Command Line Interface¶
The easiest way to test the abilities of the Snips NLU library is through the
command line interface (CLI). The CLI is installed with the python package and
is typically used by running
snips-nlu <command> [args] or alternatively
python -m snips_nlu <command> [args].
Creating a dataset¶
snips-nlu generate-dataset en my_first_intent.yaml my_second_intent.yaml my_entity.yaml
You don’t have to use separated files for each intent and entity. You could
for instance merge all intents together in a single
or even merge all intents and entities in a single
This will print a JSON string to the standard output. If you want to store the dataset directly in a JSON file, you just have to pipe the previous command like below:
snips-nlu generate-dataset en my_first_intent.yaml my_second_intent.yaml my_entity.yaml > dataset.json
Check the Training Dataset Format section for more details about the format used to describe the training data.
Once you have built a proper dataset, you can use the CLI to train an NLU engine:
snips-nlu train path/to/dataset.json path/to/persisted_engine
The first parameter corresponds to the path of the dataset file. The second
parameter is the directory where the engine should be saved after training.
The CLI takes care of creating this directory.
You can enable logs by adding a
Finally, you can use the parsing command line to test interactively the parsing abilities of a trained NLU engine:
snips-nlu parse path/to/persisted_engine
This will run a prompt allowing you to parse queries interactively. You can also pass a single query using an optional parameter:
snips-nlu parse path/to/persisted_engine -q "my query"
The CLI provides two commands that will help you evaluate the performance of your NLU engine. These commands are detailed in this dedicated section.
Two simple commands allow to print the version of the library and the version of the NLU model:
snips-nlu version snips-nlu model-version