Metadata-Version: 2.1
Name: snips-nlu
Version: 0.13.5
Summary: Snips Natural Language Understanding library
Home-page: UNKNOWN
Author: Clement Doumouro, Adrien Ball
Author-email: clement.doumouro@snips.ai, adrien.ball@snips.ai
License: Apache 2.0
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.
        
        Check out our `blog post`_ to get more details about why we built Snips NLU and how it works under the hood.
        
        Installation
        ------------
        
        .. 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"
                  }
               ]
            }
        
        
        Sample code
        -----------
        
        Here is a sample code that you can run on your machine after having
        installed `snips-nlu` and downloaded this `sample dataset`_:
        
        .. code-block:: python
        
            from __future__ import unicode_literals, print_function
        
            import io
            import json
        
            from snips_nlu import SnipsNLUEngine, load_resources
            from snips_nlu.default_configs import CONFIG_EN
        
            with io.open("sample_dataset.json") as f:
                sample_dataset = json.load(f)
        
            load_resources("en")
            nlu_engine = SnipsNLUEngine(config=CONFIG_EN)
            nlu_engine.fit(sample_dataset)
        
            text = "What will be the weather in San Francisco next week?"
            parsing = nlu_engine.parse(text)
            print(json.dumps(parsing, indent=2))
        
        What it does is training an NLU engine on a sample weather dataset and parsing
        a weather query.
        
        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.
        
        FAQ
        ---
        Please join our `Discord channel`_ to ask your questions and get feedback from the community.
        
        Links
        -----
        * `What is Snips about ? <https://snips.ai/>`_
        * Snips NLU Open sourcing `blog post`_
        * `Bug tracker <https://github.com/snipsco/snips-nlu/issues>`_
        * `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
        
        
        How do I contribute ?
        ---------------------
        
        Please see the `Contribution Guidelines <CONTRIBUTING.rst>`_.
        
        Licence
        -------
        
        This library is provided by `Snips <https://www.snips.ai>`_ as Open Source software. See `LICENSE <LICENSE>`_ for more information.
        
        .. _sample dataset: samples/sample_dataset.json
        .. _Discord channel: https://discordapp.com/invite/3939Kqx
        .. _blog post: https://medium.com/snips-ai/an-introduction-to-snips-nlu-the-open-source-library-behind-snips-embedded-voice-platform-b12b1a60a41a
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Provides-Extra: test
Provides-Extra: doc
Provides-Extra: metrics
Provides-Extra: integration_test
