print"\nTest with Pass %d, %s"%(event.pass_id,result.metrics)
```
`trainer.train` will train the model.
...
...
@@ -469,6 +481,42 @@ trainer.train(
feeding=feeding)
```
### Application
Aftern training is done, we need to select an optimal model based one performance index to do inference. In this task, one can simply select the model with the least number of marks on the test set. The `paddle.layer.crf_decoding` layer is used in the inference, but its inputs does not include the ground truth label.
```python
predict=paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
param_attr=paddle.attr.Param(name='crfw'))
```
Here, using one testing sample as an example.
```python
test_creator=paddle.dataset.conll05.test()
test_data=[]
foritemintest_creator():
test_data.append(item[0:8])
iflen(test_data)==1:
break
```
The inference interface `paddle.infer` returns the index of predicting labels. Then printing the tagging results based dictionary `labels_reverse`.
Semantic Role Labeling is an important intermediate step in a wide range of natural language processing tasks. In this tutorial, we use SRL as an example to illustrate using PaddlePaddle to do sequence tagging tasks. The models proposed are from our published paper\[[10](#Reference)\]. We only use test data for illustration since the training data on the CoNLL 2005 dataset is not completely public. This aims to propose an end-to-end neural network model with fewer dependencies on natural language processing tools but is comparable, or even better than traditional models in terms of performance. Please check out our paper for more information and discussions.
with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=reader, feeding=feeding)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
```
`trainer.train` will train the model.
...
...
@@ -511,6 +523,42 @@ trainer.train(
feeding=feeding)
```
### Application
Aftern training is done, we need to select an optimal model based one performance index to do inference. In this task, one can simply select the model with the least number of marks on the test set. The `paddle.layer.crf_decoding` layer is used in the inference, but its inputs does not include the ground truth label.
```python
predict = paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
param_attr=paddle.attr.Param(name='crfw'))
```
Here, using one testing sample as an example.
```python
test_creator = paddle.dataset.conll05.test()
test_data = []
for item in test_creator():
test_data.append(item[0:8])
if len(test_data) == 1:
break
```
The inference interface `paddle.infer` returns the index of predicting labels. Then printing the tagging results based dictionary `labels_reverse`.
Semantic Role Labeling is an important intermediate step in a wide range of natural language processing tasks. In this tutorial, we use SRL as an example to illustrate using PaddlePaddle to do sequence tagging tasks. The models proposed are from our published paper\[[10](#Reference)\]. We only use test data for illustration since the training data on the CoNLL 2005 dataset is not completely public. This aims to propose an end-to-end neural network model with fewer dependencies on natural language processing tools but is comparable, or even better than traditional models in terms of performance. Please check out our paper for more information and discussions.
