Unable to Load Model from disk and feed it to pipeline module

I am trying to load the model “roberta-large-mnli” from the disk to the pipeline module. The only way I can manage to do so is by doing the following:

model = AutoModelForSequenceClassification.from_pretrained('/path-to-roberta-large-mnli')

tokenizer = AutoTokenizer.from_pretrained('/path-to-roberta-large-mnli')

pipe = pipeline(task='zero-shot-classification', tokenizer=tokenizer, model=model)
candidate_labels = ['NEUTRAL', 'ENTAILMENT', 'CONTRADICTION']

output = pipe(data_to_test, candidate_labels)

However, doing so returns significantly different and poor prediction scores compared to if I load the model directly from the web and feed it directly to the pipeline module,

pipe = pipeline(model="roberta-large-mnli", device=device) 

output = pipe(data_to_test)

I think the zero-shot setting is not appropriate for my case, because I am using a model trained for NLI and I am testing it also for entailment-type data.

The best case for me is if I could simply specify the model path in the model parameter of the pipeline module. However, if I do that, I get the following error:

“RuntimeError: Instantiating a pipeline without a task set raised an error: Repo id must be in the form ‘repo_name’ or ‘namespace/repo_name’: ‘path-to-roberta-large-mnli’'. Use repo_type argument if needed.”

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Hello, did you find a solution to this problem?