@ptrblck That dummy code looks like a great start. When I run it, I get this an error. Should I not be converting my new data set to tensors like I had to for the model training? If so, would I just pass the values in ‘raw’ so to speak?
['POL_STATUS', 'P1_POLICY_REFUSED', 'FLOODING', 'Zipcode', 'Combined FIPS', 'State', 'Name']

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-62-c9aa9c799384> in <module>()
      1 preds = []
      2 with torch.no_grad():
----> 3   y_val = model(inputCatColumns, inputNumColumns)
      4   preds.append(y_val > 0.) # if y_val are logits
      5   loss = loss_function(y_val, test_outputs)

1 frames

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
    530             result = self._slow_forward(*input, **kwargs)
    531         else:
--> 532             result = self.forward(*input, **kwargs)
    533         for hook in self._forward_hooks.values():
    534             hook_result = hook(self, input, result)

<ipython-input-35-5bbeaf977447> in forward(self, x_categorical, x_numerical)
     28         print(x_categorical)
     29         for i,e in enumerate(self.all_embeddings):
---> 30           print(x_categorical[:,i])
     31           embeddings.append(e(x_categorical[:,i]))
     32 

TypeError: list indices must be integers or slices, not tuple