Snips Natural Language Understanding library
Project description
Snips NLU
=========
.. image:: https://travis-ci.org/snipsco/snips-nlu.svg?branch=develop
:target: https://travis-ci.org/snipsco/snips-nlu
.. image:: https://img.shields.io/pypi/v/snips-nlu.svg?branch=develop
:target: https://pypi.python.org/pypi/snips-nlu
.. image:: https://img.shields.io/pypi/pyversions/snips-nlu.svg?branch=develop
:target: https://pypi.python.org/pypi/snips-nlu
.. image:: https://codecov.io/gh/snipsco/snips-nlu/branch/develop/graph/badge.svg
:target: https://codecov.io/gh/snipsco/snips-nlu
`Snips NLU <https://snips-nlu.readthedocs.io>`_ (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information.
Installing
----------
.. code-block:: python
pip install snips-nlu
We currently have pre-built binaries (wheels) for ``snips-nlu`` and its
dependencies for MacOS and Linux x86_64. If you use a different
architecture/os you will need to build these dependencies from sources
which means you will need to install
`setuptools_rust <https://github.com/PyO3/setuptools-rust>`_ and
`Rust <https://www.rust-lang.org/en-US/install.html>`_ before running the
``pip install snips-nlu`` command.
A simple example
----------------
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",
"probability": 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"
}
]
}
Documentation
-------------
To find out how to use Snips NLU please refer to our `documentation <https://snips-nlu.readthedocs.io>`_, it will provide you with a step-by-step guide on how to use and setup our library.
Links
-----
* `Snips NLU <https://github.com/snipsco/snips-nlu>`_
* `Snips NLU Rust <https://github.com/snipsco/snips-nlu-rs>`_: Rust inference pipeline implementation and bindings (C, Swift, Kotlin, Python)
* `Rustling <https://github.com/snipsco/rustling-ontology>`_: Snips NLU builtin entities parser
* `Snips <https://snips.ai/>`_
* `Bug tracker <https://github.com/snipsco/snips-nlu/issues>`_
Contributing
------------
Please see the `Contribution Guidelines <CONTRIBUTING.rst>`_.
Copyright
---------
This library is provided by `Snips <https://www.snips.ai>`_ as Open Source software. See `LICENSE <LICENSE>`_ for more information.
=========
.. image:: https://travis-ci.org/snipsco/snips-nlu.svg?branch=develop
:target: https://travis-ci.org/snipsco/snips-nlu
.. image:: https://img.shields.io/pypi/v/snips-nlu.svg?branch=develop
:target: https://pypi.python.org/pypi/snips-nlu
.. image:: https://img.shields.io/pypi/pyversions/snips-nlu.svg?branch=develop
:target: https://pypi.python.org/pypi/snips-nlu
.. image:: https://codecov.io/gh/snipsco/snips-nlu/branch/develop/graph/badge.svg
:target: https://codecov.io/gh/snipsco/snips-nlu
`Snips NLU <https://snips-nlu.readthedocs.io>`_ (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information.
Installing
----------
.. code-block:: python
pip install snips-nlu
We currently have pre-built binaries (wheels) for ``snips-nlu`` and its
dependencies for MacOS and Linux x86_64. If you use a different
architecture/os you will need to build these dependencies from sources
which means you will need to install
`setuptools_rust <https://github.com/PyO3/setuptools-rust>`_ and
`Rust <https://www.rust-lang.org/en-US/install.html>`_ before running the
``pip install snips-nlu`` command.
A simple example
----------------
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",
"probability": 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"
}
]
}
Documentation
-------------
To find out how to use Snips NLU please refer to our `documentation <https://snips-nlu.readthedocs.io>`_, it will provide you with a step-by-step guide on how to use and setup our library.
Links
-----
* `Snips NLU <https://github.com/snipsco/snips-nlu>`_
* `Snips NLU Rust <https://github.com/snipsco/snips-nlu-rs>`_: Rust inference pipeline implementation and bindings (C, Swift, Kotlin, Python)
* `Rustling <https://github.com/snipsco/rustling-ontology>`_: Snips NLU builtin entities parser
* `Snips <https://snips.ai/>`_
* `Bug tracker <https://github.com/snipsco/snips-nlu/issues>`_
Contributing
------------
Please see the `Contribution Guidelines <CONTRIBUTING.rst>`_.
Copyright
---------
This library is provided by `Snips <https://www.snips.ai>`_ as Open Source software. See `LICENSE <LICENSE>`_ for more information.
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