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!wandb
library and log inWandbLogger
to your spaCy config file[training.logger]
you need to provide the key @loggers
with the value "spacy.WandbLogger.v3"
, plus a project_name
. You can also turn on dataset and model versioning by just adding a line to the config file.project_name
str
. The name of the Weights & Biases 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, model versioning with Artifacts will be enabled. 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 entityrun_name
Optional str
. If specified, the run will be created with the specified name.WandbLogger
to your spaCy training config you can run spacy train
as usual.