Merge pull request #66 from wangxiao1021/api

update README

README_zh.md

@@ -240,7 +240,7 @@ CUDA_VISIBLE_DEVICES=2 python run.py
 CUDA_VISIBLE_DEVICES=2,3 python run.py
 ```
 
-在多GPU模式下，PaddlePALM会自动将每批数据分配到可用的卡上。例如，如果`batch_size`设置为64，并且有4个GPU可以用于PaddlePALM，那么每个GPU中的batch_size实际上是64/4=16。因此，**当使用多个GPU时，您需要确保设置batch_size可以整除卡片的数量**。
+在多GPU模式下，PaddlePALM会自动将每个batch数据分配到可用的GPU上。例如，如果`batch_size`设置为64，并且有4个GPU可以用于PaddlePALM，那么每个GPU中的batch_size实际上是64/4=16。因此，**当使用多个GPU时，您需要确保batch_size可以被暴露给PALM的GPU数量整除**。
 
 
 ## 许可证书

examples/classification/README.md

@@ -41,9 +41,11 @@ python run.py
 If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python run.py
+CUDA_VISIBLE_DEVICES=0,1 python run.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 
 Some logs will be shown below:
 

examples/matching/README.md

@@ -49,9 +49,11 @@ python run.py
 If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python run.py
+CUDA_VISIBLE_DEVICES=0,1 python run.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 Some logs will be shown below:
 
 ```

examples/mrc/README.md

@@ -54,9 +54,11 @@ python run.py
 If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python run.py
+CUDA_VISIBLE_DEVICES=0,1 python run.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 Some logs will be shown below:
 
 ```
@@ -84,7 +86,16 @@ After the run, you can view the saved models in the `outputs/` folder and the pr
 
 ### Step 3: Evaluate
 
-Once you have the prediction, you can run the evaluation script to evaluate the model:
+#### Library Dependencies
+Before the evaluation, you need to install `nltk` and download the `punkt` tokenizer for nltk:
+
+```shell
+pip insall nltk
+python -m nltk.downloader punkt
+```
+
+#### Evaluate
+You can run the evaluation script to evaluate the model:
 
 ```shell
 python evaluate.py

examples/multi-task/README.md

@@ -57,9 +57,11 @@ python run.py
 If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python run.py
+CUDA_VISIBLE_DEVICES=0,1 python run.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 Some logs will be shown below:
 
 ```
@@ -83,10 +85,12 @@ python predict-intent.py
 If you want to specify a specific gpu or use multiple gpus for predict, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python predict-slot.py
-CUDA_VISIBLE_DEVICES=0,1,2 python predict-intent.py
+CUDA_VISIBLE_DEVICES=0,1 python predict-slot.py
+CUDA_VISIBLE_DEVICES=0,1 python predict-intent.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 After the run, you can view the predictions in the `outputs/predict-slot` folder and `outputs/predict-intent` folder. Here are some examples of predictions:
 
 `atis_slot`:

examples/predict/README.md

@@ -10,9 +10,11 @@ python run.py
 If you want to specify a specific gpu or use multiple gpus for predict, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python run.py
+CUDA_VISIBLE_DEVICES=0,1 python run.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 
 Some logs will be shown below:
 

examples/tagging/README.md

@@ -43,9 +43,11 @@ python run.py
 If you want to specify a specific gpu or use multiple gpus for training, please use **`CUDA_VISIBLE_DEVICES`**, for example:
 
 ```shell
-CUDA_VISIBLE_DEVICES=0,1,2 python run.py
+CUDA_VISIBLE_DEVICES=0,1 python run.py
 ```
 
+Note: On multi-gpu mode, PaddlePALM will automatically split each batch onto the available cards. For example, if the `batch_size` is set 64, and there are 4 cards visible for PaddlePALM, then the batch_size in each card is actually 64/4=16. If you want to change the `batch_size` or the number of gpus used in the example, **you need to ensure that the set batch_size can be divided by the number of cards.**
+
 Some logs will be shown below:
 
 ```