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Add it to your Cargo.toml:
.. code-block:: toml
[dependencies] snips-nlu-lib = { git = "https://github.com/snipsco/snips-nlu-rs", branch = "master" }
Add extern crate snips_nlu_lib to your crate root and you are good to go!
The purpose of the main crate of this repository, snips-nlu-lib, is to perform an information
extraction task called intent parsing.
Let’s take an example to illustrate the main purpose of this lib, and consider the following sentence:
.. code-block:: text
"What will be the weather in paris at 9pm?"
Properly trained, the Snips NLU engine will be able to extract structured data such as:
.. code-block:: json
{ "intent": { "intentName": "searchWeatherForecast", "confidenceScore": 0.95 }, "slots": [ { "value": "paris", "entity": "locality", "slotName": "forecast_locality" }, { "value": { "kind": "InstantTime", "value": "2018-02-08 20:00:00 +00:00" }, "entity": "snips/datetime", "slotName": "forecast_start_datetime" } ] }
In order to achieve such a result, the NLU engine needs to be fed with a trained model (json file).
This repository only contains the inference part, in order to produce trained models please check
the Snips NLU python library <https://github.com/snipsco/snips-nlu>_.
The interactive parsing CLI <examples/interactive_parsing_cli.rs>_ is a good example
of to how to use snips-nlu-rs.
Here is how you can run the CLI example:
.. code-block:: bash
$ git clone https://github.com/snipsco/snips-nlu-rs $ cd snips-nlu-rs $ cargo run --example interactive_parsing_cli data/tests/models/nlu_engine
Here we used a sample trained engine, which consists in two intents: MakeCoffee and MakeTea.
Thus, it will be able to parse queries like "Make me two cups of coffee please" or "I'd like a hot tea".
As mentioned in the previous section, you can train your own nlu engine with the
Snips NLU python library <https://github.com/snipsco/snips-nlu>_.
Licensed under either of
LICENSE-APACHE <LICENSE-APACHE>_ or http://www.apache.org/licenses/LICENSE-2.0)LICENSE-MIT <LICENSE-MIT>_) or http://opensource.org/licenses/MIT)
at your option.Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.