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.