Zero Shot Multi-label text classification

Greetings,

I have developed a script on my computer to do some zero shot multi-label text classification using `xlm-roberta`.
I want to reporduce my work on sagemaker using huggingface inference toolkit and I having some trouble doing so.

On local when i do the classification i do the following:

```
classifier = pipeline(model="joeddav/xlm-roberta-large-xnli", task="zero-shot-classification")

predictions = classifier(sequence_to_classify, candidate_labels, multi_label=True)
```

On sagemaker, I configure the model from the hub and launch a batch transform job for inference but i can't seem to find the `multi_label` parameter in the following:

```
huggingface_model = HuggingFaceModel(
transformers_version="4.17.0",
pytorch_version="1.10.2",
py_version="py38",
env=hub,
role=event['role'])

bt_output_key = f"s3://{event['bucket']}/{event['output_prefix']}/{event['execution_id']}"

hf_transformer = huggingface_model.transformer(
instance_count=event["instance_count"],
instance_type=event["instance_type"],
output_path=bt_output_key,
strategy="SingleRecord",
max_concurrent_transforms=event["concurrent_transforms"],
)

hf_transformer.transform(
data=event['input_s3_path'],
content_type="application/json",
split_type="Line",
wait=False
)

```
I looked in the environment variables list but I think Im missing some thing.
Thank you for your help.

Start by comparing the local zero-shot `pipeline` call with the SageMaker `HuggingFaceModel.transformer` batch-transform request shown in the issue. Check the inference toolkit's documented request and configuration handling for `multi_label`; done means documenting or enabling a supported way to reproduce the local multi-label results on SageMaker.

Written by the indexing model from the issue text.