update Readme

Signed-off-by: mymusise <[email protected]>

README.md

@@ -0,0 +1,48 @@
+---
+language: zh
+widget:
+- text: "天下熙熙，"
+- text: "天气不错，"
+---
+
+<h1 align="center">
+CPM-Generate-distill
+</h1>
+
+CPM(Chinese Pre-Trained Language Models), which has 2.6B parameters, made by the research team of Beijing Zhiyuan Institute of artificial intelligence and Tsinghua University @TsinghuaAI.
+
+[repo: CPM-Generate](https://github.com/TsinghuaAI/CPM-Generate)
+The One Thing You Need to Know is this model is not uploaded by official, the conver script is [here](https://github.com/mymusise/CPM-TF2Transformer/blob/main/transfor_CMP.ipynb)
+
+And the `CPM-Generate-distill` is the distill model of `CPM`.
+
+
+# How to use
+
+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")
+
+text_generater = TextGenerationPipeline(model, tokenizer)
+
+print(text_generater("天下熙熙，", max_length=15, top_k=1, use_cache=True, prefix=''))
+```
+
+![avatar](https://github.com/mymusise/CPM-TF2Transformer/raw/main/example-cpm-distill.jpeg)