Hi @kashy , To train and use a custom model with spaCy, you would need to save and load your model. However, you're correct that spaCy does not directly accept a path from DBFS.
To work around this, you can save your trained model to DBFS and then load it from there.
Here's a general way to do it:
1. Save your trained model to DBFS:
python
nlp.to_disk('/dbfs/path/to/model')
2. Load your model from DBFS:
python
nlp = spacy.load('/dbfs/path/to/model')
The provided document discusses how to use custom libraries and private packages with Model Serving to create model deployments with enterprise-grade security.
It also explains how to upload dependency files to DBFS and log the model with a custom library.
However, it does not provide information on how to save and load a spaCy model specifically.
Please note that the path you provide should be a valid path in your DBFS where you have the appropriate read and write permissions.