spaCy

  3 minute read  

spaCy is a popular “industrial-strength” NLP library: fast, accurate models with a minimum of fuss. As of spaCy v3, Weights and Biases can now be used with spacy train to track your spaCy model’s training metrics as well as to save and version your models and datasets. And all it takes is a few added lines in your configuration.

Sign up and create an API key

An API key authenticates your machine to W&B. You can generate an API key from your user profile.

  1. Click your user profile icon in the upper right corner.
  2. Select User Settings, then scroll to the API Keys section.
  3. Click Reveal. Copy the displayed API key. To hide the API key, reload the page.

Install the wandb library and log in

To install the wandb library locally and log in:

  1. Set the WANDB_API_KEY environment variable to your API key.

    export WANDB_API_KEY=<your_api_key>

  2. Install the wandb library and log in.

    pip install wandb

wandb login

pip install wandb

import wandb
wandb.login()

!pip install wandb

import wandb
wandb.login()

Add the WandbLogger to your spaCy config file

spaCy config files are used to specify all aspects of training, not just logging – GPU allocation, optimizer choice, dataset paths, and more. Minimally, under [training.logger] you need to provide the key @loggers with the value "spacy.WandbLogger.v3", plus a project_name.

[training.logger]
@loggers = "spacy.WandbLogger.v3"
project_name = "my_spacy_project"
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
log_dataset_dir = "./corpus"
model_log_interval = 1000
Name Description
project_name str. The name of the W&B Project. The project will be created automatically if it doesn’t exist yet.
remove_config_values List[str] . A list of values to exclude from the config before it is uploaded to W&B. [] by default.
model_log_interval Optional int. None by default. If set, enables model versioning with Artifacts. Pass in the number of steps to wait between logging model checkpoints. None by default.
log_dataset_dir Optional str. If passed a path, the dataset will be uploaded as an Artifact at the beginning of training. None by default.
entity Optional str . If passed, the run will be created in the specified entity
run_name Optional str . If specified, the run will be created with the specified name.

Start training

Once you have added the WandbLogger to your spaCy training config you can run spacy train as usual.

python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev

python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev

!python -m spacy train \
    config.cfg \
    --output ./output \
    --paths.train ./train \
    --paths.dev ./dev

When training begins, a link to your training run’s W&B page will be output which will take you to this run’s experiment tracking dashboard in the Weights & Biases web UI.


Last modified July 3, 2025