Projects

Abstract

1. Introduction

Weasel, previously spaCy projects, lets you manage and share

Projects can be used via the weasel CLI, or through the spacy project alias. To find out more about a command, add --help. For detailed instructions, see the Weasel documentation or spaCy projects usage guide.

orchestrate training, packaging and serving your custom pipelines. You can start off by cloning a pre-defined project template, adjust it to fit your needs, load in your data, train a pipeline, export it as a Python package, upload your outputs to a remote storage and share your results with your team.

2. Objective

🪐 End-to-end NLP workflows from prototype to production

This project demonstrates how Python, NLP 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/explosion/projects.git
cd projects

1. Clone the project template you want to use.
2. Install any project requirements.
3. Fetch assets (data, weights) defined in the project.yml.
4. Run a command defined in the project.yml.
5. Run a workflow of multiple steps in order.
6. Adjust the template for your specific use case, load in your own

python -m weasel clone tutorials/ner_fashion_brands

cd ner_fashion_brands
   python -m pip install -r requirements.txt

python -m weasel assets

python -m weasel run preprocess

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 explosion and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.