How to enable 'multi_class' predictor within Sagemaker?

[millnerryan]

I'm using the zeroshot pipeline with the valhalla/distilbart-mnli-12-9 model. How do I enable multi_class classification? When using the transformer w/ pytorch in python, I pass the argument multi_class=True, but I can't find the appropriate way to do this in Sagemaker. See code below:

from sagemaker.huggingface.model import HuggingFaceModel

# Hub Model configuration. <https://huggingface.co/models>
model = 'valhalla/distilbart-mnli-12-9'

hub = {
  'HF_MODEL_ID': model, # model_id from hf.co/models
  'HF_TASK':'zero-shot-classification' # NLP task you want to use for predictions,
}

# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
   env=hub, # configuration for loading model from Hub
   role=role, # iam role with permissions to create an Endpoint
   transformers_version="4.6", # transformers version used
   pytorch_version="1.7", # pytorch version used
    py_version="py36"
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
   initial_instance_count=1,
   instance_type="ml.p2.xlarge",
    multi_label= True
)

[millnerryan]

Figured it out, here's how!

data = {
    "inputs": s,
    "parameters": {
        "candidate_labels": [
            "hostess",
            "waiter"
        ],
        "multi_label": True
    
    }
}