snips-nlu | Snips Python library to extract meaning from text | Natural Language Processing library

by snipsco Python Version: 0.20.2 License: Apache-2.0

X-Ray Key Features Code Snippets(4)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | snips-nlu Summary

snips-nlu is a Python library typically used in Artificial Intelligence, Natural Language Processing, Tensorflow, Bert applications. snips-nlu has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install snips-nlu' or download it from GitHub, PyPI.

Snips Python library to extract meaning from text

Support

Quality

Security

License

Reuse

Support

snips-nlu has a medium active ecosystem.

It has 3777 star(s) with 524 fork(s). There are 137 watchers for this library.

It had no major release in the last 12 months.

There are 62 open issues and 197 have been closed. On average issues are closed in 26 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of snips-nlu is 0.20.2

Quality

snips-nlu has 0 bugs and 0 code smells.

Security

snips-nlu has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

snips-nlu code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

snips-nlu is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

snips-nlu releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

snips-nlu saves you 9798 person hours of effort in developing the same functionality from scratch.

It has 19972 lines of code, 1065 functions and 124 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed snips-nlu and discovered the below as its top functions. This is intended to give you an instant insight into snips-nlu implemented functionality, and help decide if they suit your requirements.

Create a new Intent instance from a dict
Parse a SMT string
Capture text in the state
Add text chunk
Create dataset from yaml files
Load dataset parts
Convert a string into a unicode string
Build an entity tree
Validates a dataset
Validate and format a dataset
Returns True if the given label is a builtin entity
Parses a text into a list of entities
Log result to logger
Return a ProcessingUnitConfig object from a dictionary
Print training information
Ensure that all entities are present in the intent
Create a ProcessingUnit from a byte array
Parses the given text
The data augmentation config
Sets the data augmentation config
Displays inference results
Create a factory from a configuration dictionary
Converts a builtin slot definition
Return an argument parser
Load a unit from a given path
Load resources from the given package
Build a builtin entity parser

Get all kandi verified functions for this library.

snips-nlu Key Features

No Key Features are available at this moment for snips-nlu.

snips-nlu Examples and Code Snippets

lily,Building,Dependencies

Rust

Lines of Code : 6

License : Permissive (Apache-2.0)

Copy

sudo apt install libssl-dev libasound2-dev libpocketsphinx-dev libsphinxbase-dev clang libgsl-dev

sudo dnf install openssl-devel alsa-lib-devel pocketsphinx-devel clang-libs llvm-devel clang gsl-devel libtool

sudo apt install libespeak-ng-dev

sudo

lily,Building,Debian package

Rust

Lines of Code : 2

License : Permissive (Apache-2.0)

Copy

cargo install cargo-deb
cargo deb

Snips-nlu intent is returning empty intent json after following quick start

Python

Lines of Code : 48

License : Strong Copyleft (CC BY-SA 4.0)

Copy

python -m snips_nlu download en 
python -m snips_nlu download-entity snips/country en

> python -m snips_nlu train dataset.json nlu_engine
Create and train the engine...
Persisting the engine...
Saved the trained

Can I add more data to snips-nlu on the fly?

Python

Lines of Code : 26

License : Strong Copyleft (CC BY-SA 4.0)

Copy

type: intent
name: questionAboutFood
slots:
  - name: foodType
    entity: foodType
utterances:
  - what color is a [foodType]
  - what is the color of an [foodType] in general
  - how much does an [foodType] cost
  - can you describe a [f

Community Discussions

Trending Discussions on snips-nlu

Compling Rust on Mac M1 for target x86_64 linux

How do I convert arrays to a snips-nlu-rs whitelist or blacklist?

QUESTION

Compling Rust on Mac M1 for target x86_64 linux

Asked 2022-Jan-18 at 17:25

I'm trying to compile my Rust code on my M1 Mac for a x86_64 target with linux. I use Docker to achieve that.

My Dockerfile:

...

ANSWER

Answered 2022-Jan-18 at 17:25

It looks like the executable is actually named x86_64-linux-gnu-gcc, see https://packages.debian.org/bullseye/arm64/gcc-x86-64-linux-gnu/filelist.

Source https://stackoverflow.com/questions/70755856

QUESTION

How do I convert arrays to a snips-nlu-rs whitelist or blacklist?

Asked 2020-Jan-23 at 16:03

I use snips and built a C library. I want to connect the library to my Node environment with Rust.

JavaScript

...

ANSWER

Answered 2020-Jan-23 at 16:03

After much struggle with it, I found a solution. I know, this won't be the best that has ever been existing, but it's a solution :)

Source https://stackoverflow.com/questions/59751727

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install snips-nlu

You can install using 'pip install snips-nlu' or download it from GitHub, PyPI.
You can use snips-nlu like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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