()
  model.to(device)
  with torch.no_grad():
      # https://huggingface.co/blog/how-to-generate
      generated_ids = model.generate(
          **features,
          max_length=128,
          use_cache=True,
          num_beams=4,
          length_penalty=0.6,
          early_stopping=True,
      )
  dec_preds = tokenizer.decode(outputs[0], skip_special_tokens=True)
  print("The prediction is: ", dec_preds)
  print(dec_preds[1:].replace("explanation:", "").lstrip())
  
  ```
  
  # Example input
  
  figurative  hypothesis: I was gone for only a few days and my considerate adult son just let the sink fill up with dirty dishes, making me feel really happy premise: I left my adult son home for a few days and just came back to a sink full of gross old dishes.