diff --git a/docs/en_US/Compressor/Overview.md b/docs/en_US/Compressor/Overview.md
index 7ee603e3e3..5fc8e45c5d 100644
--- a/docs/en_US/Compressor/Overview.md
+++ b/docs/en_US/Compressor/Overview.md
@@ -95,7 +95,17 @@ pruner.update_epoch(epoch)
 
 The other is `step`, it can be called with `pruner.step()` after each minibatch. Note that not all algorithms need these two APIs, for those that do not need them, calling them is allowed but has no effect.
 
-__[TODO]__ The last API is for users to export the compressed model. You will get a compressed model when you finish the training using this API. It also exports another file storing the values of masks.
+You can easily export the compressed model using the following API if you are pruning your model, ```state_dict``` of the sparse model weights will be stored in ```model.pth```, which can be loaded by ```torch.load('model.pth')```
+
+```
+pruner.export_model(model_path='model.pth')
+```
+
+```mask_dict ``` and pruned model in ```onnx``` format(```input_shape``` need to be specified) can also be exported like this:
+
+```python
+pruner.export_model(model_path='model.pth', mask_path='mask.pth', onnx_path='model.onnx', input_shape=[1, 1, 28, 28])
+```
 
 ## Customize new compression algorithms