Update README.md

README.md

@@ -19,10 +19,10 @@ code-autocomplete, a code completion plugin for Python.
 Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
 
 ```python
-from autocomplete.gpt2 import Infer
-m = Infer(model_name="gpt2", model_dir="shibing624/code-autocomplete-gpt2-base", use_cuda=False)
-i = m.predict('import torch.nn as')
-print(i)
+from autocomplete.gpt2_coder import GPT2Coder
+
+m = GPT2Coder("shibing624/code-autocomplete-gpt2-base")
+print(m.generate('import torch.nn as')[0])
 ```
 
 Also, use huggingface/transformers：
@@ -83,31 +83,9 @@ from torch import nn
             self.embedding_size = embedding_size
 ====================
 import numpy as np
-    import torch
-    import torch.nn as np
-from onmt import nnumpy as np
-
-
-class PredicterDNN(nn.Module):
-     @classmethod
-    @parameterized.expand([0.5, 2.5] + (10, 10))
-    @classmethod
-    @static
-    def add(self, sample_rate, max_iters=self.max_iters,     mask_fre
-====================
-import java.util.ArrayList[Tuple[Int]],
-   
-====================
-def factorial(n): number of elements per dimension,
-        assert len(n) > 1
-        n.append(self.n_iters)
-        n = n_iter(self.n_norm)
-
-     def _score(
-====================
-
-Process finished with exit code 0
-
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
 ```
 
 Model files：
@@ -130,7 +108,7 @@ cd autocomplete
 python create_dataset.py
 ```
 
-If you want train code-autocomplete GPT2 model，refer [https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2.py)
+If you want train code-autocomplete GPT2 model，refer [https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py)
 
 
 ### About GPT2