https://github.com/snipsco/snips-nlu-rs

Snips NLU rust implementation
https://github.com/snipsco/snips-nlu-rs

inference nlu rust

Last synced: 9 days ago
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Snips NLU rust implementation

• Host: GitHub
• URL: https://github.com/snipsco/snips-nlu-rs
• Owner: snipsco
• License: other
• Created: 2018-02-21T09:29:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T20:39:00.000Z (almost 2 years ago)
• Last Synced: 2024-08-01T17:32:50.251Z (3 months ago)
• Topics: inference, nlu, rust
• Language: Rust
• Homepage: https://snips.ai
• Size: 23.9 MB
• Stars: 340
• Watchers: 34
• Forks: 57
• Open Issues: 6
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
Snips NLU Rust

==============

.. image:: https://travis-ci.org/snipsco/snips-nlu-rs.svg?branch=master

   :target: https://travis-ci.org/snipsco/snips-nlu-rs

.. image:: https://ci.appveyor.com/api/projects/status/rsf27a9txeomic8o/branch/master?svg=true

   :target: https://ci.appveyor.com/project/snipsco/snips-nlu-rs

Installation

------------

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!

Intent Parsing with Snips NLU

-----------------------------

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 `_.

Example and API Usage

---------------------

The `interactive parsing CLI `_  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 `_.

License

-------

Licensed under either of

 * Apache License, Version 2.0 (`LICENSE-APACHE `_ or http://www.apache.org/licenses/LICENSE-2.0)

 * MIT license (`LICENSE-MIT `_) or http://opensource.org/licenses/MIT)

at your option.

Contribution

------------

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.