Update README.md

README.md

@@ -2,7 +2,7 @@
 language: zh
 widget:
 - text: "天下熙熙，"
-- text: "天气不错，"
+- text: "清华大学是"
 ---
 
 <h1 align="center">
@@ -22,27 +22,15 @@ And the `CPM-Generate-distill` is the distill model of `CPM`.
 How to use this model directly from the 🤗/transformers library:
 
 ```python
-from transformers import XLNetTokenizer, TFGPT2LMHeadModel
-from transformers import TextGenerationPipeline
-import jieba
-# add spicel process 
-class XLNetTokenizer(XLNetTokenizer):
-    translator = str.maketrans(" \n", "\u2582\u2583")
-    def _tokenize(self, text, *args, **kwargs):
-        text = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)]
-        text = " ".join(text)
-        return super()._tokenize(text, *args, **kwargs)
-    def _decode(self, *args, **kwargs):
-        text = super()._decode(*args, **kwargs)
-        text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n')
-        return text
-
-tokenizer = XLNetTokenizer.from_pretrained('mymusise/CPM-Generate-distill')
-model = TFGPT2LMHeadModel.from_pretrained("mymusise/CPM-Generate-distill")
+from transformers import TextGenerationPipeline, AutoTokenizer, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained("mymusise/CPM-Generate-distill")
+model = AutoModelWithLMHead.from_pretrained("mymusise/CPM-Generate-distill")
 
 text_generater = TextGenerationPipeline(model, tokenizer)
 
-print(text_generater("天下熙熙，", max_length=15, top_k=1, use_cache=True, prefix=''))
+print(text_generator('清华大学是, max_length=50, do_sample=True, top_p=0.9))
 ```
 
+
 ![avatar](https://github.com/mymusise/CPM-TF2Transformer/raw/main/example-cpm-distill.jpeg)