Update documents

README.md

@@ -23,4 +23,50 @@ pip install keras-xlnet
 
 ## Usage
 
-TODO
+### Load Pretrained Checkpoints
+
+```python
+import os
+from keras_xlnet import load_trained_model_from_checkpoint
+
+checkpoint_path = '.../xlnet_cased_L-24_H-1024_A-16'
+
+model = load_trained_model_from_checkpoint(
+    config_path=os.path.join(checkpoint_path, 'xlnet_config.json'),
+    checkpoint_path=os.path.join(checkpoint_path, 'xlnet_model.ckpt'),
+    batch_size=16,
+    memory_len=512,
+    target_len=128,
+    in_train_phase=False,
+)
+model.summary()
+```
+
+Arguments `batch_size`, `memory_len` and `target_len` are maximum sizes used for initialization of memories. The model used for training a language model is returned if `in_train_phase` is `True`, otherwise a model used for fine-tuning will be returned.
+
+### About I/O
+
+#### `in_train_phase` is `False`
+
+3 inputs:
+
+* IDs of tokens, with shape `(batch_size, target_len)`.
+* IDs of segments, with shape `(batch_size, target_len)`.
+* Length of memories, with shape `(batch_size, 1)`.
+
+1 output:
+
+* The feature for each token, with shape `(batch_size, target_len, units)`.
+
+#### `in_train_phase` is `True`
+
+4 inputs:
+
+* IDs of tokens, with shape `(batch_size, target_len)`.
+* IDs of segments, with shape `(batch_size, target_len)`.
+* Length of memories, with shape `(batch_size, 1)`.
+* Masks of tokens, with shape `(batch_size, target_len)`.
+
+1 output:
+
+* The probability of each token in each position, with shape `(batch_size, target_len, num_token)`.

README.zh-CN.md

@@ -22,3 +22,51 @@ pip install keras-xlnet
 ```
 
 ## 使用
+
+### 加载预训练检查点
+
+```python
+import os
+from keras_xlnet import load_trained_model_from_checkpoint
+
+checkpoint_path = '.../xlnet_cased_L-24_H-1024_A-16'
+
+model = load_trained_model_from_checkpoint(
+    config_path=os.path.join(checkpoint_path, 'xlnet_config.json'),
+    checkpoint_path=os.path.join(checkpoint_path, 'xlnet_model.ckpt'),
+    batch_size=16,
+    memory_len=512,
+    target_len=128,
+    in_train_phase=False,
+)
+model.summary()
+```
+
+参数`batch_size`、`memory_len`、`target_len`用于初始化记忆单元，代表最大尺寸，实际属于可以小于对应数值。如果`in_train_phase`是`True`会返回一个用于训练语言模型的模型，否则返回一个用于fine-tuning的模型。
+
+### 关于输入输出
+
+#### `in_train_phase`是`False`
+
+3个输入：
+
+* 词的ID，形状为`(batch_size, target_len)`。
+* 段落的ID，形状为`(batch_size, target_len)`。
+* 历史记忆的长度，形状为`(batch_size, 1)`。
+
+1个输出：
+
+* 每个词的特征，形状为`(batch_size, target_len, units)`。
+
+#### `in_train_phase`是`True`
+
+4个输入，前三个和`in_train_phase`为`False`时相同：
+
+* 词的ID，形状为`(batch_size, target_len)`。
+* 段落的ID，形状为`(batch_size, target_len)`。
+* 历史记忆的长度，形状为`(batch_size, 1)`。
+* 被遮罩的词的蒙版，形状为`(batch_size, target_len)`。
+
+1个输出：
+
+* 每个位置每个词的概率，形状为`(batch_size, target_len, num_token)`。

setup.py

@@ -11,7 +11,7 @@
 
 setup(
     name='keras-xlnet',
-    version='0.0.0',
+    version='0.0.1',
     packages=find_packages(),
     url='https://github.com/CyberZHG/keras-xlnet',
     license='MIT',