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_namestr. The name of the Weights & Biases project. The project will be created automatically if it doesn’t exist yet.remove_config_valuesList[str] . A list of values to exclude from the config before it is uploaded to W&B. [] by default.model_log_intervalOptional 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_dirOptional str. If passed a path, the dataset will be uploaded as an Artifact at the beginning of training. None by default.entityOptional str . If passed, the run will be created in the specified entityrun_nameOptional 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.