Snips NLU (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.
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-nluWe 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 enOr simply:
snips-nlu download enOnce 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
Snips NLU Open sourcing blog post
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.
2 maintainers
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