Weasel lets you manage and share end-to-end workflows for different use cases and domains, and 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. Weasel can be used via the weasel command and we provide templates in our projects repo.

The easiest way to get started is to clone a project template and run it – for example, this end-to-end template that lets you train a spaCy part-of-speech tagger and dependency parser on a Universal Dependencies treebank.

python -m weasel clone pipelines/tagger_parser_ud

Note

Our projects repo includes various project templates for different NLP tasks, models, workflows and integrations that you can clone and run. The easiest way to get started is to pick a template, clone it and start modifying it!

Get started with the documentation:

• Learn how to create a Weasel workflow
• Working with directory and assets
• Running custom scripts
• Using remote storage
• Weasel integrations
• Command line interface description

Weasel is a standalone replacement for spaCy Projects. There are a few backward incompatibilities that you should be aware of:

• The SPACY_CONFIG_OVERRIDES environment variable is no longer checked. You can set configuration overrides using WEASEL_CONFIG_OVERRIDES.
• Support for the spacy_version configuration key has been dropped.
• Support for the check_requirements configuration key has been dropped.
• Support for SPACY_PROJECT_USE_GIT_VERSION environment variable has been dropped.
• Error codes are now Weasel-specific, and do not follow spaCy error codes.

Weasel checks for the first three incompatibilities and will issue a warning if you're using it with spaCy-specific configuration options.