Snips Nlu

Abstract

1. Introduction

Snips NLU _ (Natural Language Understanding) is a Python library that allows to extract structured information from sentences written in natural language.

In this case, the identified intent is searchWeatherForecast and two slots were extracted, a locality and a datetime. As you can see, Snips NLU does an extra step on top of extracting entities: it resolves them. The extracted datetime value has indeed been converted into a handy ISO format.

Check out our blog post_ to get more details about why we built Snips NLU and how it works under the hood. We also published a paper on arxiv_, presenting the machine learning architecture of the Snips Voice Platform.

2. Objective

Snips Python library to extract meaning from text

This project demonstrates how Python, NLP, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/snipsco/snips-nlu.git
cd snips-nlu

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by snipsco and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.