readme: ## Generating Chinese poetry by topic.
  
  ```python
  from transformers import *
  
  tokenizer = BertTokenizer.from_pretrained("gaochangkuan/model_dir")
  
  model = AutoModelWithLMHead.from_pretrained("gaochangkuan/model_dir")
  
  
  prompt= '''<s>田园躬耕'''
  
  length= 84    
  stop_token='</s>'        
  
  temperature = 1.2 
    
  repetition_penalty=1.3 
   
  k= 30
  p= 0.95
   
  device ='cuda'
  seed=2020          
  no_cuda=False      
   
  prompt_text = prompt if prompt else input("Model prompt >>> ")
  
  encoded_prompt = tokenizer.encode(
                                    '<s>'+prompt_text+'<sep>',
                                    add_special_tokens=False, 
                                    return_tensors="pt"
                                   )
  
  encoded_prompt = encoded_prompt.to(device)
  
  output_sequences = model.generate(
      input_ids=encoded_prompt,
      max_length=length,
      min_length=10,
      do_sample=True,
      early_stopping=True,
      num_beams=10,
      temperature=temperature,
      top_k=k,
      top_p=p,
      repetition_penalty=repetition_penalty,
      bad_words_ids=None,
      bos_token_id=tokenizer.bos_token_id,
      pad_token_id=tokenizer.pad_token_id,
      eos_token_id=tokenizer.eos_token_id,
      length_penalty=1.2,
      no_repeat_ngram_size=2,
      num_return_sequences=1,
      attention_mask=None,
      decoder_start_token_id=tokenizer.bos_token_id,)
      
      
      generated_sequence = output_sequences[0].tolist()
  text = tokenizer.decode(generated_sequence)
  
  
  text = text[: text.find(stop_token) if stop_token else None]
  
  print(''.join(text).replace(' ','').replace('<pad>','').replace('<s>',''))
  ```
  