Update Readme

README.md

@@ -1,2 +1,64 @@
+---
+language: 
+- ja
+thumbnail: 
+tags:
+- xlnet
+- lm-head
+- causal-lm
+license: 
+datasets:
+metrics:
+---
+
+# XLNet-japanese
+
+## Model description
+This model require Mecab and senetencepiece with XLNetTokenizer.
+See details https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+import MeCab
+import subprocess
+
+from transformers import (
+    pipeline,
+    XLNetLMHeadModel,
+    XLNetTokenizer
+)
+
+class XLNet():
+    def __init__(self):
+        cmd = 'echo `mecab-config --dicdir`"/mecab-ipadic-neologd"'
+        path = (subprocess.Popen(cmd, stdout=subprocess.PIPE, 
+            shell=True).communicate()[0]).decode('utf-8')
+        self.m = MeCab.Tagger(f"-Owakati -d {path}")
+
+        self.gen_model = XLNetLMHeadModel.from_pretrained("hajime9652/xlnet-japanese")
+        self.gen_tokenizer = XLNetTokenizer.from_pretrained("hajime9652/xlnet-japanese")
+         
+    def generate(self, prompt="福岡のご飯は美味しい。コンパクトで暮らしやすい街。"):
+        prompt = self.m.parse(prompt)
+        inputs = self.gen_tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+        prompt_length = len(self.gen_tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
+        outputs = self.gen_model.generate(inputs, max_length=200, do_sample=True, top_p=0.95, top_k=60)
+        generated = prompt + self.gen_tokenizer.decode(outputs[0])[prompt_length:]
+        return generated
+```
+
+#### Limitations and bias
+
+## Training data
+
+
+## Training procedure
+
+## Eval results
+
+### 
 See this documents https://qiita.com/mkt3/items/4d0ae36f3f212aee8002
 published by https://github.com/mkt3