Update README.md

README.md

@@ -26,24 +26,21 @@ widget:
 使用 pipeline 调用模型:
 
 ```python
->>> # 调用微调后的模型
->>> senc="燕子归来，问昔日雕梁何处。 -"
->>> model_id="couplet-gpt2-finetuning"
->>> from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline
-
->>> tokenizer = BertTokenizer.from_pretrained(model_id)
->>> model = GPT2LMHeadModel.from_pretrained(model_id)
->>> text_generator = TextGenerationPipeline(model, tokenizer)   
->>> text_generator.model.config.pad_token_id = text_generator.model.config.eos_token_id
->>> text_generator( senc,max_length=25, do_sample=True)
-[{'generated_text': '燕子归来，问昔日雕梁何处。 - 风 儿 吹 醒 ， 叹 今 朝 烟 雨 无'}]
+>>> task_prefix = ""
+>>> sentence = task_prefix+"国色天香，姹紫嫣红，碧水青云欣共赏"
+>>> model_output_dir='couplet-hel-mt5-finetuning/'
+>>> from transformers import pipeline
+>>> translation = pipeline("translation", model=model_output_dir)
+>>> print(translation(sentence,max_length=28))
+[{'translation_text': '月圆花好,良辰美景,良辰美景喜相逢'}]
+
 ```
 Here is how to use this model to get the features of a given text in PyTorch:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("supermy/couplet")
-model = AutoModelForCausalLM.from_pretrained("supermy/couplet")
+tokenizer = AutoTokenizer.from_pretrained("supermy/couplet-helsinki")
+model = AutoModelForCausalLM.from_pretrained("supermy/couplet-helsinki")
 ```