Run Time Error and Transfer Learning?

[ks0m1c]

I got the following error while compiling
python train.py -src_data data/europarl-v7_de.txt -trg_data data/europarl-v7_en.txt -src_lang de -trg_lang en -SGDR -epochs 10 -checkpoint 10 -batchsize 128 -load_weights weights
loading spacy tokenizers...
loading presaved fields...
creating dataset and iterator...
The device argument should be set by using torch.device or passing a string as an argument. This behavior will be deprecated soon and currently defaults to cpu.
Traceback (most recent call last):
File "train.py", line 185, in
main()
File "train.py", line 97, in main
opt.train = create_dataset(opt, SRC, TRG)
File "Documents\transformers\Process.py", line 89, in create_dataset
opt.train_len = get_len(train_iter)
File "Documents\transformers\Process.py", line 95, in get_len
for i, b in enumerate(train):
File "envs\alexandria\lib\site-packages\torchtext\data\iterator.py", line 157, in iter
yield Batch(minibatch, self.dataset, self.device)
File "Anaconda3\envs\alexandria\lib\site-packages\torchtext\data\batch.py", line 34, in init
setattr(self, name, field.process(batch, device=device))
File "Anaconda3\envs\alexandria\lib\site-packages\torchtext\data\field.py", line 201, in process
tensor = self.numericalize(padded, device=device)
File "Anaconda3\envs\alexandria\lib\site-packages\torchtext\data\field.py", line 323, in numericalize
var = torch.tensor(arr, dtype=self.dtype, device=device)
RuntimeError: sizes must be non-negative
I am not sure why this is occurring but I had changed my source and training parallel corpus to a larger europarl dataset is such transfer learning supported? If not how would i go about doing that.
EDIT 1: I have subsequently trained it a model from scratch with a batchsize of 128 ( I am running on a GTX960M) and encounter the same problem.