How do i resume training for text classification?

Describe the bug
I found that some names agruments in framework aren't consistent.
So for example:

class SupervisedRunner(Runner):
    """Runner for experiments with supervised model."""

    _experiment_fn: Callable = SupervisedExperiment

    def __init__(
        self,
        model: Model = None,
        device: Device = None,
        input_key: Any = "features", 
      

Clients created with worker_client or get_client don't respect the timeout settings (e.g. distributed.comm.timeouts.connect. The timeout is available to set programmatically, but defaults to 3 rather than falling back to the config file. I think this should be as simple as replacing timeout=3 with timeout=None throughout that code path.

If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.

We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing