transformers/hubconf.py/modelForQuestionAnswering
def modelForQuestionAnswering(*args, **kwargs):
    r"""
        # Using torch.hub !
        import torch

        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
        assert model.config.output_attentions == True
        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
        config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
        model = torch.hub.load('huggingface/transformers', 'modelForQuestionAnswering', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)

    """
    return AutoModelForQuestionAnswering.from_pretrained(*args, **kwargs)