Function model

hubconf.py:57–72

r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache. model = torch.hub.load('huggingface/transformers', 'model', './test/bert_mo

(*args, **kwargs)

@add_start_docstrings(AutoModel.__doc__)
def model(*args, **kwargs):
    r"""
            # Using torch.hub !
            import torch

            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased')    # Download model and configuration from S3 and cache.
            model = torch.hub.load('huggingface/transformers', 'model', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
            model = torch.hub.load('huggingface/transformers', 'model', 'bert-base-uncased', output_attention=True)  # Update configuration during loading
            assert model.config.output_attention == True
            # Loading from a TF checkpoint file instead of a PyTorch model (slower)
            config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
            model = torch.hub.load('huggingface/transformers', 'model', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)

        """

    return AutoModel.from_pretrained(*args, **kwargs)


@add_start_docstrings(AutoModelWithLMHead.__doc__)