提交 3e82ae13 编写于 作者: Q qiuxuezhong

update README

上级 054eb11a
...@@ -33,17 +33,17 @@ We use Bleu and Rouge as evaluation metrics, the calculation of these metrics re ...@@ -33,17 +33,17 @@ We use Bleu and Rouge as evaluation metrics, the calculation of these metrics re
``` ```
cd utils && bash download_thirdparty.sh cd utils && bash download_thirdparty.sh
``` ```
#### Environment Requirements ### Environment Requirements
For now we've only tested on PaddlePaddle v1.0, to install PaddlePaddle and for more details about PaddlePaddle, see [PaddlePaddle Homepage](http://paddlepaddle.org). For now we've only tested on PaddlePaddle v1.0, to install PaddlePaddle and for more details about PaddlePaddle, see [PaddlePaddle Homepage](http://paddlepaddle.org).
#### Preparation ### Preparation
Before training the model, we have to make sure that the data is ready. For preparation, we will check the data files, make directories and extract a vocabulary for later use. You can run the following command to do this with a specified task name: Before training the model, we have to make sure that the data is ready. For preparation, we will check the data files, make directories and extract a vocabulary for later use. You can run the following command to do this with a specified task name:
``` ```
sh run.sh --prepare sh run.sh --prepare
``` ```
You can specify the files for train/dev/test by setting the `trainset`/`devset`/`testset`. You can specify the files for train/dev/test by setting the `trainset`/`devset`/`testset`.
#### Training ### Training
To train the model and you can also set the hyper-parameters such as the learning rate by using `--learning_rate NUM`. For example, to train the model for 10 passes, you can run: To train the model and you can also set the hyper-parameters such as the learning rate by using `--learning_rate NUM`. For example, to train the model for 10 passes, you can run:
``` ```
...@@ -52,14 +52,14 @@ sh run.sh --train --pass_num 10 ...@@ -52,14 +52,14 @@ sh run.sh --train --pass_num 10
The training process includes an evaluation on the dev set after each training epoch. By default, the model with the least Bleu-4 score on the dev set will be saved. The training process includes an evaluation on the dev set after each training epoch. By default, the model with the least Bleu-4 score on the dev set will be saved.
#### Evaluation ### Evaluation
To conduct a single evaluation on the dev set with the the model already trained, you can run the following command: To conduct a single evaluation on the dev set with the the model already trained, you can run the following command:
``` ```
sh run.sh --evaluate --load_dir models/1 sh run.sh --evaluate --load_dir models/1
``` ```
#### Prediction ### Prediction
You can also predict answers for the samples in some files using the following command: You can also predict answers for the samples in some files using the following command:
``` ```
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