1.    ---------------------------------------------------------------------------
2. ValueError                                Traceback (most recent call last)
3. <ipython-input-18-7678758b2c9c> in <module>()
4.      56   train(bert_model,bert_tokenizer,train_data_set_path)
5.      57   #prepare_data_set(bert_tokenizer)
6. ---> 58 main()
7.  
8. 9 frames
9. <ipython-input-18-7678758b2c9c> in main()
10.      54   bert_model = BertForNextSentencePrediction.from_pretrained("bert-base-cased")
11.      55   train_data_set_path = "/content/drive/My Drive/next_sentence/line_data_set_file.txt"
12. ---> 56   train(bert_model,bert_tokenizer,train_data_set_path)
13.      57   #prepare_data_set(bert_tokenizer)
14.      58 main()
15.  
16. <ipython-input-18-7678758b2c9c> in train(bert_model, bert_tokenizer, path, eval_path)
17.      47
18.      48   )
19. ---> 49   trainer.train()
20.      50   trainer.save_model(out_dir)
21.      51 def main():
22.  
23. /usr/local/lib/python3.6/dist-packages/transformers/trainer.py in train(self, model_path, trial)
24.     697
25.     698             epoch_pbar = tqdm(epoch_iterator, desc="Iteration", disable=disable_tqdm)
26. --> 699             for step, inputs in enumerate(epoch_iterator):
27.     700
28.     701                 # Skip past any already trained steps if resuming training
29.  
30. /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
31.     361
32.     362     def __next__(self):
33. --> 363         data = self._next_data()
34.     364         self._num_yielded += 1
35.     365         if self._dataset_kind == _DatasetKind.Iterable and \
36.  
37. /usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
38.     401     def _next_data(self):
39.     402         index = self._next_index()  # may raise StopIteration
40. --> 403         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
41.     404         if self._pin_memory:
42.     405             data = _utils.pin_memory.pin_memory(data)
43.  
44. /usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
45.      45         else:
46.      46             data = self.dataset[possibly_batched_index]
47. ---> 47         return self.collate_fn(data)
48.  
49. /usr/local/lib/python3.6/dist-packages/transformers/data/data_collator.py in __call__(self, examples)
50.     356         for i, doc in enumerate(examples):
51.     357             input_id, segment_id, attention_mask, label = self.create_examples_from_document(doc, i, examples)
52. --> 358             input_ids.extend(input_id)
53.     359             segment_ids.extend(segment_id)
54.     360             attention_masks.extend(attention_mask)
55.  
56. /usr/local/lib/python3.6/dist-packages/transformers/data/data_collator.py in create_examples_from_document(self, document, doc_index, examples)
57.     444                         random_document = examples[random_document_index]
58.     445                         random_start = random.randint(0, len(random_document) - 1)
59. --> 446                         for j in range(random_start, len(random_document)):
60.     447                             tokens_b.extend(random_document[j])
61.     448                             if len(tokens_b) >= target_b_length:
62.  
63. /usr/lib/python3.6/random.py in randint(self, a, b)
64.     219         """
65.     220
66. --> 221         return self.randrange(a, b+1)
67.     222
68.     223     def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type,
69.  
70. /usr/lib/python3.6/random.py in randrange(self, start, stop, step, _int)
71.     197             return istart + self._randbelow(width)
72.     198         if step == 1:
73. --> 199             raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width))
74.     200
75.     201         # Non-unit step argument supplied.
76.  
77. ValueError: empty range for randrange() (0,0, 0)