huggingface_hub

🪐 spaCy Project: Hugging Face Hub integration

With Hugging Face Hub, you can easily share any trained pipeline with the community. The Hugging Face Hub offers:

• Free model hosting.
• Built-in file versioning, even with very large files, thanks to a git-based approach.
• In-browser widgets to play with the uploaded models.

This uses spacy-huggingface-hub to push a packaged pipeline to the Hugging Face Hub, including the whl file. This enables using pip installing a pipeline directly from the Hugging Face Hub.

📋 project.yml

The project.yml defines the data assets required by the project, as well as the available commands and workflows. For details, see the spaCy projects documentation.

⏯ Commands

The following commands are defined by the project. They can be executed using spacy project run [name]. Commands are only re-run if their inputs have changed.

Command	Description
install	Install dependencies, log in to Hugging Face and download a model
preprocess	Convert the data to spaCy's binary format
train	Train a named entity recognition model
evaluate	Evaluate the model and export metrics
package	Package the trained model so it can be installed
push_to_hub	Push the model to the Hub

⏭ Workflows

The following workflows are defined by the project. They can be executed using spacy project run [name] and will run the specified commands in order. Commands are only re-run if their inputs have changed.

Workflow	Steps
all	preprocess → train → evaluate → package → push_to_hub

🗂 Assets

The following assets are defined by the project. They can be fetched by running spacy project assets in the project directory.

File	Source	Description
assets/fashion_brands_training.jsonl	Local	JSONL-formatted training data exported from Prodigy, annotated with FASHION_BRAND entities (1235 examples)
assets/fashion_brands_eval.jsonl	Local	JSONL-formatted development data exported from Prodigy, annotated with FASHION_BRAND entities (500 examples)