Snips NLU is a Natural Language Understanding Python library that allows to parse sentences written in natural language, and extract structured information.
It’s the library that powers the NLU engine used in the Snips Console that you can use to create awesome and private-by-design voice assistants.
Snips NLU relies on external language resources that must be downloaded before the library can be used.
Snips NLU requires Python 2.7 or Python >= 3.5.
Key Features
- ‘Private by Design’ alternative to traditional voice assistants. This means user data is not touched, processed or collected, unlike most voice assistants.
- Leverages machine learning algorithms and some training data in order to produce a powerful intent recognition engine.
- Provides a default NLU pipeline containing built-in processing units such as the LookupIntentParser or the ProbabilisticIntentParser.
- Define custom processing units and use them in a SnipsNLUEngine.
- Supports 2 dataset formats: YAML and JSON.
- Command Line Interface.
- Supports English, French, German, Italian, Japanese, Korean, Portuguese, Portuguese (Brazilian), and Spanish.
Website: snips-nlu.readthedocs.io
Support: Documentation, GitHub Code Repository
Developer: Snips
License: Apache License 2.0
Snips have also open sourced Snips NLU-rs, a Rust implementation focused on the prediction aspect.
Snips NLU is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Python Natural Language Processing Tools | |
|---|---|
| PyTorch-Transformers | Library of state-of-the-art pre-trained models for NLP |
| NLTK | Natural Language Toolkit |
| spaCy | Industrial strength natural language processing |
| scikit-learn | Machine learning library |
| Haystack | AI orchestration framework |
| Gensim | Vector space modeling and topic modeling toolkit |
| flair | Simple framework for state-of-the-art NLP |
| TextBlob | Python (2 and 3) library for processing textual data |
| doccano | Data labelling and text annotation platform |
| BERTopic | Topic modeling |
| textacy | Python library for performing NLP tasks |
| Presidio | Data protection and de-identification SDK |
| polyglot | Multilingual text (NLP) processing toolkit |
| AllenNLP | Apache 2.0 NLP research library |
| Snips NLU | Natural Language Understanding Python library |
| PyNLPI | Various modules useful for common, and less common, NLP tasks |
| nlpnet | Natural Language Processing with neural networks |
| Pattern | Web mining module |
| GluonNLP | Deep Learning for NLP |
| PyTorch-NLP | Neural network layers, text processing modules and datasets |
| NLP Architect | Deep Learning NLP/NLU library |
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