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+## 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>',''))
+```