Weights & Biases makes running collaborative machine learning projects a breeze. You can focus on what you're trying to experiment with, and W&B will take on the burden of keeping track of everything. If you want to review a loss plot, download the latest model for production, or just see which configurations produced a certain model, W&B is your friend. There's also a bunch of features to help you and your team collaborate, like having a shared dashboard and sharing interactive reports.

spaCy can serve a lot of your Natural Language Processing (NLP) needs out-of-the-box. This includes Named Entity Recognition (NER), Part of Speech tagging, text classification and more. Even better, these components are all customizable, extendable and composable.

Hyperparameter Search with W&B + spaCy

This notebook accompanies and implements a blog post on automating hyperparameter search for spaCy models using W&B sweeps.

Run the cells below to start your own hyperparameter search.

The code is organized as a spaCy project, available here.

# Setup

✍️ Login to W&B

# Clone the project with spacy project clone

# Install the project-specific dependencies

# Download the project-specific assets

# Run the hyperparameter search

Running the cell below will produce a Sweeps page 🧹 link which you can follow to see the all the metrics of your runs.