snips-nlu 0.16.5

System requirements

• 64-bit Linux, MacOS >= 10.11, 64-bit Windows

• Python 2.7 or Python >= 3.4

• RAM: Snips NLU will typically use between 100MB and 200MB of RAM, depending on the language and the size of the dataset.

Installation

pip install snips-nlu

We currently have pre-built binaries (wheels) for snips-nlu and its dependencies for MacOS (10.11 and later), Linux x86_64 and Windows.

For any other architecture/os snips-nlu can be installed from the source distribution. To do so, Rust and setuptools_rust must be installed before running the pip install snips-nlu command.

Language resources

Snips NLU relies on language resources that must be downloaded before the library can be used. You can fetch resources for a specific language by running the following command:

python -m snips_nlu download en

Or simply:

snips-nlu download en

Once the resources have been fetched, they can be loaded in Python using:

from snips_nlu import load_resources

load_resources("en")

The list of supported languages is available here.

A simple example

Let’s take an example to illustrate the main purpose of this lib, and consider the following sentence:

"What will be the weather in paris at 9pm?"

Properly trained, the Snips NLU engine will be able to extract structured data such as:

{
   "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, fetched the english resources and downloaded this sample dataset:

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, 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 ?

• Snips NLU Open sourcing blog post

• Snips NLU Language Resources

• Bug tracker

• Snips NLU Rust: Rust inference pipeline implementation and bindings (C, Swift, Kotlin, Python)

• Rustling: Snips NLU builtin entities parser

How do I contribute ?

Please see the Contribution Guidelines.

Licence

This library is provided by Snips as Open Source software. See LICENSE for more information.